While Artificial Intelligence (AI) is leading the way in terms of hardware advancements, such as GPUs, memory, and processing power, real-time applications are still catching up. It is inevitable that when one aspect ...While Artificial Intelligence (AI) is leading the way in terms of hardware advancements, such as GPUs, memory, and processing power, real-time applications are still catching up. It is inevitable that when one aspect leads and other trails behind, they coexist in life, as is often the case. The trailing aspect cannot remain far behind because, without application and use, there would be a dead end. Everything, whether an object, software, or tool, must have a practical use for humans. Without this, it will become obsolete. We can see this in many instances, such as blockchain technology, which is superior yet faces challenges in practical implementation, leading to a decline in adoption. This publication aims to bridge the gap between AI advancements and maintenance, specifically focusing on making predictive maintenance a practical application. There are multiple building blocks that make predictive maintenance a practical application. Each block performs a function leading to an output. This output forms an input to the receiving block. There are also foundational parts for all these building blocks to perform a function. Eventually, once the building blocks are connected, they form a loop and start to lead the path to predictive maintenance. Predictive maintenance is indeed practically achievable, but one must comprehend all the building blocks necessary for its implementation. Although detailed explanations will be provided in the upcoming sections, it is important to understand that simply purchasing software and plugging it in might be a far-fetched approach.展开更多
Predictive maintenance plays a crucial role in preventing equipment failures and minimizing operational downtime in modern industries.However,traditional predictive maintenance methods often face challenges in adaptin...Predictive maintenance plays a crucial role in preventing equipment failures and minimizing operational downtime in modern industries.However,traditional predictive maintenance methods often face challenges in adapting to diverse industrial environments and ensuring the transparency and fairness of their predictions.This paper presents a novel predictive maintenance framework that integrates deep learning and optimization techniques while addressing key ethical considerations,such as transparency,fairness,and explainability,in artificial intelligence driven decision-making.The framework employs an Autoencoder for feature reduction,a Convolutional Neural Network for pattern recognition,and a Long Short-Term Memory network for temporal analysis.To enhance transparency,the decision-making process of the framework is made interpretable,allowing stakeholders to understand and trust the model’s predictions.Additionally,Particle Swarm Optimization is used to refine hyperparameters for optimal performance and mitigate potential biases in the model.Experiments are conducted on multiple datasets from different industrial scenarios,with performance validated using accuracy,precision,recall,F1-score,and training time metrics.The results demonstrate an impressive accuracy of up to 99.92%and 99.45%across different datasets,highlighting the framework’s effectiveness in enhancing predictive maintenance strategies.Furthermore,the model’s explainability ensures that the decisions can be audited for fairness and accountability,aligning with ethical standards for critical systems.By addressing transparency and reducing potential biases,this framework contributes to the responsible and trustworthy deployment of artificial intelligence in industrial environments,particularly in safety-critical applications.The results underscore its potential for wide application across various industrial contexts,enhancing both performance and ethical decision-making.展开更多
The fractionating tower bottom in fluid catalytic cracking Unit (FCCU) is highly susceptible to coking due to the interplay of complex external operating conditions and internal physical properties. Consequently, quan...The fractionating tower bottom in fluid catalytic cracking Unit (FCCU) is highly susceptible to coking due to the interplay of complex external operating conditions and internal physical properties. Consequently, quantitative risk assessment (QRA) and predictive maintenance (PdM) are essential to effectively manage coking risks influenced by multiple factors. However, the inherent uncertainties of the coking process, combined with the mixed-frequency nature of distributed control systems (DCS) and laboratory information management systems (LIMS) data, present significant challenges for the application of data-driven methods and their practical implementation in industrial environments. This study proposes a hierarchical framework that integrates deep learning and fuzzy logic inference, leveraging data and domain knowledge to monitor the coking condition and inform prescriptive maintenance planning. The framework proposes the multi-layer fuzzy inference system to construct the coking risk index, utilizes multi-label methods to select the optimal feature dataset across the reactor-regenerator and fractionation system using coking risk factors as label space, and designs the parallel encoder-integrated decoder architecture to address mixed-frequency data disparities and enhance adaptation capabilities through extracting the operation state and physical properties information. Additionally, triple attention mechanisms, whether in parallel or temporal modules, adaptively aggregate input information and enhance intrinsic interpretability to support the disposal decision-making. Applied in the 2.8 million tons FCCU under long-period complex operating conditions, enabling precise coking risk management at the fractionating tower bottom.展开更多
Offshore platforms are large,complex structures designed for long-term service,and they are characterized by high risk and significant investment.Ensuring the safety and reliability of in-service offshore platforms re...Offshore platforms are large,complex structures designed for long-term service,and they are characterized by high risk and significant investment.Ensuring the safety and reliability of in-service offshore platforms requires intelligent operation and maintenance strategies.Digital twin technology can enable the accurate description and prediction of changes in the platform’s physical state through real-time monitoring data.This technology is expected to revolutionize the maintenance of existing offshore platform structures.A digital twin system is proposed for real-time assessment of structural health,prediction of residual life,formulation of maintenance plans,and extension of service life through predictive maintenance.The system integrates physical entities,digital models,intelligent predictive maintenance tools,a visualization platform,and interconnected modules to provide a comprehensive and efficient maintenance framework.This paper examines the current development status of core technologies in physical entity monitoring,digital model construction,and intelligent predictive maintenance.It also outlines future directions for the advancement of these technologies within the digital twin system,offering technical insights and practical references to support further research and applications of digital twin technology in offshore platform structures.展开更多
The reliability-based maintenance optimization model has been focused by the engineers and scholars but it has never been solved effectively to formulate the effect of a maintenance action on the optimization model. I...The reliability-based maintenance optimization model has been focused by the engineers and scholars but it has never been solved effectively to formulate the effect of a maintenance action on the optimization model. In existing works, the system reliability was assumed to be increased to 1 after a predictive maintenance. However, it is very difficult in the most practical systems. Therefore, a new reliability-based maintenance optimization model under imperfect predictive maintenance (PM) is proposed in this paper. In the model, the system reliability is only restored to R i (0<R i <1, i∈N, N is natural number set) after the ith PM. The system uptimes and the corresponding probability in two cases whether there is an unexpected fault in one cycle are derived respectively and the system expected uptime model is given. To formulate the system expected downtime, the probability of each imperfect PM number in one cycle is calculated. Then, the system expected total time model is obtained. The total expected long-term operation cost is composed of the expected maintenance cost, the expected loss due to the downtime and the expected additional cost due to the occurrence of an unexpected failure. They are modeled respectively in this work. Jointing the system expected total time and long-term operation cost in one cycle, the expected long-term operation cost per time could be computed. Then, the proposed maintenance optimization model is formulated where the objective function is to minimize the expected long-term operation cost per time. The results of numerical example show that the proposed model could scheme the optimal maintenance actions for the considered system when the required parameters are given and the optimal solution of the proposed model is sensitive to the parameters of effective age model and insensitive to other parameters. The proposed model effectively solves the problem of evaluating the effect of an imperfect PM on the system reliability and presents a more practical optimization method for the reliability-based maintenance strategy than the existing works.展开更多
The vast potential of system health monitoring and condition based maintenance on modern commercial aircraft is being realized through the innovative use of Airplane Condition Monitoring System(ACMS) data.However ther...The vast potential of system health monitoring and condition based maintenance on modern commercial aircraft is being realized through the innovative use of Airplane Condition Monitoring System(ACMS) data.However there are few methods addressing the issues of failure prognostics and predictive maintenance for commercial aircraft Air Conditioning System(ACS).This study developed a Bayesian failure prognostics approach using ACMS data for predictive maintenance of ACS.First, a health index characterizing the ACS health state is inferred from a multiple sensor signals using a data driven method.Then a dynamic linear model is proposed to describe the degradation process for failure prognostics.Bayesian inference formulas are carried out for degradation estimation and prediction.The developed approach is applied on a passenger aircraft fleet with ACMS data recorded for one year.The analysis of the case study shows that the developed method can produce satisfactory prognostics results, where all the ACS failure precursors are identified in advance, and the relative errors for the failure time prediction made when just entering the degradation warning stage are less than 8%.This would allow operators to proactively plan future maintenance.展开更多
Structural health monitoring and performance prediction are crucial for smart disaster mitigation and intelligent management of structures throughout their lifespan.Recent advancements in predictive maintenance strate...Structural health monitoring and performance prediction are crucial for smart disaster mitigation and intelligent management of structures throughout their lifespan.Recent advancements in predictive maintenance strategies within the industrial manufacturing industry have inspired similar innovations in civil engineering,aiming to improve structural performance evaluation,damage diagnosis,and capacity prediction.This review delves into the framework of predictive maintenance and examines various existing solutions,focusing on critical areas such as data acquisition,condition monitoring,damage prognosis,and maintenance planning.Results from real-world applications of predictive maintenance in civil engineering,covering high-rise structures,deep foundation pits,and other infrastructure,are presented.The challenges of implementing predictive maintenance in civil engineering structures under current technology,such as model interpretability of data-driven methods and standards for predictive maintenance,are explored.Future research prospects within this area are also discussed.展开更多
Remaining useful life(RUL)prediction is an advanced technique for system maintenance scheduling.Most of existing RUL prediction methods are only interested in the precision of RUL estimation;the adverse impact of over...Remaining useful life(RUL)prediction is an advanced technique for system maintenance scheduling.Most of existing RUL prediction methods are only interested in the precision of RUL estimation;the adverse impact of overestimated RUL on maintenance scheduling is not of concern.In this work,an RUL estimation method with risk-averse adaptation is developed which can reduce the over-estimation rate while maintaining a reasonable under-estimation level.The proposed method includes a module of degradation feature selection to obtain crucial features which reflect system degradation trends.Then,the latent structure between the degradation features and the RUL labels is modeled by a support vector regression(SVR)model and a long short-term memory(LSTM)network,respectively.To enhance the prediction robustness and increase its marginal utility,the SVR model and the LSTM model are integrated to generate a hybrid model via three connection parameters.By designing a cost function with penalty mechanism,the three parameters are determined using a modified grey wolf optimization algorithm.In addition,a cost metric is proposed to measure the benefit of such a risk-averse predictive maintenance method.Verification is done using an aero-engine data set from NASA.The results show the feasibility and effectiveness of the proposed RUL estimation method and the predictive maintenance strategy.展开更多
Airframe maintenance is traditionally performed at scheduled maintenance stops.The decision to repair a fuselage panel is based on a fixed crack size threshold,which allows to ensure the aircraft safety until the next...Airframe maintenance is traditionally performed at scheduled maintenance stops.The decision to repair a fuselage panel is based on a fixed crack size threshold,which allows to ensure the aircraft safety until the next scheduled maintenance stop.With progress in sensor technology and data processing techniques,structural health monitoring（SHM） systems are increasingly being considered in the aviation industry.SHM systems track the aircraft health state continuously,leading to the possibility of planning maintenance based on an actual state of aircraft rather than on a fixed schedule.This paper builds upon a model-based prognostics framework that the authors developed in their previous work,which couples the Extended Kalman filter（EKF） with a firstorder perturbation（FOP） method.By using the information given by this prognostics method,a novel cost driven predictive maintenance（CDPM） policy is proposed,which ensures the aircraft safety while minimizing the maintenance cost.The proposed policy is formally derived based on the trade-off between probabilities of occurrence of scheduled and unscheduled maintenance.A numerical case study simulating the maintenance process of an entire fleet of aircrafts is implemented.Under the condition of assuring the same safety level,the CDPM is compared in terms of cost with two other maintenance policies：scheduled maintenance and threshold based SHM maintenance.The comparison results show CDPM could lead to significant cost savings.展开更多
The fundamental process of predictive maintenance is prognostics and health management,and it is the tool resulting in the development of many algorithms to predict the remaining useful life of industrial equipment.A ...The fundamental process of predictive maintenance is prognostics and health management,and it is the tool resulting in the development of many algorithms to predict the remaining useful life of industrial equipment.A new data-driven predictive maintenance and an architectural impulse,based on a regularized deep neural network using predictive analytics,are proposed successfully for ring spinning technology.The paradigm shift in computational infrastructures enormously puts pressure on large-scale linear and non-linear automated assembly systems to eliminate and cut down unscheduled downtime and unexpected stoppages.The sensor network designed for the scheduling process comprises different critical components of the same spinning machine frames containing more than thousands of spindles attached to them.We established a genetic algorithm based on multi-sensor performance assessment and prediction procedure for the spinning system.Results show that it operates with a relatively less amount of training data sets but takes advantage of larger volumes of data.This integrated system aims to prognosticate abnormalities,disturbances,and failures by providing condition-based monitoring for each component,which makes it more accurate to locate the defined component failures in the current spinning spindles by using smart agents during the operations through the neural sensing network.A case study has provided to demonstrate the feasibility of the proposed predictive model for highly dynamic,high-speed textile spinning system through real-time data sensing and signal processing via the industrial Internet of Things.展开更多
Predictive maintenance is a vital aspect of the industrial sector,and the use of Industrial Internet of Things(IIoT)sensor nodes is becoming increasingly popular for detecting motor faults and monitoring motor conditi...Predictive maintenance is a vital aspect of the industrial sector,and the use of Industrial Internet of Things(IIoT)sensor nodes is becoming increasingly popular for detecting motor faults and monitoring motor conditions.An integrated approach for acquiring,processing,and wirelessly transmitting a large amount of data in predictive maintenance applications remains a significant challenge.This study presents an IIoT-based sensor node for industrial motors.The sensor node is designed to acquire vibration data on the radial and axial axes of the motor and utilizes a hybrid approach for efficient data processing via edge and cloud platforms.The initial step of signal processing is performed on the node at the edge,reducing the burden on a centralized cloud for processing data from multiple sensors.The proposed architecture utilizes the lightweight Message Queue Telemetry Transport(MQTT)communication protocol for seamless data transmission from the node to the local and main brokers.The broker’s bridging allows for data backup in case of connection loss.The proposed sensor node is rigorously tested on a motor testbed in a laboratory setup and an industrial setting in a rice industry for validation,ensuring its performance and accuracy in real-world industrial environments.The data analysis and results from both testbed and industrial motors were discussed using vibration analysis for identifying faults.The proposed sensor node is a significant step towards improving the efficiency and reliability of industrial motors through realtime monitoring and early fault detection,ultimately leading to minimized unscheduled downtime and cost savings.展开更多
Predictive Maintenance is a type of condition-based maintenance that assesses the equipment's states and estimates its failure probability and when maintenance should be performed.Although machine learning techniq...Predictive Maintenance is a type of condition-based maintenance that assesses the equipment's states and estimates its failure probability and when maintenance should be performed.Although machine learning techniques have been frequently implemented in this area,the existing studies disregard to the nat-ural order between the target attribute values of the historical sensor data.Thus,these methods cause losing the inherent order of the data that positively affects the prediction performances.To deal with this problem,a novel approach,named Ordinal Multi-dimensional Classification(OMDC),is proposed for estimating the conditions of a hydraulic system's four components by taking into the natural order of class values.To demonstrate the prediction ability of the proposed approach,eleven different multi-dimensional classification algorithms(traditional Binary Relevance(BR),Classifier Chain(CC),Bayesian Classifier Chain(BCC),Monte Carlo Classifier Chain(MCC),Probabilistic Classifier Chain(PCC),Clas-sifier Dependency Network(CDN),Classifier Trellis(CT),Classifier Dependency Trellis(CDT),Label Powerset(LP),Pruned Sets(PS),and Random k-Labelsets(RAKEL))were implemented using the Ordinal Class Classifier(OCC)algorithm.Besides,seven different classification algorithms(Multilayer Perceptron(MLP),Support Vector Machine(SVM),k-Nearest Neighbour(kNN),Decision Tree(C4.5),Bagging,Random Forest(RF),and Adaptive Boosting(AdaBoost))were chosen as base learners for the OCC algorithm.The experimental results present that the proposed OMDC approach using binary relevance multi-dimensional classification methods predicts the conditions of a hydraulic system's multiple components with high accuracy.Also,it is clearly seen from the results that the OMDC models that utilize ensemble-based classification algorithms give more reliable prediction performances with an average Hamming score of 0.853 than the others that use traditional algorithms as base learners.展开更多
Manned spacecraft pose challenges in terms of extremely high safety and reliability, and with the growth of system complexity and longer on-orbit operation time, the traditional management mode, such as monitoring the...Manned spacecraft pose challenges in terms of extremely high safety and reliability, and with the growth of system complexity and longer on-orbit operation time, the traditional management mode, such as monitoring the threshold of parameter passively, is difficult to meet the required safety standards. Predictive maintenance, which analyzes the system heath trend and estimates remaining useful life(RUL) to establish maintenance strategies ahead of time before failure occurs, is a new mode to approach maintenance tasks. Here, a predictive maintenance strategy for complex manned spacecraft is proposed based on the remaining useful life estimation technique. Firstly, a health index is established based on an abundance of telemetry data, reflecting the system's current health state. Secondly, we map the health index to the remaining useful life through system degradation modelling, taking into consideration both the system's stochastic deterioration and uncertainty. The maintenance and management strategies are then made based on the calculated distribution of RUL time. Finally, a case study on Chinese space station energy system predictive maintenance is presented.展开更多
This paper provides a review of predictive analytics for roads,identifying gaps and limitations in current methodologies.It explores the implications of these limitations on accuracy and application,while also discuss...This paper provides a review of predictive analytics for roads,identifying gaps and limitations in current methodologies.It explores the implications of these limitations on accuracy and application,while also discussing how advanced predictive analytics can address these challenges.The article acknowledges the transformative shift brought about by technological advancements and increased computational capabilities.The degradation of pavement surfaces due to increased road users has resulted in safety and comfort issues.Researchers have conducted studies to assess pavement condition and predict future changes in pavement structure.Pavement Management Systems are crucial in developing prediction performance models that estimate pavement condition and degradation severity over time.Machine learning algorithms,artificial neural networks,and regression models have been used,with strengths and weaknesses.Researchers generally agree on their accuracy in estimating pavement condition considering factors like traffic,pavement age,and weather conditions.However,it is important to carefully select an appropriate prediction model to achieve a high-quality prediction performance system.Understanding the strengths and weaknesses of each model enables informed decisions for implementing prediction models that suit specific needs.The advancement of prediction models,coupled with innovative technologies,will contribute to improved pavement management and the overall safety and comfort of road users.展开更多
In the era of Industry 4.0,conditionmonitoring has emerged as an effective solution for process industries to optimize their operational efficiency.Condition monitoring helps minimize unplanned downtime,extending equi...In the era of Industry 4.0,conditionmonitoring has emerged as an effective solution for process industries to optimize their operational efficiency.Condition monitoring helps minimize unplanned downtime,extending equipment lifespan,reducing maintenance costs,and improving production quality and safety.This research focuses on utilizing Bayesian search-based machine learning and deep learning approaches for the condition monitoring of industrial equipment.The study aims to enhance predictive maintenance for industrial equipment by forecasting vibration values based on domain-specific feature engineering.Early prediction of vibration enables proactive interventions to minimize downtime and extend the lifespan of critical assets.A data set of load information and vibration values from a heavy-duty industrial slip ring induction motor(4600 kW)and gearbox equipped with vibration sensors is used as a case study.The study implements and compares six machine learning models with the proposed Bayesian-optimized stacked Long Short-Term Memory(LSTM)model.The hyperparameters used in the implementation of models are selected based on the Bayesian optimization technique.Comparative analysis reveals that the proposed Bayesian optimized stacked LSTM outperforms other models,showcasing its capability to learn temporal features as well as long-term dependencies in time series information.The implemented machine learning models:Linear Regression(LR),RandomForest(RF),Gradient Boosting Regressor(GBR),ExtremeGradient Boosting(XGBoost),Light Gradient Boosting Machine(LightGBM),and Support Vector Regressor(SVR)displayed a mean squared error of 0.9515,0.4654,0.1849,0.0295,0.2127 and 0.0273,respectively.The proposed model predicts the future vibration characteristics with a mean squared error of 0.0019 on the dataset containing motor load information and vibration characteristics.The results demonstrate that the proposed model outperforms other models in terms of other evaluation metrics with a mean absolute error of 0.0263 and 0.882 as a coefficient of determination.Current research not only contributes to the comparative performance of machine learning models in condition monitoring but also showcases the practical implications of employing these techniques.By transitioning fromreactive to proactive maintenance strategies,industries canminimize downtime,reduce costs,and prolong the lifespan of crucial assets.This study demonstrates the practical advantages of transitioning from reactive to proactive maintenance strategies using ML-based condition monitoring.展开更多
Railway infrastructure is a crucial asset for the mobility of people and goods.The increased traffic frequency imposes higher loads and speeds,leading to accelerated infrastructure degradation.Asset managers require t...Railway infrastructure is a crucial asset for the mobility of people and goods.The increased traffic frequency imposes higher loads and speeds,leading to accelerated infrastructure degradation.Asset managers require timely information regarding the current(diagnosis)and future(prognosis)condition of their assets to make informed decisions on maintenance and renewal actions.In recent years,in-service vehicles equipped with on-board monitoring(OBM)measuring devices,such as accelerometers,have been introduced on railroad networks,traversing the network almost daily.This article explores the application of state-of-the-art OBM-based track quality indicators for railway infrastructure condition assessment and prediction,primarily under the prism of track geometry quality.The results highlight the similarities and advantages of applying track quality indicators generated from OBM measurements(high frequency and relatively lower accuracy data)compared to those generated from higher precision,yet temporally sparser,data collected by traditional track recording vehicles(TRVs)for infrastructure management purposes.The findings demonstrate the performance of the two approaches,further revealing the value of OBM information for monitoring the track status degradation process.This work makes a case for the advantageous use of OBM data for railway infrastructure management,and attempts to aid understanding in the application of OBM techniques for engineers and operators.展开更多
This paper focuses on AI intelligence as the fundamental direction to conduct research on information system operation and maintenance(O&M).Combining current AI-supported technologies in information system O&M...This paper focuses on AI intelligence as the fundamental direction to conduct research on information system operation and maintenance(O&M).Combining current AI-supported technologies in information system O&M,it proposes O&M strategies such as intelligent fault prediction and diagnosis,intelligent system performance optimization,intelligent system security protection,and adaptive system O&M implementation.Practical applications reveal that AI intelligence technology offers significant advantages in information system O&M,effectively addressing pain points of traditional O&M techniques,such as low fault prediction rates,slow repair speeds,poor security interception,and high labor costs.This substantially enhances the effectiveness of information system O&M.展开更多
Predictive maintenance is essential for the implementation of an innovative and efficient structural health monitoring strategy.Models capable of accurately interpreting new data automatically collected by suitably pl...Predictive maintenance is essential for the implementation of an innovative and efficient structural health monitoring strategy.Models capable of accurately interpreting new data automatically collected by suitably placed sensors to assess the state of the infrastructure represent a fundamental step,particularly for the railway sector,whose safe and continuous operation plays a strategic role in the well-being and development of nations.In this scenario,the benefits of a digital twin of a bonded insu-lated rail joint(IRJ)with the predictive capabilities of advanced classification algorithms based on artificial intelligence have been explored.The digital model provides an accurate mechanical response of the infrastructure as a pair of wheels passes over the joint.As bolt preload conditions vary,four structural health classes were identified for the joint.Two parameters,i.e.gap value and vertical displacement,which are strongly correlated with bolt preload,are used in different combinations to train and test five predictive classifiers.Their classification effectiveness was assessed using several performance indica-tors.Finally,we compared the IRJ condition predictions of two trained classifiers with the available data,confirming their high accuracy.The approach presented provides an interesting solution for future predictive tools in SHM especially in the case of complex systems such as railways where the vehicle-infrastructure interaction is complex and always time varying.展开更多
文摘While Artificial Intelligence (AI) is leading the way in terms of hardware advancements, such as GPUs, memory, and processing power, real-time applications are still catching up. It is inevitable that when one aspect leads and other trails behind, they coexist in life, as is often the case. The trailing aspect cannot remain far behind because, without application and use, there would be a dead end. Everything, whether an object, software, or tool, must have a practical use for humans. Without this, it will become obsolete. We can see this in many instances, such as blockchain technology, which is superior yet faces challenges in practical implementation, leading to a decline in adoption. This publication aims to bridge the gap between AI advancements and maintenance, specifically focusing on making predictive maintenance a practical application. There are multiple building blocks that make predictive maintenance a practical application. Each block performs a function leading to an output. This output forms an input to the receiving block. There are also foundational parts for all these building blocks to perform a function. Eventually, once the building blocks are connected, they form a loop and start to lead the path to predictive maintenance. Predictive maintenance is indeed practically achievable, but one must comprehend all the building blocks necessary for its implementation. Although detailed explanations will be provided in the upcoming sections, it is important to understand that simply purchasing software and plugging it in might be a far-fetched approach.
文摘Predictive maintenance plays a crucial role in preventing equipment failures and minimizing operational downtime in modern industries.However,traditional predictive maintenance methods often face challenges in adapting to diverse industrial environments and ensuring the transparency and fairness of their predictions.This paper presents a novel predictive maintenance framework that integrates deep learning and optimization techniques while addressing key ethical considerations,such as transparency,fairness,and explainability,in artificial intelligence driven decision-making.The framework employs an Autoencoder for feature reduction,a Convolutional Neural Network for pattern recognition,and a Long Short-Term Memory network for temporal analysis.To enhance transparency,the decision-making process of the framework is made interpretable,allowing stakeholders to understand and trust the model’s predictions.Additionally,Particle Swarm Optimization is used to refine hyperparameters for optimal performance and mitigate potential biases in the model.Experiments are conducted on multiple datasets from different industrial scenarios,with performance validated using accuracy,precision,recall,F1-score,and training time metrics.The results demonstrate an impressive accuracy of up to 99.92%and 99.45%across different datasets,highlighting the framework’s effectiveness in enhancing predictive maintenance strategies.Furthermore,the model’s explainability ensures that the decisions can be audited for fairness and accountability,aligning with ethical standards for critical systems.By addressing transparency and reducing potential biases,this framework contributes to the responsible and trustworthy deployment of artificial intelligence in industrial environments,particularly in safety-critical applications.The results underscore its potential for wide application across various industrial contexts,enhancing both performance and ethical decision-making.
基金financially supported by the Innovative Research Group Project of the National Natural Science Foundation of China (22021004)Sinopec Major Science and Technology Projects (321123-1)
文摘The fractionating tower bottom in fluid catalytic cracking Unit (FCCU) is highly susceptible to coking due to the interplay of complex external operating conditions and internal physical properties. Consequently, quantitative risk assessment (QRA) and predictive maintenance (PdM) are essential to effectively manage coking risks influenced by multiple factors. However, the inherent uncertainties of the coking process, combined with the mixed-frequency nature of distributed control systems (DCS) and laboratory information management systems (LIMS) data, present significant challenges for the application of data-driven methods and their practical implementation in industrial environments. This study proposes a hierarchical framework that integrates deep learning and fuzzy logic inference, leveraging data and domain knowledge to monitor the coking condition and inform prescriptive maintenance planning. The framework proposes the multi-layer fuzzy inference system to construct the coking risk index, utilizes multi-label methods to select the optimal feature dataset across the reactor-regenerator and fractionation system using coking risk factors as label space, and designs the parallel encoder-integrated decoder architecture to address mixed-frequency data disparities and enhance adaptation capabilities through extracting the operation state and physical properties information. Additionally, triple attention mechanisms, whether in parallel or temporal modules, adaptively aggregate input information and enhance intrinsic interpretability to support the disposal decision-making. Applied in the 2.8 million tons FCCU under long-period complex operating conditions, enabling precise coking risk management at the fractionating tower bottom.
基金Supported by the National Natural Science Foundation of China under Grant No.11472076Heilongjiang Provincial Universities Basic Scientific Research Business Fee Research Project No.145209210.
文摘Offshore platforms are large,complex structures designed for long-term service,and they are characterized by high risk and significant investment.Ensuring the safety and reliability of in-service offshore platforms requires intelligent operation and maintenance strategies.Digital twin technology can enable the accurate description and prediction of changes in the platform’s physical state through real-time monitoring data.This technology is expected to revolutionize the maintenance of existing offshore platform structures.A digital twin system is proposed for real-time assessment of structural health,prediction of residual life,formulation of maintenance plans,and extension of service life through predictive maintenance.The system integrates physical entities,digital models,intelligent predictive maintenance tools,a visualization platform,and interconnected modules to provide a comprehensive and efficient maintenance framework.This paper examines the current development status of core technologies in physical entity monitoring,digital model construction,and intelligent predictive maintenance.It also outlines future directions for the advancement of these technologies within the digital twin system,offering technical insights and practical references to support further research and applications of digital twin technology in offshore platform structures.
基金supported by National Natural Science Foundation of China (Grant No. 51005041)Fundamental Research Funds for the Central Universities of China (Grant No. N090303005)Key National Science & Technology Special Project on High-Grade CNC Machine Tools and Basic Manufacturing Equipment of China (Grant No. 2010ZX04014-014)
文摘The reliability-based maintenance optimization model has been focused by the engineers and scholars but it has never been solved effectively to formulate the effect of a maintenance action on the optimization model. In existing works, the system reliability was assumed to be increased to 1 after a predictive maintenance. However, it is very difficult in the most practical systems. Therefore, a new reliability-based maintenance optimization model under imperfect predictive maintenance (PM) is proposed in this paper. In the model, the system reliability is only restored to R i (0<R i <1, i∈N, N is natural number set) after the ith PM. The system uptimes and the corresponding probability in two cases whether there is an unexpected fault in one cycle are derived respectively and the system expected uptime model is given. To formulate the system expected downtime, the probability of each imperfect PM number in one cycle is calculated. Then, the system expected total time model is obtained. The total expected long-term operation cost is composed of the expected maintenance cost, the expected loss due to the downtime and the expected additional cost due to the occurrence of an unexpected failure. They are modeled respectively in this work. Jointing the system expected total time and long-term operation cost in one cycle, the expected long-term operation cost per time could be computed. Then, the proposed maintenance optimization model is formulated where the objective function is to minimize the expected long-term operation cost per time. The results of numerical example show that the proposed model could scheme the optimal maintenance actions for the considered system when the required parameters are given and the optimal solution of the proposed model is sensitive to the parameters of effective age model and insensitive to other parameters. The proposed model effectively solves the problem of evaluating the effect of an imperfect PM on the system reliability and presents a more practical optimization method for the reliability-based maintenance strategy than the existing works.
基金supported by National Natural Science Foundation of China(91860139)China Postdoctoral Science Foundation(2015M581792)。
文摘The vast potential of system health monitoring and condition based maintenance on modern commercial aircraft is being realized through the innovative use of Airplane Condition Monitoring System(ACMS) data.However there are few methods addressing the issues of failure prognostics and predictive maintenance for commercial aircraft Air Conditioning System(ACS).This study developed a Bayesian failure prognostics approach using ACMS data for predictive maintenance of ACS.First, a health index characterizing the ACS health state is inferred from a multiple sensor signals using a data driven method.Then a dynamic linear model is proposed to describe the degradation process for failure prognostics.Bayesian inference formulas are carried out for degradation estimation and prediction.The developed approach is applied on a passenger aircraft fleet with ACMS data recorded for one year.The analysis of the case study shows that the developed method can produce satisfactory prognostics results, where all the ACS failure precursors are identified in advance, and the relative errors for the failure time prediction made when just entering the degradation warning stage are less than 8%.This would allow operators to proactively plan future maintenance.
基金The National Natural Science Foundation of China(No.52278312)the National Key Research and Development Program of China(No.2022YFC3801202)the Fundamental Research Funds for the Central Universities.
文摘Structural health monitoring and performance prediction are crucial for smart disaster mitigation and intelligent management of structures throughout their lifespan.Recent advancements in predictive maintenance strategies within the industrial manufacturing industry have inspired similar innovations in civil engineering,aiming to improve structural performance evaluation,damage diagnosis,and capacity prediction.This review delves into the framework of predictive maintenance and examines various existing solutions,focusing on critical areas such as data acquisition,condition monitoring,damage prognosis,and maintenance planning.Results from real-world applications of predictive maintenance in civil engineering,covering high-rise structures,deep foundation pits,and other infrastructure,are presented.The challenges of implementing predictive maintenance in civil engineering structures under current technology,such as model interpretability of data-driven methods and standards for predictive maintenance,are explored.Future research prospects within this area are also discussed.
基金support by Natural Science Foundation of China(61873122)。
文摘Remaining useful life(RUL)prediction is an advanced technique for system maintenance scheduling.Most of existing RUL prediction methods are only interested in the precision of RUL estimation;the adverse impact of overestimated RUL on maintenance scheduling is not of concern.In this work,an RUL estimation method with risk-averse adaptation is developed which can reduce the over-estimation rate while maintaining a reasonable under-estimation level.The proposed method includes a module of degradation feature selection to obtain crucial features which reflect system degradation trends.Then,the latent structure between the degradation features and the RUL labels is modeled by a support vector regression(SVR)model and a long short-term memory(LSTM)network,respectively.To enhance the prediction robustness and increase its marginal utility,the SVR model and the LSTM model are integrated to generate a hybrid model via three connection parameters.By designing a cost function with penalty mechanism,the three parameters are determined using a modified grey wolf optimization algorithm.In addition,a cost metric is proposed to measure the benefit of such a risk-averse predictive maintenance method.Verification is done using an aero-engine data set from NASA.The results show the feasibility and effectiveness of the proposed RUL estimation method and the predictive maintenance strategy.
基金supported by UT-INSA Program(2013)the support of the China Scholarship Council(CSC)
文摘Airframe maintenance is traditionally performed at scheduled maintenance stops.The decision to repair a fuselage panel is based on a fixed crack size threshold,which allows to ensure the aircraft safety until the next scheduled maintenance stop.With progress in sensor technology and data processing techniques,structural health monitoring（SHM） systems are increasingly being considered in the aviation industry.SHM systems track the aircraft health state continuously,leading to the possibility of planning maintenance based on an actual state of aircraft rather than on a fixed schedule.This paper builds upon a model-based prognostics framework that the authors developed in their previous work,which couples the Extended Kalman filter（EKF） with a firstorder perturbation（FOP） method.By using the information given by this prognostics method,a novel cost driven predictive maintenance（CDPM） policy is proposed,which ensures the aircraft safety while minimizing the maintenance cost.The proposed policy is formally derived based on the trade-off between probabilities of occurrence of scheduled and unscheduled maintenance.A numerical case study simulating the maintenance process of an entire fleet of aircrafts is implemented.Under the condition of assuring the same safety level,the CDPM is compared in terms of cost with two other maintenance policies：scheduled maintenance and threshold based SHM maintenance.The comparison results show CDPM could lead to significant cost savings.
基金the National Natural Science Founda-tion of China(No.51475301)the Fundamental Hesearch Funds for the Central Universities of China(No.2232017A-03)。
文摘The fundamental process of predictive maintenance is prognostics and health management,and it is the tool resulting in the development of many algorithms to predict the remaining useful life of industrial equipment.A new data-driven predictive maintenance and an architectural impulse,based on a regularized deep neural network using predictive analytics,are proposed successfully for ring spinning technology.The paradigm shift in computational infrastructures enormously puts pressure on large-scale linear and non-linear automated assembly systems to eliminate and cut down unscheduled downtime and unexpected stoppages.The sensor network designed for the scheduling process comprises different critical components of the same spinning machine frames containing more than thousands of spindles attached to them.We established a genetic algorithm based on multi-sensor performance assessment and prediction procedure for the spinning system.Results show that it operates with a relatively less amount of training data sets but takes advantage of larger volumes of data.This integrated system aims to prognosticate abnormalities,disturbances,and failures by providing condition-based monitoring for each component,which makes it more accurate to locate the defined component failures in the current spinning spindles by using smart agents during the operations through the neural sensing network.A case study has provided to demonstrate the feasibility of the proposed predictive model for highly dynamic,high-speed textile spinning system through real-time data sensing and signal processing via the industrial Internet of Things.
基金This paper is supported by the NCAIRF 079 project fund.The project is funded by National Center of Artificial Intelligence.
文摘Predictive maintenance is a vital aspect of the industrial sector,and the use of Industrial Internet of Things(IIoT)sensor nodes is becoming increasingly popular for detecting motor faults and monitoring motor conditions.An integrated approach for acquiring,processing,and wirelessly transmitting a large amount of data in predictive maintenance applications remains a significant challenge.This study presents an IIoT-based sensor node for industrial motors.The sensor node is designed to acquire vibration data on the radial and axial axes of the motor and utilizes a hybrid approach for efficient data processing via edge and cloud platforms.The initial step of signal processing is performed on the node at the edge,reducing the burden on a centralized cloud for processing data from multiple sensors.The proposed architecture utilizes the lightweight Message Queue Telemetry Transport(MQTT)communication protocol for seamless data transmission from the node to the local and main brokers.The broker’s bridging allows for data backup in case of connection loss.The proposed sensor node is rigorously tested on a motor testbed in a laboratory setup and an industrial setting in a rice industry for validation,ensuring its performance and accuracy in real-world industrial environments.The data analysis and results from both testbed and industrial motors were discussed using vibration analysis for identifying faults.The proposed sensor node is a significant step towards improving the efficiency and reliability of industrial motors through realtime monitoring and early fault detection,ultimately leading to minimized unscheduled downtime and cost savings.
文摘Predictive Maintenance is a type of condition-based maintenance that assesses the equipment's states and estimates its failure probability and when maintenance should be performed.Although machine learning techniques have been frequently implemented in this area,the existing studies disregard to the nat-ural order between the target attribute values of the historical sensor data.Thus,these methods cause losing the inherent order of the data that positively affects the prediction performances.To deal with this problem,a novel approach,named Ordinal Multi-dimensional Classification(OMDC),is proposed for estimating the conditions of a hydraulic system's four components by taking into the natural order of class values.To demonstrate the prediction ability of the proposed approach,eleven different multi-dimensional classification algorithms(traditional Binary Relevance(BR),Classifier Chain(CC),Bayesian Classifier Chain(BCC),Monte Carlo Classifier Chain(MCC),Probabilistic Classifier Chain(PCC),Clas-sifier Dependency Network(CDN),Classifier Trellis(CT),Classifier Dependency Trellis(CDT),Label Powerset(LP),Pruned Sets(PS),and Random k-Labelsets(RAKEL))were implemented using the Ordinal Class Classifier(OCC)algorithm.Besides,seven different classification algorithms(Multilayer Perceptron(MLP),Support Vector Machine(SVM),k-Nearest Neighbour(kNN),Decision Tree(C4.5),Bagging,Random Forest(RF),and Adaptive Boosting(AdaBoost))were chosen as base learners for the OCC algorithm.The experimental results present that the proposed OMDC approach using binary relevance multi-dimensional classification methods predicts the conditions of a hydraulic system's multiple components with high accuracy.Also,it is clearly seen from the results that the OMDC models that utilize ensemble-based classification algorithms give more reliable prediction performances with an average Hamming score of 0.853 than the others that use traditional algorithms as base learners.
文摘Manned spacecraft pose challenges in terms of extremely high safety and reliability, and with the growth of system complexity and longer on-orbit operation time, the traditional management mode, such as monitoring the threshold of parameter passively, is difficult to meet the required safety standards. Predictive maintenance, which analyzes the system heath trend and estimates remaining useful life(RUL) to establish maintenance strategies ahead of time before failure occurs, is a new mode to approach maintenance tasks. Here, a predictive maintenance strategy for complex manned spacecraft is proposed based on the remaining useful life estimation technique. Firstly, a health index is established based on an abundance of telemetry data, reflecting the system's current health state. Secondly, we map the health index to the remaining useful life through system degradation modelling, taking into consideration both the system's stochastic deterioration and uncertainty. The maintenance and management strategies are then made based on the calculated distribution of RUL time. Finally, a case study on Chinese space station energy system predictive maintenance is presented.
文摘This paper provides a review of predictive analytics for roads,identifying gaps and limitations in current methodologies.It explores the implications of these limitations on accuracy and application,while also discussing how advanced predictive analytics can address these challenges.The article acknowledges the transformative shift brought about by technological advancements and increased computational capabilities.The degradation of pavement surfaces due to increased road users has resulted in safety and comfort issues.Researchers have conducted studies to assess pavement condition and predict future changes in pavement structure.Pavement Management Systems are crucial in developing prediction performance models that estimate pavement condition and degradation severity over time.Machine learning algorithms,artificial neural networks,and regression models have been used,with strengths and weaknesses.Researchers generally agree on their accuracy in estimating pavement condition considering factors like traffic,pavement age,and weather conditions.However,it is important to carefully select an appropriate prediction model to achieve a high-quality prediction performance system.Understanding the strengths and weaknesses of each model enables informed decisions for implementing prediction models that suit specific needs.The advancement of prediction models,coupled with innovative technologies,will contribute to improved pavement management and the overall safety and comfort of road users.
文摘In the era of Industry 4.0,conditionmonitoring has emerged as an effective solution for process industries to optimize their operational efficiency.Condition monitoring helps minimize unplanned downtime,extending equipment lifespan,reducing maintenance costs,and improving production quality and safety.This research focuses on utilizing Bayesian search-based machine learning and deep learning approaches for the condition monitoring of industrial equipment.The study aims to enhance predictive maintenance for industrial equipment by forecasting vibration values based on domain-specific feature engineering.Early prediction of vibration enables proactive interventions to minimize downtime and extend the lifespan of critical assets.A data set of load information and vibration values from a heavy-duty industrial slip ring induction motor(4600 kW)and gearbox equipped with vibration sensors is used as a case study.The study implements and compares six machine learning models with the proposed Bayesian-optimized stacked Long Short-Term Memory(LSTM)model.The hyperparameters used in the implementation of models are selected based on the Bayesian optimization technique.Comparative analysis reveals that the proposed Bayesian optimized stacked LSTM outperforms other models,showcasing its capability to learn temporal features as well as long-term dependencies in time series information.The implemented machine learning models:Linear Regression(LR),RandomForest(RF),Gradient Boosting Regressor(GBR),ExtremeGradient Boosting(XGBoost),Light Gradient Boosting Machine(LightGBM),and Support Vector Regressor(SVR)displayed a mean squared error of 0.9515,0.4654,0.1849,0.0295,0.2127 and 0.0273,respectively.The proposed model predicts the future vibration characteristics with a mean squared error of 0.0019 on the dataset containing motor load information and vibration characteristics.The results demonstrate that the proposed model outperforms other models in terms of other evaluation metrics with a mean absolute error of 0.0263 and 0.882 as a coefficient of determination.Current research not only contributes to the comparative performance of machine learning models in condition monitoring but also showcases the practical implications of employing these techniques.By transitioning fromreactive to proactive maintenance strategies,industries canminimize downtime,reduce costs,and prolong the lifespan of crucial assets.This study demonstrates the practical advantages of transitioning from reactive to proactive maintenance strategies using ML-based condition monitoring.
基金supported financially by the project OMISM from the ETH Zurich Mobility Initiative。
文摘Railway infrastructure is a crucial asset for the mobility of people and goods.The increased traffic frequency imposes higher loads and speeds,leading to accelerated infrastructure degradation.Asset managers require timely information regarding the current(diagnosis)and future(prognosis)condition of their assets to make informed decisions on maintenance and renewal actions.In recent years,in-service vehicles equipped with on-board monitoring(OBM)measuring devices,such as accelerometers,have been introduced on railroad networks,traversing the network almost daily.This article explores the application of state-of-the-art OBM-based track quality indicators for railway infrastructure condition assessment and prediction,primarily under the prism of track geometry quality.The results highlight the similarities and advantages of applying track quality indicators generated from OBM measurements(high frequency and relatively lower accuracy data)compared to those generated from higher precision,yet temporally sparser,data collected by traditional track recording vehicles(TRVs)for infrastructure management purposes.The findings demonstrate the performance of the two approaches,further revealing the value of OBM information for monitoring the track status degradation process.This work makes a case for the advantageous use of OBM data for railway infrastructure management,and attempts to aid understanding in the application of OBM techniques for engineers and operators.
文摘This paper focuses on AI intelligence as the fundamental direction to conduct research on information system operation and maintenance(O&M).Combining current AI-supported technologies in information system O&M,it proposes O&M strategies such as intelligent fault prediction and diagnosis,intelligent system performance optimization,intelligent system security protection,and adaptive system O&M implementation.Practical applications reveal that AI intelligence technology offers significant advantages in information system O&M,effectively addressing pain points of traditional O&M techniques,such as low fault prediction rates,slow repair speeds,poor security interception,and high labor costs.This substantially enhances the effectiveness of information system O&M.
基金the National Recovery and Resilience Plan (NRRP), Mission 4 Component 2 Investment 1.4-Call for tender No. 3138 of 16/12/2021 of Italian Ministry of University and Research funded by the European Union-Next Generation EU. Award Number: Project code CN00000023Concession Decree No. 1033 of 17/06/2022 adopted by the Italian Ministry of University and Research, CUP D93C22000400001, “Sustainable Mobility Center” (CNMS). Spoke 4-Rail Transportation
文摘Predictive maintenance is essential for the implementation of an innovative and efficient structural health monitoring strategy.Models capable of accurately interpreting new data automatically collected by suitably placed sensors to assess the state of the infrastructure represent a fundamental step,particularly for the railway sector,whose safe and continuous operation plays a strategic role in the well-being and development of nations.In this scenario,the benefits of a digital twin of a bonded insu-lated rail joint(IRJ)with the predictive capabilities of advanced classification algorithms based on artificial intelligence have been explored.The digital model provides an accurate mechanical response of the infrastructure as a pair of wheels passes over the joint.As bolt preload conditions vary,four structural health classes were identified for the joint.Two parameters,i.e.gap value and vertical displacement,which are strongly correlated with bolt preload,are used in different combinations to train and test five predictive classifiers.Their classification effectiveness was assessed using several performance indica-tors.Finally,we compared the IRJ condition predictions of two trained classifiers with the available data,confirming their high accuracy.The approach presented provides an interesting solution for future predictive tools in SHM especially in the case of complex systems such as railways where the vehicle-infrastructure interaction is complex and always time varying.
