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
The development and application of new reliability models and methods are presented to analyze the system relia-bility of complex condition monitoring systems.The methods include a method analyzing failure modes of a ...The development and application of new reliability models and methods are presented to analyze the system relia-bility of complex condition monitoring systems.The methods include a method analyzing failure modes of a type of redundant con-dition monitoring systems(RCMS)by invoking failure tree model,Markov modeling techniques for analyzing system reliability of RCMS,and methods for estimating Markov model parameters.Furthermore,a computing case is investigated and many conclu-sions upon this case are summarized.Results show that the method proposed here is practical and valuable for designing condition monitoring systems and their maintenance.展开更多
It is an urgent project to realize online and overall condition monitoring and timely fault diagnosis for large-scale mobile and complex equipment. Moreover, most of the existing large-scale complex equipment has quit...It is an urgent project to realize online and overall condition monitoring and timely fault diagnosis for large-scale mobile and complex equipment. Moreover, most of the existing large-scale complex equipment has quite insufficient accessibility of examination, although it still has quite a long service life. The decentralized and overall condition monitoring, as a new concept, is proposed from the point of view of the whole system. A set of complex equipment is divided into several parts in terms of concrete equipment. Every part is processed via one detecting unit, and the main detecting unit is connected with other units. The management work and communications with the remote monitoring center have been taken on by it. Consequently, the difficulty of realizing a condition monitoring system and the complexity of processing information is reduced greatly. Furthermore, excellent maintainability of the condition monitoring system is obtained because of the modularization design. Through an application example, the design and realization of the decentralized and overall condition monitoring system is introduced specifically. Some advanced technologies, such as, micro control unit （MCU）, advanced RISC machines （ARM）, and control area network （CAN）, have been adopted in the system. The system＇s applicability for the existing large-scale mobile and complex equipment is tested.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
This article introduces a portable wind turbine condition monitoring system(CMS)and its applications in wind turbine drivetrain damage detection.The portable CMS based on vibration detection and analysis has a long ap...This article introduces a portable wind turbine condition monitoring system(CMS)and its applications in wind turbine drivetrain damage detection.The portable CMS based on vibration detection and analysis has a long application history in conventional rotating machineries,but it is not widely used in wind turbines.There are several reasons why it is not used,including the labor-and knowledge-intensive requirements for test setup and result interpretation.There are also reasons specific to wind turbines,such as the structural diversity of drivetrains,the uncertainty of operational conditions,and the complexity of the damage mechanism of different parts that make the conventional vibration-based CMS inefficient and not cost-effective.All these factors affect the wide application of the portable system.The portable wind turbine CMS discussed in this article is integrated using advanced vibration measurement and analysis methodology.Fault detection for the acquired acceleration response and high-speed shaft speed signal is carried out by a suite of data analysis techniques specifically designed for a wind turbine gearbox.Using these techniques,damage detection accuracy for all the components inside a gearbox is improved significantly,especially for those related to medium-and low-speed shafts.The new data processing techniques also are briefly described with the developed methodologies verified by three wind turbines with typical low-speed shaft-related component damages.These damage assessments include the low-and medium-speed planetary stage ring gear,the low-speed planetary stage planet gear and damage to the main bearing.展开更多
Poor road conditions in underground mine tunnels can lead to decreased production efficiency and increased wear on production vehicles. A prototype system for road condition monitoring is presented in this paper to co...Poor road conditions in underground mine tunnels can lead to decreased production efficiency and increased wear on production vehicles. A prototype system for road condition monitoring is presented in this paper to counteract this. The system consists of three components i.e. localization, road monitoring, and scheduling. The localization of vehicles is performed using a Rao-Blackwellized extended particle filter, combining vehicle mounted sensors with signal strengths of Wi Fi access points. Two methods for road monitoring are described: a Kalman filter used together with a model of the vehicle suspension system, and a relative condition measure based on the power spectral density. Lastly, a method for taking automatic action on an ill-conditioned road segment is proposed in the form of a rescheduling algorithm.The scheduling algorithm is based on the large neighborhood search and is used to integrate road service activities in the short-term production schedule while minimizing introduced production disturbances.The system is demonstrated on experimental data collected in a Swedish underground mine.展开更多
This paper presents a replacement strategy for an aged transformer based on condition monitoring and system risk.The condition monitoring measurements are used to estimate the insulation age of the transformer.The una...This paper presents a replacement strategy for an aged transformer based on condition monitoring and system risk.The condition monitoring measurements are used to estimate the insulation age of the transformer.The unavailability because of transformer aging failure is calculated using the insulation age instead of the traditional natural age.The system risk is assessed using the total unavailability from both aging and repairable failures.The expected damage cost and benefit due to delaying the replacement of the transformer are evaluated.The best replacement timing is determined as the year when the replacement of the transformer reaches the maximum net benefit.A case study for two aged transformers in a utility system demonstrates the effectiveness of the proposed method.展开更多
An integrated monitoring system for running parameters of key mining equipmenton the basis of condition monitoring technology and modern communication networktechnology was developed.The system consists of a client co...An integrated monitoring system for running parameters of key mining equipmenton the basis of condition monitoring technology and modern communication networktechnology was developed.The system consists of a client computer with functions ofsignal acquisition and processing, and a host computer in the central control room.Thesignal acquisition module of the client computer can collect the running parameters fromvarious monitoring terminals in real-time.The DSP high-speed data processing system ofthe main control module can quickly achieve the numerical calculation for the collectedsignal.The signal modulation and signal demodulation are completed by the frequencyshift keying circuit and phase-locked loop frequency circuit, respectively.Finally, the signalis sent to the host computer for logic estimation and diagnostic analysis using the networkcommunication technology, which is helpful for technicians and managers to control therunning state of equipment.展开更多
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
Machinery condition monitoring is beneficial to equipment maintenance and has been receiving much attention from academia and industry.Machine learning,especially deep learning,has become popular for machinery conditi...Machinery condition monitoring is beneficial to equipment maintenance and has been receiving much attention from academia and industry.Machine learning,especially deep learning,has become popular for machinery condition monitoring because that can fully use available data and computational power.Since significant accidents might be caused if wrong fault alarms are given for machine condition monitoring,interpretable machine learning models,integrate signal processing knowledge to enhance trustworthiness of models,are gradually becoming a research hotspot.A previous spectrum-based and interpretable optimized weights method has been proposed to indicate faulty and fundamental frequencies when the analyzed data only contains a healthy type and a fault type.Considering that multiclass fault types are naturally met in practice,this work aims to explore the interpretable optimized weights method for multiclass fault type scenarios.Therefore,a new multiclass optimized weights spectrum(OWS)is proposed and further studied theoretically and numerically.It is found that the multiclass OWS is capable of capturing the characteristic components associated with different conditions and clearly indicating specific fault characteristic frequencies(FCFs)corresponding to each fault condition.This work can provide new insights into spectrum-based fault classification models,and the new multiclass OWS also shows great potential for practical applications.展开更多
This paper investigates the use of explainable artificial intelligence(XAI)and trustworthy artificial intelligence(TAI)methods for condition monitoring on a laser cutting machine.The focus is on the analysis of the ra...This paper investigates the use of explainable artificial intelligence(XAI)and trustworthy artificial intelligence(TAI)methods for condition monitoring on a laser cutting machine.The focus is on the analysis of the rack and pinion contact with wear being predicted by four differently derived adaptive-network-based fuzzy inference system(s)(ANFIS)models.Using both model-agnostic and model-specific parameters integrated in a weighted evaluation framework,the models are evaluated with respect to the effectiveness of explanations.This framework is based on the observation of the outputs of the individual layers of ANFIS,also focusing on aspects of two multivalued logics,namely fuzzy logic and support logic.The results show that the introduced weighted evaluation framework makes it possible to quantify the explainability of the individual models in terms of XAI and TAI.Finally,a preselection of a model for predicting the wear of the rack and pinion contact can be made.展开更多
Increasing renewable energy targets globally has raised the requirement for the efficient and profitable operation of solar photovoltaic(PV)systems.In light of this requirement,this paper provides a path for evaluatin...Increasing renewable energy targets globally has raised the requirement for the efficient and profitable operation of solar photovoltaic(PV)systems.In light of this requirement,this paper provides a path for evaluating the operating condition and improving the power output of the PV system in a grid integrated environment.To achieve this,different types of faults in grid-connected PV systems(GCPVs)and their impact on the energy loss associated with the electrical network are analyzed.A data-driven approach using neural networks(NNs)is proposed to achieve root cause analysis and localize the fault to the component level in the system.The localized fault condition is combined with a parallel operation of adaptive neurofuzzy inference units(ANFIUs)to develop a power mismatch-based control unit(PMCU)for improving the power output of the GCPV.To develop the proposed framework,a 10-kW single-phase GCPV is simulated for training the NN-based anomaly detection approach with 14 deviation signals.Further,the developed algorithm is combined with the PMCU implemented with the experimental setup of GCPV.The results identified 98.2%training accuracy and 43000 observations/sec prediction speed for the trained classifier,and improved power output with reduced voltage and current harmonics for the grid-connected PV operation.展开更多
A web based condition monitoring and fault diagnosis system (CMAFDS) for the F2 finishing mill of the 2050 Hot Strip Mill was developed at a steel works. The features of the condition monitoring and fault diagnosis s...A web based condition monitoring and fault diagnosis system (CMAFDS) for the F2 finishing mill of the 2050 Hot Strip Mill was developed at a steel works. The features of the condition monitoring and fault diagnosis system based on the Web are analyzed in this paper. This paper also describes the main frame of the hardware and the software in the system and emphatically points out the function of the database management system(DBMS) based on the Web. It is proved that the web based CMAFDS is practical in technology and much superior to the CMAFDS based on other network technology in functions.展开更多
This paper describes the development of the condition monitoring and faultdiagnosing system of a group of rotating machinery. The data management is performed by means ofdouble redundant data bases stored simultaneous...This paper describes the development of the condition monitoring and faultdiagnosing system of a group of rotating machinery. The data management is performed by means ofdouble redundant data bases stored simultaneously in both the analyzing server and monitoringclient. In this way, high reliability of the storage of data is guaranteed. Condensation of trenddata releases much space resource of the hard disk. Diagnosing strategies orientated to differenttypical faults of rotating machinery are developed and incorporated into the system. Experimentalverification shows that the system is suitable and effective for condition monitoring and faultdiagnosing for a rotating machine group.展开更多
An intelligent machine is the earnest aspiration of people. From the point of view to construct an intelligent machine with self-monitoring and self-diagnosis abilities, the technology for realizing an internet orient...An intelligent machine is the earnest aspiration of people. From the point of view to construct an intelligent machine with self-monitoring and self-diagnosis abilities, the technology for realizing an internet oriented embedded intelligent condition monitoring and fault diagnosis system for the rotating machine with remote monitoring, diagnosis, maintenance and upgrading functions is introduced systematically. Based on the DSP （ Digital Signal Processor） and embedded microcomputer, the system can measure and store the machine work status in real time, such as the rotating speed and vibration, etc. In the system, the DSP chip is used to do the fault signal processing and feature extraction, and the embedded microcomputer with a customized Linux operation system is used to realize the internet oriented remote software upgrading and system maintenance. Embedded fault diagnosis software based on mobile agent technology is also designed in the system, which can interconnect with the remote fault diagnosis center to realize the collaborative diagnosis. The embedded condition monitoring and fault diagnosis technology proposed in this paper will effectively improve the intelligence degree of the fault diagnosis system.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
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
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
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This research is primarily founded by the projects of the National Science Foundation of China(No.50575087,No.50335020)
文摘The development and application of new reliability models and methods are presented to analyze the system relia-bility of complex condition monitoring systems.The methods include a method analyzing failure modes of a type of redundant con-dition monitoring systems(RCMS)by invoking failure tree model,Markov modeling techniques for analyzing system reliability of RCMS,and methods for estimating Markov model parameters.Furthermore,a computing case is investigated and many conclu-sions upon this case are summarized.Results show that the method proposed here is practical and valuable for designing condition monitoring systems and their maintenance.
基金This project was supported by the Hebei Provincial Nature Science Foundation (E20070011048).
文摘It is an urgent project to realize online and overall condition monitoring and timely fault diagnosis for large-scale mobile and complex equipment. Moreover, most of the existing large-scale complex equipment has quite insufficient accessibility of examination, although it still has quite a long service life. The decentralized and overall condition monitoring, as a new concept, is proposed from the point of view of the whole system. A set of complex equipment is divided into several parts in terms of concrete equipment. Every part is processed via one detecting unit, and the main detecting unit is connected with other units. The management work and communications with the remote monitoring center have been taken on by it. Consequently, the difficulty of realizing a condition monitoring system and the complexity of processing information is reduced greatly. Furthermore, excellent maintainability of the condition monitoring system is obtained because of the modularization design. Through an application example, the design and realization of the decentralized and overall condition monitoring system is introduced specifically. Some advanced technologies, such as, micro control unit （MCU）, advanced RISC machines （ARM）, and control area network （CAN）, have been adopted in the system. The system＇s applicability for the existing large-scale mobile and complex equipment is tested.
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This article introduces a portable wind turbine condition monitoring system(CMS)and its applications in wind turbine drivetrain damage detection.The portable CMS based on vibration detection and analysis has a long application history in conventional rotating machineries,but it is not widely used in wind turbines.There are several reasons why it is not used,including the labor-and knowledge-intensive requirements for test setup and result interpretation.There are also reasons specific to wind turbines,such as the structural diversity of drivetrains,the uncertainty of operational conditions,and the complexity of the damage mechanism of different parts that make the conventional vibration-based CMS inefficient and not cost-effective.All these factors affect the wide application of the portable system.The portable wind turbine CMS discussed in this article is integrated using advanced vibration measurement and analysis methodology.Fault detection for the acquired acceleration response and high-speed shaft speed signal is carried out by a suite of data analysis techniques specifically designed for a wind turbine gearbox.Using these techniques,damage detection accuracy for all the components inside a gearbox is improved significantly,especially for those related to medium-and low-speed shafts.The new data processing techniques also are briefly described with the developed methodologies verified by three wind turbines with typical low-speed shaft-related component damages.These damage assessments include the low-and medium-speed planetary stage ring gear,the low-speed planetary stage planet gear and damage to the main bearing.
基金partially supported by the Wallenberg AIAutonomous Systems and Software Program (WASP) funded by the Knut and Alice Wallenberg Foundation
文摘Poor road conditions in underground mine tunnels can lead to decreased production efficiency and increased wear on production vehicles. A prototype system for road condition monitoring is presented in this paper to counteract this. The system consists of three components i.e. localization, road monitoring, and scheduling. The localization of vehicles is performed using a Rao-Blackwellized extended particle filter, combining vehicle mounted sensors with signal strengths of Wi Fi access points. Two methods for road monitoring are described: a Kalman filter used together with a model of the vehicle suspension system, and a relative condition measure based on the power spectral density. Lastly, a method for taking automatic action on an ill-conditioned road segment is proposed in the form of a rescheduling algorithm.The scheduling algorithm is based on the large neighborhood search and is used to integrate road service activities in the short-term production schedule while minimizing introduced production disturbances.The system is demonstrated on experimental data collected in a Swedish underground mine.
文摘This paper presents a replacement strategy for an aged transformer based on condition monitoring and system risk.The condition monitoring measurements are used to estimate the insulation age of the transformer.The unavailability because of transformer aging failure is calculated using the insulation age instead of the traditional natural age.The system risk is assessed using the total unavailability from both aging and repairable failures.The expected damage cost and benefit due to delaying the replacement of the transformer are evaluated.The best replacement timing is determined as the year when the replacement of the transformer reaches the maximum net benefit.A case study for two aged transformers in a utility system demonstrates the effectiveness of the proposed method.
基金Supported by the National Hi-tech Research and Development Program of China(2007AA04Z415)the Hunan Province and Xiangtan City Natural Science Joint Foundation(09JJ8005)the Torch Program Project of Hunan Province(2008SH044)
文摘An integrated monitoring system for running parameters of key mining equipmenton the basis of condition monitoring technology and modern communication networktechnology was developed.The system consists of a client computer with functions ofsignal acquisition and processing, and a host computer in the central control room.Thesignal acquisition module of the client computer can collect the running parameters fromvarious monitoring terminals in real-time.The DSP high-speed data processing system ofthe main control module can quickly achieve the numerical calculation for the collectedsignal.The signal modulation and signal demodulation are completed by the frequencyshift keying circuit and phase-locked loop frequency circuit, respectively.Finally, the signalis sent to the host computer for logic estimation and diagnostic analysis using the networkcommunication technology, which is helpful for technicians and managers to control therunning state of equipment.
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.
基金supported by the National Natural Science Foundation of China under Grant Nos.523B2043 and 52475112.
文摘Machinery condition monitoring is beneficial to equipment maintenance and has been receiving much attention from academia and industry.Machine learning,especially deep learning,has become popular for machinery condition monitoring because that can fully use available data and computational power.Since significant accidents might be caused if wrong fault alarms are given for machine condition monitoring,interpretable machine learning models,integrate signal processing knowledge to enhance trustworthiness of models,are gradually becoming a research hotspot.A previous spectrum-based and interpretable optimized weights method has been proposed to indicate faulty and fundamental frequencies when the analyzed data only contains a healthy type and a fault type.Considering that multiclass fault types are naturally met in practice,this work aims to explore the interpretable optimized weights method for multiclass fault type scenarios.Therefore,a new multiclass optimized weights spectrum(OWS)is proposed and further studied theoretically and numerically.It is found that the multiclass OWS is capable of capturing the characteristic components associated with different conditions and clearly indicating specific fault characteristic frequencies(FCFs)corresponding to each fault condition.This work can provide new insights into spectrum-based fault classification models,and the new multiclass OWS also shows great potential for practical applications.
基金This project(ProKInect N°02P20A090)was funded by the German Federal Ministry of Education and Research(BMBF)within the“The Future of Value Creation-Research on Production,Services and Work”program and managed by the Project Management Agency Karlsruhe(PTKA).The support is greatly acknowledged.
文摘This paper investigates the use of explainable artificial intelligence(XAI)and trustworthy artificial intelligence(TAI)methods for condition monitoring on a laser cutting machine.The focus is on the analysis of the rack and pinion contact with wear being predicted by four differently derived adaptive-network-based fuzzy inference system(s)(ANFIS)models.Using both model-agnostic and model-specific parameters integrated in a weighted evaluation framework,the models are evaluated with respect to the effectiveness of explanations.This framework is based on the observation of the outputs of the individual layers of ANFIS,also focusing on aspects of two multivalued logics,namely fuzzy logic and support logic.The results show that the introduced weighted evaluation framework makes it possible to quantify the explainability of the individual models in terms of XAI and TAI.Finally,a preselection of a model for predicting the wear of the rack and pinion contact can be made.
基金Funding for this study was received from the Deputyship for Research&Innovation,Ministry of Education in Saudi Arabia through the project number“IFPHI-021–135–2020”and King Abdulaziz University,DSR,Jeddah,Saudi Arabia.
文摘Increasing renewable energy targets globally has raised the requirement for the efficient and profitable operation of solar photovoltaic(PV)systems.In light of this requirement,this paper provides a path for evaluating the operating condition and improving the power output of the PV system in a grid integrated environment.To achieve this,different types of faults in grid-connected PV systems(GCPVs)and their impact on the energy loss associated with the electrical network are analyzed.A data-driven approach using neural networks(NNs)is proposed to achieve root cause analysis and localize the fault to the component level in the system.The localized fault condition is combined with a parallel operation of adaptive neurofuzzy inference units(ANFIUs)to develop a power mismatch-based control unit(PMCU)for improving the power output of the GCPV.To develop the proposed framework,a 10-kW single-phase GCPV is simulated for training the NN-based anomaly detection approach with 14 deviation signals.Further,the developed algorithm is combined with the PMCU implemented with the experimental setup of GCPV.The results identified 98.2%training accuracy and 43000 observations/sec prediction speed for the trained classifier,and improved power output with reduced voltage and current harmonics for the grid-connected PV operation.
基金theNationalKeyProjectPlanofChina (GrantNo .PD95 2 190 8)National"973"Project of China (GrantNo .G19880 2 0 32 0 )
文摘A web based condition monitoring and fault diagnosis system (CMAFDS) for the F2 finishing mill of the 2050 Hot Strip Mill was developed at a steel works. The features of the condition monitoring and fault diagnosis system based on the Web are analyzed in this paper. This paper also describes the main frame of the hardware and the software in the system and emphatically points out the function of the database management system(DBMS) based on the Web. It is proved that the web based CMAFDS is practical in technology and much superior to the CMAFDS based on other network technology in functions.
文摘This paper describes the development of the condition monitoring and faultdiagnosing system of a group of rotating machinery. The data management is performed by means ofdouble redundant data bases stored simultaneously in both the analyzing server and monitoringclient. In this way, high reliability of the storage of data is guaranteed. Condensation of trenddata releases much space resource of the hard disk. Diagnosing strategies orientated to differenttypical faults of rotating machinery are developed and incorporated into the system. Experimentalverification shows that the system is suitable and effective for condition monitoring and faultdiagnosing for a rotating machine group.
文摘An intelligent machine is the earnest aspiration of people. From the point of view to construct an intelligent machine with self-monitoring and self-diagnosis abilities, the technology for realizing an internet oriented embedded intelligent condition monitoring and fault diagnosis system for the rotating machine with remote monitoring, diagnosis, maintenance and upgrading functions is introduced systematically. Based on the DSP （ Digital Signal Processor） and embedded microcomputer, the system can measure and store the machine work status in real time, such as the rotating speed and vibration, etc. In the system, the DSP chip is used to do the fault signal processing and feature extraction, and the embedded microcomputer with a customized Linux operation system is used to realize the internet oriented remote software upgrading and system maintenance. Embedded fault diagnosis software based on mobile agent technology is also designed in the system, which can interconnect with the remote fault diagnosis center to realize the collaborative diagnosis. The embedded condition monitoring and fault diagnosis technology proposed in this paper will effectively improve the intelligence degree of the fault diagnosis system.
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.
