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
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
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
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
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
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
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
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
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
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
In order to online monitor the running state of variable voltage and variable frequency(VVVF)hydraulic system,this paper presents a graphic monitoring method that fuses the information of variable frequency electric p...In order to online monitor the running state of variable voltage and variable frequency(VVVF)hydraulic system,this paper presents a graphic monitoring method that fuses the information of variable frequency electric parameters.This paper first analyzes how the voltage and current of the motor stator change with the operation conditions of VVVF hydraulic system.As a result,we draw the relationship between the electric parameters(voltage and current)and power frequency.Then,the signals of the voltage and current are fused as dynamic figures based on the idea of Lissajous figures,and the values of the electric parameters are related to the features of the dynamic figures.Rigorous theoretical analysis establishes the function between the electric power of the variable frequency motor(VFM)and the features of the plotted dynamic figures including area of diagram,area of bounding rectangle,tilt angle,etc.Finally,the effectiveness of the proposed method is verified by two cases,in which the speed of VFM and the load of VVVF hydraulic system are changed.The results show that the increase of the speed of VFM enhances its three-phase electric power,but reduces the tilt angle of the plotted dynamic figures.In addition,as the load of VVVF hydraulic system is increased,the three-phase electric power of VFM and the tilt angle of the plotted dynamic figures are both increased.This paper provides a new way to online monitor the running state of VVVF hydraulic system.展开更多
The gripper cylinder that provides braced force for Tunnel Boring Machine （TBM） might fail due to severe vibration when the TBM excavates in the tunnel. Early fault diagnosis of the gripper cylinder is important for...The gripper cylinder that provides braced force for Tunnel Boring Machine （TBM） might fail due to severe vibration when the TBM excavates in the tunnel. Early fault diagnosis of the gripper cylinder is important for the safety and efficiency of the whole tunneling project. In this paper, an online condition monitoring system based on the Empirical Mode Decomposition （EMD） method is estab- lished for fault diagnosis of the gripper cylinder while TBM is working. Firstly, the lumped mass parameter model of the gripper cylinder is established considering the influence of the variable stiffness at the rock interface, the equivalent stiffness of the oil, the seals, and the copper guide sleeve. The dynamic performance of the gripper cylinder is investigated to provide basis for its health condition evaluation. Then, the EMD method is applied to identify the characteristic frequencies of the gripper cylinder for fault diagnosis and a field test is used to verify the accuracy of the EMD method for detection of the characteristic frequencies. Furthermore, the contact stiff- ness at the interface between the barrel and the rod is calculated with Hertz theory and the relationship between the natural frequency and the stiffness varying with the health condition of the cylinder is simulated based on the dynamic model. The simulation shows that the character- istic frequencies decrease with the increasing clearance between the barrel and the rod, thus the defects could be indicated by monitoring the natural frequency. Finally, a health condition management system of the gripper cylin- der based on the vibration signal and the EMD method is established, which could ensure the safety of TBM.展开更多
The application of bio-inspired computational techniques to the field of condition monitoring is addressed. First, the bio-inspired computational techniques are briefly addressed; the advantages and disadvantages of t...The application of bio-inspired computational techniques to the field of condition monitoring is addressed. First, the bio-inspired computational techniques are briefly addressed; the advantages and disadvantages of these computational methods are made clear. Then, the roles of condition monitoring in the predictive maintenance and failures prediction and the development trends of condition monitoring are discussed. Finally, a case study on the condition monitoring of grinding machine is described, which shows the application of bio-inspired computational technique to a practical condition monitoring system.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.
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.
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.
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.
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.
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.
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.
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.
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.
基金National Natural Science Foundation of China(No.51675399)
文摘In order to online monitor the running state of variable voltage and variable frequency(VVVF)hydraulic system,this paper presents a graphic monitoring method that fuses the information of variable frequency electric parameters.This paper first analyzes how the voltage and current of the motor stator change with the operation conditions of VVVF hydraulic system.As a result,we draw the relationship between the electric parameters(voltage and current)and power frequency.Then,the signals of the voltage and current are fused as dynamic figures based on the idea of Lissajous figures,and the values of the electric parameters are related to the features of the dynamic figures.Rigorous theoretical analysis establishes the function between the electric power of the variable frequency motor(VFM)and the features of the plotted dynamic figures including area of diagram,area of bounding rectangle,tilt angle,etc.Finally,the effectiveness of the proposed method is verified by two cases,in which the speed of VFM and the load of VVVF hydraulic system are changed.The results show that the increase of the speed of VFM enhances its three-phase electric power,but reduces the tilt angle of the plotted dynamic figures.In addition,as the load of VVVF hydraulic system is increased,the three-phase electric power of VFM and the tilt angle of the plotted dynamic figures are both increased.This paper provides a new way to online monitor the running state of VVVF hydraulic system.
基金Supported by National Basic Research Program of China(Grant No.2013CB035403)National Natural Science Foundation of China(Grant No.51375297)Program of Shanghai Subject Chief Scientist of China(Grant No.14XD1402000)
文摘The gripper cylinder that provides braced force for Tunnel Boring Machine （TBM） might fail due to severe vibration when the TBM excavates in the tunnel. Early fault diagnosis of the gripper cylinder is important for the safety and efficiency of the whole tunneling project. In this paper, an online condition monitoring system based on the Empirical Mode Decomposition （EMD） method is estab- lished for fault diagnosis of the gripper cylinder while TBM is working. Firstly, the lumped mass parameter model of the gripper cylinder is established considering the influence of the variable stiffness at the rock interface, the equivalent stiffness of the oil, the seals, and the copper guide sleeve. The dynamic performance of the gripper cylinder is investigated to provide basis for its health condition evaluation. Then, the EMD method is applied to identify the characteristic frequencies of the gripper cylinder for fault diagnosis and a field test is used to verify the accuracy of the EMD method for detection of the characteristic frequencies. Furthermore, the contact stiff- ness at the interface between the barrel and the rod is calculated with Hertz theory and the relationship between the natural frequency and the stiffness varying with the health condition of the cylinder is simulated based on the dynamic model. The simulation shows that the character- istic frequencies decrease with the increasing clearance between the barrel and the rod, thus the defects could be indicated by monitoring the natural frequency. Finally, a health condition management system of the gripper cylin- der based on the vibration signal and the EMD method is established, which could ensure the safety of TBM.
基金supported by the National Natural Science Foundation of China ( No. 61025019No. 90820016)+1 种基金Program for New Century Excellent Talents in University ( No. NECT-07-0735)Natural Science Foundation of Hebei ( No. F2009001638)
文摘The application of bio-inspired computational techniques to the field of condition monitoring is addressed. First, the bio-inspired computational techniques are briefly addressed; the advantages and disadvantages of these computational methods are made clear. Then, the roles of condition monitoring in the predictive maintenance and failures prediction and the development trends of condition monitoring are discussed. Finally, a case study on the condition monitoring of grinding machine is described, which shows the application of bio-inspired computational technique to a practical condition monitoring system.
