This paper focuses on the key issues of tool wear condition monitoring in the field of machining,and deeply discusses the application of digital twin technology in this aspect.This paper expounds the principle and arc...This paper focuses on the key issues of tool wear condition monitoring in the field of machining,and deeply discusses the application of digital twin technology in this aspect.This paper expounds the principle and architecture of digital twin technology,analyzes its specific methods in tool wear data acquisition,modeling,simulation,and real-time monitoring,and shows the significant advantages of this technology in improving the accuracy of tool wear monitoring and realizing predictive maintenance.At the same time,the challenges faced by digital twin technology in tool wear condition monitoring are discussed,and the corresponding development direction is put forward,aiming to provide theoretical reference and practical guidance for optimizing tool management by digital twin technology in the machining industry.展开更多
Polymer gears are increasingly replacing metal gears in applications with low to medium torque.Traditionally,polymer gears have been manufactured using injection molding,but additive manufacturing(AM)is becoming incre...Polymer gears are increasingly replacing metal gears in applications with low to medium torque.Traditionally,polymer gears have been manufactured using injection molding,but additive manufacturing(AM)is becoming increasingly common.Among the different types of polymer gears,nylon gears are particularly popular.However,there is currently very limited understanding of the wear resistance of nylon gears and of the impact of the manufacturing method on gear wear performance.The aims of this work are(a)to study the wear process of nylon gears made using the conventional injection molding method and two popularly used AM methods,namely,fused deposition modeling and selective laser sintering,(b)to compare and understand the wear performance by monitoring the evolution of the gear surfaces of the teeth,and(c)to study the effect of wear on the gear dynamics by analyzing gearbox vibration signals.This article presents experimental work,data analysis of the wear processes using molding and image analysis techniques,as well as the vibration data collected during gear wear tests.It also provides key results and further insights into the wear performance of the tested nylon gears.The information gained in this study is useful for better understanding the degradation process of additively manufactured nylon gears.展开更多
As one of the most important terminals in machining, cutting tools have been widely used for components manufacturing in aerospace and other industries. The quality of these components and processing efficiency are cl...As one of the most important terminals in machining, cutting tools have been widely used for components manufacturing in aerospace and other industries. The quality of these components and processing efficiency are closely linked to the performance of cutting tools. Therefore, it is essential and critical to inspect the cutting tools and monitor the condition during the stage of manufacturing and machining. This review aims to discuss and summarize the key problems, methods,and techniques from the perspective of the tool geometric and the physical quantities measurement,including machine vision, physical sensors and data processing. It is worth mentioning that we focus on the topic of precision measurement methods and discuss universal solutions by identifying the common characteristics of the measured quantities. Eventually, the challenges and future trends for the development of in-depth research and practical applications are concluded. The research and application of precise measurement techniques for geometric and physical quantities will better promote the development of intelligent manufacturing.展开更多
Background Traditional methods for monitoring mining equipment rely primarily on visual inspections,which are time-consuming,inefficient,and hazardous.This article introduces a novel approach to monitoring mission-cri...Background Traditional methods for monitoring mining equipment rely primarily on visual inspections,which are time-consuming,inefficient,and hazardous.This article introduces a novel approach to monitoring mission-critical systems and services in the mining industry by integrating virtual reality(VR)and digital twin(DT)technologies.VR-based DTs enable remote equipment monitoring,advanced analysis of machine health,enhanced visualization,and improved decision making.Methods This article presents an architecture for VR-based DT development,including the developmental stages,activities,and stakeholders involved.A case study on the condition monitoring of a conveyor belt using real-time synthetic vibration sensor data was conducted using the proposed methodology.The study demonstrated the application of the methodology in remote monitoring and identified the need for further development for implementation in active mining operations.The article also discusses interdisciplinarity,choice of tools,computational resources,time and cost,human involvement,user acceptance,frequency of inspection,multiuser environment,potential risks,and applications beyond the mining industry.Results The findings of this study provide a foundation for future research in the domain of VR-based DTs for remote equipment monitoring and a novel application area for VR in mining.展开更多
Battery systems are increasingly being used for powering ocean going ships,and the number of fully electric or hybrid ships relying on battery power for propulsion is growing.To ensure the safety of such ships,it is i...Battery systems are increasingly being used for powering ocean going ships,and the number of fully electric or hybrid ships relying on battery power for propulsion is growing.To ensure the safety of such ships,it is important to monitor the available energy that can be stored in the batteries,and classification societies typically require the state of health(SOH)to be verified by independent tests.This paper addresses statistical modeling of SOH for maritime lithium-ion batteries based on operational sensor data.Various methods for sensor-based,data-driven degradation monitoring will be presented,and advantages and challenges with the different approaches will be discussed.The different approaches include cumulative degradation models and snapshot models,models that need to be trained and models that need no prior training,and pure data-driven models and physics-informed models.Some of the methods only rely on measured data,such as current,voltage,and temperature,whereas others rely on derived quantities such as state of charge.Models include simple statistical models and more complicated machine learning techniques.Insight from this exploration will be important in establishing a framework for data-driven diagnostics and prognostics of maritime battery systems within the scope of classification societies.展开更多
Latent variable models can effectively determine the condition of essential rotating machinery without needing labeled data.These models analyze vibration data via an unsupervised learning strategy.Temporal preservati...Latent variable models can effectively determine the condition of essential rotating machinery without needing labeled data.These models analyze vibration data via an unsupervised learning strategy.Temporal preservation is necessary to obtain an informative latent manifold for the fault diagnosis task.In a temporalpreserving context,two approaches exist to develop a condition-monitoring methodology:offline and online.For latent variable models,the available training modes are not different.While many traditional methods use offline training,online training can dynamically adjust the latent manifold,possibly leading to better fault signature extraction from the vibration data.This study explores online training using temporal-preserving latent variable models.Within online training,there are two main methods:one focuses on reconstructing data and the other on interpreting the data components.Both are considered to evaluate how they diagnose faults over time.Using two experimental datasets,the study confirms that models from both training modes can detect changes in machinery health and identify faults even under varying conditions.Importantly,the complementarity of offline and online models is emphasized,reassuring their versatility in fault diagnostics.Understanding the implications of the training approach and the available model formulations is crucial for further research in latent variable modelbased fault diagnostics.展开更多
Lithium ion batteries are complicated distributed parameter systems that can be described preferably by partial differential equations and a field theory. To reduce the solution difficulty and the calculation amount, ...Lithium ion batteries are complicated distributed parameter systems that can be described preferably by partial differential equations and a field theory. To reduce the solution difficulty and the calculation amount, if a distributed parameter system is described by ordinary differential equations （ODE） during the analysis and the design of distributed parameter system, the reliability of the system description will be reduced, and the systemic errors will be introduced. Studies on working condition real-time monitoring can improve the security because the rechargeable LIBs are widely used in many electronic systems and electromechanical equipment. Single particle model （SPM） is the simplification of LIB under some approximations, and can estimate the working parameters of a LIB at the faster simulation speed. A LIB modelling algorithm based on PDEs and SPM is proposed to monitor the working condition of LIBs in real time. Although the lithium ion concentration is an unmeasurable distributed parameter in the anode of LIB, the working condition monitoring model can track the real time lithium ion concentration in the anode of LIB, and calculate the residual which is the difference between the ideal data and the measured data. A fault alarm can be triggered when the residual is beyond the preset threshold. A simulation example verifies that the effectiveness and the accuracy of the working condition real-time monitoring model of LIB based on PDEs and SPM.展开更多
Reliable on line cutting tool conditioning monitoring is an essential feature of automatic machine tool and flexible manufacturing system (FMS) and computer integrated manufacturing system (CIMS). Recently artificia...Reliable on line cutting tool conditioning monitoring is an essential feature of automatic machine tool and flexible manufacturing system (FMS) and computer integrated manufacturing system (CIMS). Recently artificial neural networks (ANNs) are used for this purpose in conjunction with suitable sensory systems. The present work in Norwegian University of Science and Technology (NTNU) uses back propagation neural networks (BP) and fuzzy neural networks (FNN) to process the cutting tool state data measured with force and acoustic emission (AE) sensors, and implements a valuable on line tool condition monitoring system using the ANNs. Different ANN structures are designed and investigated to estimate the tool wear state based on the fusion of acoustic emission and force signals. Finally, four case studies are introduced for the sensing and ANN processing of the tool wear states and the failures of the tool with practical experiment examples. The results indicate that a tool wear identification system can be achieved using the sensors integration with ANNs, and that ANNs provide a very effective method of implementing sensor integration for on line monitoring of tool wear states and abnormalities.展开更多
High-Speed Trains (HSTs) have emerged as a mainstream mode of transportation in China, owing to their exceptional safety and efficiency. Ensuring the reliable operation of HSTs is of paramount economic and societal im...High-Speed Trains (HSTs) have emerged as a mainstream mode of transportation in China, owing to their exceptional safety and efficiency. Ensuring the reliable operation of HSTs is of paramount economic and societal importance. As critical rotating mechanical components of the transmission system, bearings make their fault diagnosis a topic of extensive attention. This paper provides a systematic review of image encoding-based bearing fault diagnosis methods tailored to the condition monitoring of HSTs. First, it categorizes the image encoding techniques applied in the field of bearing fault diagnosis. Then, a review of state-of-the-art studies has been presented, encompassing both monomodal image conversion and multimodal image fusion approaches. Finally, it highlights current challenges and proposes future research directions to advance intelligent fault diagnosis in HSTs, aiming to provide a valuable reference for researchers and engineers in the field of intelligent operation and maintenance.展开更多
Efficient tool condition monitoring techniques help to realize intelligent management of tool life and reduce tool usage costs.In this paper,the influence of different wear degrees of ball-end milling cutters on the t...Efficient tool condition monitoring techniques help to realize intelligent management of tool life and reduce tool usage costs.In this paper,the influence of different wear degrees of ball-end milling cutters on the texture shape of machining tool marks is investigated,and a method is proposed for predicting the wear state(including the position and degree of tool wear)of ball-end milling cutters based on entropy measurement of tool mark texture images.Firstly,data samples are prepared through wear experiments,and the change law of the tool mark texture shape with the tool wear state is analyzed.Then,a two-dimensional sample entropy algorithm is developed to quantify the texture morphology.Finally,the processing parameters and tool attitude are integrated into the prediction process to predict the wear value and wear position of the ball end milling cutter.After testing,the correlation between the predicted value and the standard value of the proposed tool condition monitoring method reaches 95.32%,and the accuracy reaches 82.73%,indicating that the proposed method meets the requirement of tool condition monitoring.展开更多
Submodule capacitor aging poses significant challenges to the safe operation of modular multilevel converter(MMC)systems.Traditional detection methods rely predominantly on offline tests,lacking real-time evaluation c...Submodule capacitor aging poses significant challenges to the safe operation of modular multilevel converter(MMC)systems.Traditional detection methods rely predominantly on offline tests,lacking real-time evaluation capabilities.Moreover,existing online approaches require additional sampling channels,thereby increasing system complexity and costs.To address these issues,this paper proposes an online evaluation method for submodule capacitor aging based on CapAgingNet.Initially,an MMC system simulation platform is developed to examine the effects of submodule capacitor aging on system operational characteristics and to create a dataset of submodule capacitor switching states.Subsequently,the CapAgingNet model is introduced,incorporating key technical modules to enhance performance:the Deep Stem module,which extracts larger receptive fields through multiple convolution layers and mitigates the impact of data sparsity in capacitor aging on feature extraction;the efficient channel attention(ECA)module,utilizing onedimensional convolution for dynamic weighting to adjust the importance of each channel,thereby enhancing the ability of the model to process high-dimensional features in capacitor aging data;and the multiscale feature fusion(MSF)module,which integrates capacitor aging information across different scales by combining fine-grained and coarse-grained features,thus improving the capacity of the model to capture high-frequency variation characteristics.The experimental results reveal that the CapAgingNet model achieves a TOP-1 accuracy of 95.32%and a macro-averaged F1 score of 95.49%on the test set,thereby providing effective technical support for online monitoring of submodule capacitor aging.展开更多
In this work, an adaptive control constraint system has been developed for computer numerical control （CNC） turning based on the feedback control and adaptive control/self-tuning control. In an adaptive controlled s...In this work, an adaptive control constraint system has been developed for computer numerical control （CNC） turning based on the feedback control and adaptive control/self-tuning control. In an adaptive controlled system, the signals from the online measurement have to be processed and fed back to the machine tool controller to adjust the cutting parameters so that the machining can be stopped once a certain threshold is crossed. The main focus of the present work is to develop a reliable adaptive control system, and the objective of the control system is to control the cutting parameters and maintain the displacement and tool flank wear under constraint valves for a particular workpiece and tool combination as per ISO standard. Using Matlab Simulink, the digital adaption of the cutting parameters for experiment has confirmed the efficiency of the adaptively controlled condition monitoring system, which is reflected in different machining processes at varying machining conditions. This work describes the state of the art of the adaptive control constraint （ACC） machining systems for turning. AIS14140 steel of 150 BHN hardness is used as the workpiece material, and carbide inserts are used as cutting tool material throughout the experiment. With the developed approach, it is possible to predict the tool condition pretty accurately, if the feed and surface roughness are measured at identical conditions. As part of the present research work, the relationship between displacement due to vibration, cutting force, flank wear, and surface roughness has been examined.展开更多
Weak feature extraction is of great importance for condition monitoring and intelligent diagnosis of aeroengine.Aimed at achieving intelligent diagnosis of aero-engine main shaft bearing,an enhanced sparsity-assisted ...Weak feature extraction is of great importance for condition monitoring and intelligent diagnosis of aeroengine.Aimed at achieving intelligent diagnosis of aero-engine main shaft bearing,an enhanced sparsity-assisted intelligent condition monitoring method is proposed in this paper.Through analyzing the weakness of convex sparse model,i.e.the tradeoff between noise reduction and feature reconstruction,this paper proposes an enhanced-sparsity nonconvex regularized convex model based on Moreau envelope to achieve weak feature extraction.Accordingly,a sparsity-assisted deep convolutional variational autoencoders network is proposed,which achieves the intelligent identification of fault state through training denoised normal data.Finally,the effectiveness of the proposed method is verified through aero-engine bearing run-to-failure experiment.The comparison results show that the proposed method is good at abnormal pattern recognition,showing a good potential for weak fault intelligent diagnosis of aero-engine main shaft bearings.展开更多
This article introduces a novel approach for tricone bit wear condition monitoring and failure prediction for surface mining applications.A successful bit health monitoring system is essential to achieve fully autonom...This article introduces a novel approach for tricone bit wear condition monitoring and failure prediction for surface mining applications.A successful bit health monitoring system is essential to achieve fully autonomous blasthole drilling.In this research in-situ vibration signals were analyzed in timefrequency domain and signals trend during tricone bit life span were investigated and introduced to support the development of artificial intelligence(AI)models.In addition to the signal statistical features,wavelet packet energy distribution proved to be a powerful indicator for bit wear assessment.Backpropagation artificial neural network(ANN)models were designed,trained and evaluated for bit state classification.Finally,an ANN architecture and feature vector were introduced to classify the bit condition and predict the bit failure.展开更多
It is common for wind turbines to be installed in remote locations on land or offshore, leading to difficulties in routine inspection and maintenance. Further, wind turbines in these locations are often subject to har...It is common for wind turbines to be installed in remote locations on land or offshore, leading to difficulties in routine inspection and maintenance. Further, wind turbines in these locations are often subject to harsh operating conditions. These challenges mean there is a requirement for a high degree of maintenance. The data generated by monitoring systems can be used to obtain models of wind turbines operating under different conditions, and hence predict output signals based on known inputs. A model-based condition monitoring system can be implemented by comparing output data obtained from operational turbines with those predicted by the models, so as to detect changes that could be due to the presence of faults. This paper discusses several techniques for model-based condition monitoring systems: linear models, artificial neural networks, and state dependent parameter "pseudo" transfer functions.The models are identified using supervisory control and data acquisition(SCADA) data acquired from an operational wind firm. It is found that the multiple-input single-output state dependent parameter method outperforms both multivariate linear and artificial neural network-based approaches. Subsequently, state dependent parameter models are used to develop adaptive thresholds for critical output signals. In order to provide an early warning of a developing fault, it is necessary to interpret the amount by which the threshold is exceeded, together with the period of time over which this occurs. In this regard, a fuzzy logic-based inference system is proposed and demonstrated to be practically feasible.展开更多
Nowadays,renewable energy has been emerging as the major source of energy and is driven by its aggressive expansion and falling costs.Most of the renewable energy sources involve turbines and their operation and maint...Nowadays,renewable energy has been emerging as the major source of energy and is driven by its aggressive expansion and falling costs.Most of the renewable energy sources involve turbines and their operation and maintenance are vital and a difficult task.Condition monitoring and fault diagnosis have seen remarkable and revolutionary up-gradation in approaches,practices and technology during the last decade.Turbines mostly do use a rotating type of machinery and analysis of those signals has been challenging to localize the defect.This paper proposes a new hybrid model wherein multiple swarm intelligence models have been evaluated to optimize the conventional Long Short-Term Memory(LSTM)model in classifying the faults from the vibration signals data acquired from the gearbox.This helps to analyze the performance and behavioral patterns of the system more effectively and efficiently which helps to suggest for replacement of the unit with higher precision.The results have demonstrated that the proposed hybrid modeling approach is effective in classifying the faults of the gearbox from the time series data and achieve higher diagnostic accuracy in comparison to the conventional LSTM methods.展开更多
This paper presents a novel approach to investigate the relations between drilling signals and bit wear condition in real world full-scale mining operations.This research addresses the increasing demand for automation...This paper presents a novel approach to investigate the relations between drilling signals and bit wear condition in real world full-scale mining operations.This research addresses the increasing demand for automation in mining to increase the efficiency,safety,and ability to work in harsh environments.A crucial issue in fully autonomous unmanned drilling is to have a system to detect the bit wear condition through the drilling signals analysis in real time.In this work,based on extensive field studies,a novel qualitative method for tricone bit wear state classification is developed and introduced.The relations between drilling vibration as well as electric motor current signals and bit wear are investigated and bit failure vibration frequencies,regardless of the geological conditions,are introduced.Bit failure frequencies are experimentally investigated and analytically calculated.Finally,the effect of bit design parameters on the failure frequencies is presented for the application of bit wear condition monitoring and bit failure prediction.展开更多
Turbopump condition monitoring is a significant approach to ensure the safety of liquid rocket engine （LRE）.Because of lack of fault samples,a monitoring system cannot be trained on all possible condition patterns.T...Turbopump condition monitoring is a significant approach to ensure the safety of liquid rocket engine （LRE）.Because of lack of fault samples,a monitoring system cannot be trained on all possible condition patterns.Thus it is important to differentiate abnormal or unknown patterns from normal pattern with novelty detection methods.One-class support vector machine （OCSVM） that has been commonly used for novelty detection cannot deal well with large scale samples.In order to model the normal pattern of the turbopump with OCSVM and so as to monitor the condition of the turbopump,a monitoring method that integrates OCSVM with incremental clustering is presented.In this method,the incremental clustering is used for sample reduction by extracting representative vectors from a large training set.The representative vectors are supposed to distribute uniformly in the object region and fulfill the region.And training OCSVM on these representative vectors yields a novelty detector.By applying this method to the analysis of the turbopump＇s historical test data,it shows that the incremental clustering algorithm can extract 91 representative points from more than 36 000 training vectors,and the OCSVM detector trained on these 91 representative points can recognize spikes in vibration signals caused by different abnormal events such as vane shedding,rub-impact and sensor faults.This monitoring method does not need fault samples during training as classical recognition methods.The method resolves the learning problem of large samples and is an alternative method for condition monitoring of the LRE turbopump.展开更多
Various technologies have recently been developed for high-speed railways, in order to boost commercial speeds from 300 km.h： to 400 km.h-1. Among these technologies, this paper introduces the 400 km-h-1 class curren...Various technologies have recently been developed for high-speed railways, in order to boost commercial speeds from 300 km.h： to 400 km.h-1. Among these technologies, this paper introduces the 400 km-h-1 class current collection performance evaluation methods that have been developed and demonstrated by Korea. Specifically, this paper reports details of the video-based monitoring techniques that have been adopted to inspect the stability of overhead contact line （OCL） components at 400 km.h-1 without direct contact with any components of the power supply system. Unlike conventional OCL monitoring systems, which detect contact wire positions using either laser sensors or line cameras, the developed system measures parameters in the active state by video data. According to experimental results that were obtained at a field-test site established at a commercial line, it is claimed that the proposed mea- surement system is capable of effectively measuring OCL parameters.展开更多
Three-dimensional(3D)microdisplacement monitoring plays a crucial role in the assembly of large aircraft.This paper presents a broadly applicable high-precision online 3D microdisplacement monitoring method and system...Three-dimensional(3D)microdisplacement monitoring plays a crucial role in the assembly of large aircraft.This paper presents a broadly applicable high-precision online 3D microdisplacement monitoring method and system based on proximity sensors as well as a corresponding in situ calibration method,which can be applied under various extreme working conditions encountered in the aircraft assembly process,such as compact and obstructed spaces.A 3D monitoring model is first established to achieve 3D microdisplacement monitoring based only on the one-dimensional distances measured by proximity sensors,which concerns the extrinsic sensor parameters,such as the probe base point(PBP)and the unit displacement vector(UDV).Then,a calibration method is employed to obtain these extrinsic parameters with high precision by combining spatial transformation principles and weighted optimization.Finally,calibration and monitoring experiments performed for a tailplane assembly process are reported.The calibration precision for the PBP is better than±10 lm in the X and Y directions and±2 lm in the Z direction,and the calibration precision for the UDV is better than 0.07°.Moreover,the accuracy of the 3D microdisplacement monitoring system can reach±15 lm.In general,this paper provides new insights into the modeling and calibration of 3D microdisplacement monitoring based on proximity sensors and a precise,efficient,and low-cost technical means for performing related measurements in compact spaces during the aircraft assembly process.展开更多
文摘This paper focuses on the key issues of tool wear condition monitoring in the field of machining,and deeply discusses the application of digital twin technology in this aspect.This paper expounds the principle and architecture of digital twin technology,analyzes its specific methods in tool wear data acquisition,modeling,simulation,and real-time monitoring,and shows the significant advantages of this technology in improving the accuracy of tool wear monitoring and realizing predictive maintenance.At the same time,the challenges faced by digital twin technology in tool wear condition monitoring are discussed,and the corresponding development direction is put forward,aiming to provide theoretical reference and practical guidance for optimizing tool management by digital twin technology in the machining industry.
文摘Polymer gears are increasingly replacing metal gears in applications with low to medium torque.Traditionally,polymer gears have been manufactured using injection molding,but additive manufacturing(AM)is becoming increasingly common.Among the different types of polymer gears,nylon gears are particularly popular.However,there is currently very limited understanding of the wear resistance of nylon gears and of the impact of the manufacturing method on gear wear performance.The aims of this work are(a)to study the wear process of nylon gears made using the conventional injection molding method and two popularly used AM methods,namely,fused deposition modeling and selective laser sintering,(b)to compare and understand the wear performance by monitoring the evolution of the gear surfaces of the teeth,and(c)to study the effect of wear on the gear dynamics by analyzing gearbox vibration signals.This article presents experimental work,data analysis of the wear processes using molding and image analysis techniques,as well as the vibration data collected during gear wear tests.It also provides key results and further insights into the wear performance of the tested nylon gears.The information gained in this study is useful for better understanding the degradation process of additively manufactured nylon gears.
基金co-supported by the National Key Research and Development Project of China (No. 2018YFA0703304)the National Natural Science Foundation of China (Nos. 52125504, 92148301, 52090053)。
文摘As one of the most important terminals in machining, cutting tools have been widely used for components manufacturing in aerospace and other industries. The quality of these components and processing efficiency are closely linked to the performance of cutting tools. Therefore, it is essential and critical to inspect the cutting tools and monitor the condition during the stage of manufacturing and machining. This review aims to discuss and summarize the key problems, methods,and techniques from the perspective of the tool geometric and the physical quantities measurement,including machine vision, physical sensors and data processing. It is worth mentioning that we focus on the topic of precision measurement methods and discuss universal solutions by identifying the common characteristics of the measured quantities. Eventually, the challenges and future trends for the development of in-depth research and practical applications are concluded. The research and application of precise measurement techniques for geometric and physical quantities will better promote the development of intelligent manufacturing.
基金the Natural Sciences and Engineering Research Council of Canada(NSERC)under GR012389.
文摘Background Traditional methods for monitoring mining equipment rely primarily on visual inspections,which are time-consuming,inefficient,and hazardous.This article introduces a novel approach to monitoring mission-critical systems and services in the mining industry by integrating virtual reality(VR)and digital twin(DT)technologies.VR-based DTs enable remote equipment monitoring,advanced analysis of machine health,enhanced visualization,and improved decision making.Methods This article presents an architecture for VR-based DT development,including the developmental stages,activities,and stakeholders involved.A case study on the condition monitoring of a conveyor belt using real-time synthetic vibration sensor data was conducted using the proposed methodology.The study demonstrated the application of the methodology in remote monitoring and identified the need for further development for implementation in active mining operations.The article also discusses interdisciplinarity,choice of tools,computational resources,time and cost,human involvement,user acceptance,frequency of inspection,multiuser environment,potential risks,and applications beyond the mining industry.Results The findings of this study provide a foundation for future research in the domain of VR-based DTs for remote equipment monitoring and a novel application area for VR in mining.
基金This work has been carried out with in the DDD BATMAN project,supported by MarTERA and the Research Council of Norway(project no 311445).
文摘Battery systems are increasingly being used for powering ocean going ships,and the number of fully electric or hybrid ships relying on battery power for propulsion is growing.To ensure the safety of such ships,it is important to monitor the available energy that can be stored in the batteries,and classification societies typically require the state of health(SOH)to be verified by independent tests.This paper addresses statistical modeling of SOH for maritime lithium-ion batteries based on operational sensor data.Various methods for sensor-based,data-driven degradation monitoring will be presented,and advantages and challenges with the different approaches will be discussed.The different approaches include cumulative degradation models and snapshot models,models that need to be trained and models that need no prior training,and pure data-driven models and physics-informed models.Some of the methods only rely on measured data,such as current,voltage,and temperature,whereas others rely on derived quantities such as state of charge.Models include simple statistical models and more complicated machine learning techniques.Insight from this exploration will be important in establishing a framework for data-driven diagnostics and prognostics of maritime battery systems within the scope of classification societies.
文摘Latent variable models can effectively determine the condition of essential rotating machinery without needing labeled data.These models analyze vibration data via an unsupervised learning strategy.Temporal preservation is necessary to obtain an informative latent manifold for the fault diagnosis task.In a temporalpreserving context,two approaches exist to develop a condition-monitoring methodology:offline and online.For latent variable models,the available training modes are not different.While many traditional methods use offline training,online training can dynamically adjust the latent manifold,possibly leading to better fault signature extraction from the vibration data.This study explores online training using temporal-preserving latent variable models.Within online training,there are two main methods:one focuses on reconstructing data and the other on interpreting the data components.Both are considered to evaluate how they diagnose faults over time.Using two experimental datasets,the study confirms that models from both training modes can detect changes in machinery health and identify faults even under varying conditions.Importantly,the complementarity of offline and online models is emphasized,reassuring their versatility in fault diagnostics.Understanding the implications of the training approach and the available model formulations is crucial for further research in latent variable modelbased fault diagnostics.
文摘Lithium ion batteries are complicated distributed parameter systems that can be described preferably by partial differential equations and a field theory. To reduce the solution difficulty and the calculation amount, if a distributed parameter system is described by ordinary differential equations （ODE） during the analysis and the design of distributed parameter system, the reliability of the system description will be reduced, and the systemic errors will be introduced. Studies on working condition real-time monitoring can improve the security because the rechargeable LIBs are widely used in many electronic systems and electromechanical equipment. Single particle model （SPM） is the simplification of LIB under some approximations, and can estimate the working parameters of a LIB at the faster simulation speed. A LIB modelling algorithm based on PDEs and SPM is proposed to monitor the working condition of LIBs in real time. Although the lithium ion concentration is an unmeasurable distributed parameter in the anode of LIB, the working condition monitoring model can track the real time lithium ion concentration in the anode of LIB, and calculate the residual which is the difference between the ideal data and the measured data. A fault alarm can be triggered when the residual is beyond the preset threshold. A simulation example verifies that the effectiveness and the accuracy of the working condition real-time monitoring model of LIB based on PDEs and SPM.
文摘Reliable on line cutting tool conditioning monitoring is an essential feature of automatic machine tool and flexible manufacturing system (FMS) and computer integrated manufacturing system (CIMS). Recently artificial neural networks (ANNs) are used for this purpose in conjunction with suitable sensory systems. The present work in Norwegian University of Science and Technology (NTNU) uses back propagation neural networks (BP) and fuzzy neural networks (FNN) to process the cutting tool state data measured with force and acoustic emission (AE) sensors, and implements a valuable on line tool condition monitoring system using the ANNs. Different ANN structures are designed and investigated to estimate the tool wear state based on the fusion of acoustic emission and force signals. Finally, four case studies are introduced for the sensing and ANN processing of the tool wear states and the failures of the tool with practical experiment examples. The results indicate that a tool wear identification system can be achieved using the sensors integration with ANNs, and that ANNs provide a very effective method of implementing sensor integration for on line monitoring of tool wear states and abnormalities.
基金supported by the Fundamental Research Funds for the Central Universities(No.2024JBZX027)the National Natural Science Foundation of China(No.52375078).
文摘High-Speed Trains (HSTs) have emerged as a mainstream mode of transportation in China, owing to their exceptional safety and efficiency. Ensuring the reliable operation of HSTs is of paramount economic and societal importance. As critical rotating mechanical components of the transmission system, bearings make their fault diagnosis a topic of extensive attention. This paper provides a systematic review of image encoding-based bearing fault diagnosis methods tailored to the condition monitoring of HSTs. First, it categorizes the image encoding techniques applied in the field of bearing fault diagnosis. Then, a review of state-of-the-art studies has been presented, encompassing both monomodal image conversion and multimodal image fusion approaches. Finally, it highlights current challenges and proposes future research directions to advance intelligent fault diagnosis in HSTs, aiming to provide a valuable reference for researchers and engineers in the field of intelligent operation and maintenance.
基金Project(51975169)supported by the National Natural Science Foundation of ChinaProject(LH2022E085)supported by the Natural Science Foundation of Heilongjiang Province,China。
文摘Efficient tool condition monitoring techniques help to realize intelligent management of tool life and reduce tool usage costs.In this paper,the influence of different wear degrees of ball-end milling cutters on the texture shape of machining tool marks is investigated,and a method is proposed for predicting the wear state(including the position and degree of tool wear)of ball-end milling cutters based on entropy measurement of tool mark texture images.Firstly,data samples are prepared through wear experiments,and the change law of the tool mark texture shape with the tool wear state is analyzed.Then,a two-dimensional sample entropy algorithm is developed to quantify the texture morphology.Finally,the processing parameters and tool attitude are integrated into the prediction process to predict the wear value and wear position of the ball end milling cutter.After testing,the correlation between the predicted value and the standard value of the proposed tool condition monitoring method reaches 95.32%,and the accuracy reaches 82.73%,indicating that the proposed method meets the requirement of tool condition monitoring.
基金supported by China Yangtze Power Co.,Ltd.(No.ZSF2502001).
文摘Submodule capacitor aging poses significant challenges to the safe operation of modular multilevel converter(MMC)systems.Traditional detection methods rely predominantly on offline tests,lacking real-time evaluation capabilities.Moreover,existing online approaches require additional sampling channels,thereby increasing system complexity and costs.To address these issues,this paper proposes an online evaluation method for submodule capacitor aging based on CapAgingNet.Initially,an MMC system simulation platform is developed to examine the effects of submodule capacitor aging on system operational characteristics and to create a dataset of submodule capacitor switching states.Subsequently,the CapAgingNet model is introduced,incorporating key technical modules to enhance performance:the Deep Stem module,which extracts larger receptive fields through multiple convolution layers and mitigates the impact of data sparsity in capacitor aging on feature extraction;the efficient channel attention(ECA)module,utilizing onedimensional convolution for dynamic weighting to adjust the importance of each channel,thereby enhancing the ability of the model to process high-dimensional features in capacitor aging data;and the multiscale feature fusion(MSF)module,which integrates capacitor aging information across different scales by combining fine-grained and coarse-grained features,thus improving the capacity of the model to capture high-frequency variation characteristics.The experimental results reveal that the CapAgingNet model achieves a TOP-1 accuracy of 95.32%and a macro-averaged F1 score of 95.49%on the test set,thereby providing effective technical support for online monitoring of submodule capacitor aging.
文摘In this work, an adaptive control constraint system has been developed for computer numerical control （CNC） turning based on the feedback control and adaptive control/self-tuning control. In an adaptive controlled system, the signals from the online measurement have to be processed and fed back to the machine tool controller to adjust the cutting parameters so that the machining can be stopped once a certain threshold is crossed. The main focus of the present work is to develop a reliable adaptive control system, and the objective of the control system is to control the cutting parameters and maintain the displacement and tool flank wear under constraint valves for a particular workpiece and tool combination as per ISO standard. Using Matlab Simulink, the digital adaption of the cutting parameters for experiment has confirmed the efficiency of the adaptively controlled condition monitoring system, which is reflected in different machining processes at varying machining conditions. This work describes the state of the art of the adaptive control constraint （ACC） machining systems for turning. AIS14140 steel of 150 BHN hardness is used as the workpiece material, and carbide inserts are used as cutting tool material throughout the experiment. With the developed approach, it is possible to predict the tool condition pretty accurately, if the feed and surface roughness are measured at identical conditions. As part of the present research work, the relationship between displacement due to vibration, cutting force, flank wear, and surface roughness has been examined.
基金the National Natural Science Foundations of China(Nos.91860125,51705398)the National Key Basic Research Program of China(No.2015CB057400)the Shaanxi Province 2020 Natural Science Basic Research Plan(No.2020JQ-042).
文摘Weak feature extraction is of great importance for condition monitoring and intelligent diagnosis of aeroengine.Aimed at achieving intelligent diagnosis of aero-engine main shaft bearing,an enhanced sparsity-assisted intelligent condition monitoring method is proposed in this paper.Through analyzing the weakness of convex sparse model,i.e.the tradeoff between noise reduction and feature reconstruction,this paper proposes an enhanced-sparsity nonconvex regularized convex model based on Moreau envelope to achieve weak feature extraction.Accordingly,a sparsity-assisted deep convolutional variational autoencoders network is proposed,which achieves the intelligent identification of fault state through training denoised normal data.Finally,the effectiveness of the proposed method is verified through aero-engine bearing run-to-failure experiment.The comparison results show that the proposed method is good at abnormal pattern recognition,showing a good potential for weak fault intelligent diagnosis of aero-engine main shaft bearings.
基金The authors appreciate generous supports from Canada Natural Sciences and Engineering Research Council,McGill University Engine Centre as well as Faculty of Engineering.
文摘This article introduces a novel approach for tricone bit wear condition monitoring and failure prediction for surface mining applications.A successful bit health monitoring system is essential to achieve fully autonomous blasthole drilling.In this research in-situ vibration signals were analyzed in timefrequency domain and signals trend during tricone bit life span were investigated and introduced to support the development of artificial intelligence(AI)models.In addition to the signal statistical features,wavelet packet energy distribution proved to be a powerful indicator for bit wear assessment.Backpropagation artificial neural network(ANN)models were designed,trained and evaluated for bit state classification.Finally,an ANN architecture and feature vector were introduced to classify the bit condition and predict the bit failure.
基金supported by the UK Engineering and Physical Sciences Research Council(EPSRC)(No.EP/I037326/1)
文摘It is common for wind turbines to be installed in remote locations on land or offshore, leading to difficulties in routine inspection and maintenance. Further, wind turbines in these locations are often subject to harsh operating conditions. These challenges mean there is a requirement for a high degree of maintenance. The data generated by monitoring systems can be used to obtain models of wind turbines operating under different conditions, and hence predict output signals based on known inputs. A model-based condition monitoring system can be implemented by comparing output data obtained from operational turbines with those predicted by the models, so as to detect changes that could be due to the presence of faults. This paper discusses several techniques for model-based condition monitoring systems: linear models, artificial neural networks, and state dependent parameter "pseudo" transfer functions.The models are identified using supervisory control and data acquisition(SCADA) data acquired from an operational wind firm. It is found that the multiple-input single-output state dependent parameter method outperforms both multivariate linear and artificial neural network-based approaches. Subsequently, state dependent parameter models are used to develop adaptive thresholds for critical output signals. In order to provide an early warning of a developing fault, it is necessary to interpret the amount by which the threshold is exceeded, together with the period of time over which this occurs. In this regard, a fuzzy logic-based inference system is proposed and demonstrated to be practically feasible.
文摘Nowadays,renewable energy has been emerging as the major source of energy and is driven by its aggressive expansion and falling costs.Most of the renewable energy sources involve turbines and their operation and maintenance are vital and a difficult task.Condition monitoring and fault diagnosis have seen remarkable and revolutionary up-gradation in approaches,practices and technology during the last decade.Turbines mostly do use a rotating type of machinery and analysis of those signals has been challenging to localize the defect.This paper proposes a new hybrid model wherein multiple swarm intelligence models have been evaluated to optimize the conventional Long Short-Term Memory(LSTM)model in classifying the faults from the vibration signals data acquired from the gearbox.This helps to analyze the performance and behavioral patterns of the system more effectively and efficiently which helps to suggest for replacement of the unit with higher precision.The results have demonstrated that the proposed hybrid modeling approach is effective in classifying the faults of the gearbox from the time series data and achieve higher diagnostic accuracy in comparison to the conventional LSTM methods.
基金supports from Canada Natural Sciences and Engineering Research Council(NSERC-CRD grant#461514,NSERC-I2I grant#516232)McGill University Engine Centre+2 种基金Teck ResourcesArcelorMittalRotacan companies。
文摘This paper presents a novel approach to investigate the relations between drilling signals and bit wear condition in real world full-scale mining operations.This research addresses the increasing demand for automation in mining to increase the efficiency,safety,and ability to work in harsh environments.A crucial issue in fully autonomous unmanned drilling is to have a system to detect the bit wear condition through the drilling signals analysis in real time.In this work,based on extensive field studies,a novel qualitative method for tricone bit wear state classification is developed and introduced.The relations between drilling vibration as well as electric motor current signals and bit wear are investigated and bit failure vibration frequencies,regardless of the geological conditions,are introduced.Bit failure frequencies are experimentally investigated and analytically calculated.Finally,the effect of bit design parameters on the failure frequencies is presented for the application of bit wear condition monitoring and bit failure prediction.
基金supported by National Natural Science Foundation of China (Grant No. 50675219)Hu’nan Provincial Science Committee Excellent Youth Foundation of China (Grant No. 08JJ1008)
文摘Turbopump condition monitoring is a significant approach to ensure the safety of liquid rocket engine （LRE）.Because of lack of fault samples,a monitoring system cannot be trained on all possible condition patterns.Thus it is important to differentiate abnormal or unknown patterns from normal pattern with novelty detection methods.One-class support vector machine （OCSVM） that has been commonly used for novelty detection cannot deal well with large scale samples.In order to model the normal pattern of the turbopump with OCSVM and so as to monitor the condition of the turbopump,a monitoring method that integrates OCSVM with incremental clustering is presented.In this method,the incremental clustering is used for sample reduction by extracting representative vectors from a large training set.The representative vectors are supposed to distribute uniformly in the object region and fulfill the region.And training OCSVM on these representative vectors yields a novelty detector.By applying this method to the analysis of the turbopump＇s historical test data,it shows that the incremental clustering algorithm can extract 91 representative points from more than 36 000 training vectors,and the OCSVM detector trained on these 91 representative points can recognize spikes in vibration signals caused by different abnormal events such as vane shedding,rub-impact and sensor faults.This monitoring method does not need fault samples during training as classical recognition methods.The method resolves the learning problem of large samples and is an alternative method for condition monitoring of the LRE turbopump.
文摘Various technologies have recently been developed for high-speed railways, in order to boost commercial speeds from 300 km.h： to 400 km.h-1. Among these technologies, this paper introduces the 400 km-h-1 class current collection performance evaluation methods that have been developed and demonstrated by Korea. Specifically, this paper reports details of the video-based monitoring techniques that have been adopted to inspect the stability of overhead contact line （OCL） components at 400 km.h-1 without direct contact with any components of the power supply system. Unlike conventional OCL monitoring systems, which detect contact wire positions using either laser sensors or line cameras, the developed system measures parameters in the active state by video data. According to experimental results that were obtained at a field-test site established at a commercial line, it is claimed that the proposed mea- surement system is capable of effectively measuring OCL parameters.
基金This work was supported in part by the National Natural Science Foundation of China(U1808217)the National Science Fund for Distinguished Young Scholars(52125504)+1 种基金the Highlevel Personnel Innovation Support Program of Dalian(2017RJ04)the Liao Ning Revitalization Talents Program(XLYC1807086 and XLYC1801008).
文摘Three-dimensional(3D)microdisplacement monitoring plays a crucial role in the assembly of large aircraft.This paper presents a broadly applicable high-precision online 3D microdisplacement monitoring method and system based on proximity sensors as well as a corresponding in situ calibration method,which can be applied under various extreme working conditions encountered in the aircraft assembly process,such as compact and obstructed spaces.A 3D monitoring model is first established to achieve 3D microdisplacement monitoring based only on the one-dimensional distances measured by proximity sensors,which concerns the extrinsic sensor parameters,such as the probe base point(PBP)and the unit displacement vector(UDV).Then,a calibration method is employed to obtain these extrinsic parameters with high precision by combining spatial transformation principles and weighted optimization.Finally,calibration and monitoring experiments performed for a tailplane assembly process are reported.The calibration precision for the PBP is better than±10 lm in the X and Y directions and±2 lm in the Z direction,and the calibration precision for the UDV is better than 0.07°.Moreover,the accuracy of the 3D microdisplacement monitoring system can reach±15 lm.In general,this paper provides new insights into the modeling and calibration of 3D microdisplacement monitoring based on proximity sensors and a precise,efficient,and low-cost technical means for performing related measurements in compact spaces during the aircraft assembly process.
