- Email: [email protected]
Recent progress on electro-mechanical system prognostics and health management
Microelectronics Reliability 75 (2017) 195–196
Contents lists available at ScienceDirect
Microelectronics Reliability journal homepage: www.elsevier.com/locate/microrel
Editorial
Recent progress on electro-mechanical system prognostics and health management
As complexity and intelligence of electromechanical system continuously improve, probability of failure gradually increases and maintenance and security costs are getting higher, correspondingly. Failure of electromechanical system may lead to huge economic losses and adverse social effects, and even catastrophic accidents. Therefore, prognostics and health management (PHM) with efficient prognostic technology has become highly desirable and a very important research area as a result of need to provide electromechanical system with system level health management. However, operating environment of electromechanical system is usually complicated, and uncertainty associate with loading conditions further imposes difficulty in PHM implementation. The motivation of this special issue stems from the growing interest to develop reliability and prognosis solutions in electromechanical systems. Fifteen research articles were selected from PHM-2016 Chengdu, which cover several topics of electromechanical system health management and prognostics. Each article was blindly reviewed by normally three reviewers consisting of guest editor and external reviewers. The first paper in this special issue is IETM Centered Intelligent Maintenance System Integrating Fuzzy Semantic Inference and Data Fusion, by G. Niu, and H. Li. This work presents a novel interactive electronic technical manual (IETM) centered intelligent maintenance system, which integrates diagnosis strategies of experience-based manual interpretation, rule-based fuzzy semantic inference and conditionbased data fusion. The proposed method was evaluated by two experiments of fault diagnosis for electric multiple units (EMU) trains. Experiment results show that intelligent, convenient, accurate and flexible diagnosis advantages can be obtained, which are beneficial to maintenance implementation. The second paper in this special issue is Health Assessment and Management of Wind Turbine Blade Based on the Fatigue Test Data, by X. Bai, Z. An, Y. Hou, and Q. Ma. This work defines a health degree indicator by the Grey Relation (GR) model, which can be used to quantitatively assess wind turbine blade health condition. The availability, reliability and artificial test result are taken as three indexes of the health degree. As a result, an approach and a certain theoretical guidance for the health assessment and management of wind turbine blade are established. The third paper in this special issue is A Deep Learning-Based Recognition Method for Degradation Monitoring of Ball Screw with MultiSensor Data Fusion, by L. Zhang, H. Gao,J. Wen, S. Li, and Q. Liu. This work proposes an intelligent ball screw degradation recognition method based on deep belief networks (DBN) and multi-sensor data fusion. A
http://dx.doi.org/10.1016/j.microrel.2017.07.089 0026-2714/© 2017 Published by Elsevier Ltd.
case study using dataset collected from the degradation test of ball screw is conducted to validate the proposed method. The fourth paper in this special issue is Condition Multi-classification and Evaluation of System Degradation Process Using An Improved Support Vector Machine, by Q. Miao, X. Zhang, Z. Liu, and H. Zhang. The purpose of this work is to realize multi-state condition classification of system degradation process, and an improved support vector machine with a new voting scheme is proposed. Lifetime experimental data collected from a cooling fan bearing accelerated test is used to validate the method, and the comparison study shows that the proposed method improves the classification accuracy. The fifth paper in this special issue is Coupling Damage and Reliability Modeling for Creep and Fatigue of Solder Joint by Y. Chen, Y. Jin, and R. Kang. This work focuses on two dominant failure modes of the solder joint, including the low-cycle fatigue due to temperature cycling and the creep brought by continuous high temperature. A coupling damage model considering both low-cycle fatigue and creep is established. A case study of a lead-free solder joint is conducted to validate the proposed method. The sixth paper in this special issue is Multi-frequency Weak Signal Detection Based on Multi-segment Cascaded Stochastic Resonance for Rolling Bearings, by W. Guo, Z. Zhou, C. Chen, and X. Li. This work proposes an improved mechanism for the stochastic resonance (SR), called multi-segment cascaded stochastic resonance (MS-CSR), so as to solve the problem of noise impact on incipient fault detection. In the case study section, simulated data and real data collected from a faulty bearing are used to validate the MS-CSR method. Comparison results show that the proposed method is useful for detecting weak signal with multiple characteristic frequencies. The seventh paper in this special issue is EMA Remaining Useful Life Prediction with Weighted Bagging GPR Algorithm by Y. Zhang, D Liu, J. Yu, Y. Peng, and X. Peng. This work is an investigation on electro-mechanical actuator (EMA) remaining useful life (RUL) prediction, which is critical for the PHM development of avionics system. Due to the uncertainty and nonlinearity existing in RUL prediction of EMA, a weighted bagging Gaussian process regression (WB_GPR) algorithm combining ensemble learning is proposed in this work. A case study utilizing the sensor data collected from the EMA testbed is conducted to validate the proposed WB_GPR, and it can be concluded that the WB_GPR is superior in the RUL prediction with lower error rate and standard deviation. The eighth paper in this special issue is Quantitative Selection of Sensor Data Based on Improved Permutation Entropy for System Remaining
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Editorial
Useful Life Prediction, by L. Liu, S. Wang, D. Liu, and Y. Peng. The purpose of this work is to solve the problem of condition data selection in the process of RUL prediction, and the quantitative metric of an improved permutation entropy is defined. Two case studies are carried out to evaluate the proposed metric, which prove the advantage of the proposed approach. The ninth paper in this special issue is Service Reliability Modeling and Evaluation of Active-active Cloud Data Center Based on the IT Infrastructure, by X. Li, Y. Liu, R. Kang, and L. Xiao. This work focuses on the active-active data center, which is a typical mode of the cloud data center. A method based on the queuing theory and graph theory is proposed for the service reliability modeling of IT infrastructure. The tenth paper in this special issue is An Optimal Evaluating Method for Uncertainty Metrics in Reliability Based on Uncertain Data Envelopment Analysis, by T. Zu, M. Wen, and R. Kang. This work proposes an uncertainty evaluation model to realize objective evaluation on some popular reliability uncertainty metrics. An evaluating index system is defined from the aspects of capability and adaptability, and an evaluating method based on uncertain data envelopment analysis is proposed. The evaluating method is validated with a numerical example, which shows that final metric choices vary with different requirements. The eleventh paper in this special issue is Remaining Useful Life Prediction of Lithium-ion Battery Using An Improved UPF Method Based on MCMC, by X. Zhang, Q. Miao, and Z. Liu. This work proposes an improved unscented particle filter (IUPF) method for lithium-ion battery RUL prediction. The Markov chain Monte Carlo (MCMC) is utilized to solve the problem of sample impoverishment in UPF algorithm. A case study using the lithium-ion battery life test data is conducted, and the proposed method demonstrates its effectiveness for the battery RUL prediction. The twelfth paper in this special issue is Research on Fault Diagnosis of Airborne Fuel Pump Based on EMD and Probabilistic Neural Networks, by X. Jiao, B. Jing, Y. Huang, J. Li, and G. Xu. This work develops an experimental platform of airborne fuel transfusion system, and proposes a fault diagnosis model based on empirical mode decomposition and probabilistic neural networks. Case study utilizing the data obtained from the experimental platform shows that only one pressure sensor and one y-axis vibration sensor are needed to achieve 100% airborne fuel pump fault diagnosis. The thirteenth paper in this special issue is Current Similarity Based Open-Circuit Fault Diagnosis for Induction Motor Drives with Discrete Wavelet Transform, by F. Wu, Y. Hao, J. Zhao, and Y. Liu. This work proposes a real-time single and multiple transistor open-circuit fault diagnosis based on current similarity analysis. The discrete wavelet
transform is used for pre-treatment of three-phase output currents, and the Euclidean distance measurement is used to realize fault diagnosis through the current similarity. Simulation and experimental results show high efficiency of the proposed method. The fourteenth paper in this special issue is A Fault Detection Strategy Using the Enhancement Ensemble Empirical Mode Decomposition and Random Decrement Technique, by J. Xiang, and Y. Zhong. This work presents a new fault detection method that combines the fast ensemble empirical mode decomposition (EEMD) and the random decrement technique (RDT). The proposed method utilizes EEMD for signal decomposition, and the RDT for extraction of impulsive component. The fifteenth paper in this special issue is Deep Neural Networksbased Rolling Bearing Fault Diagnosis, by Z. Chen, S. Deng, X. Chen, C. Li, RV Sanchez, and H. Qin. This work employs three deep neural network models (i.e., Deep Boltzmann Machines, Deep Belief Networks and Stacked Auto-Encoders) to identify rolling bearing fault condition, and four preprocessing schemes including time domain, frequency domain and time-frequency domain are considered for feature extraction. The analysis results show that the deep neural network models are highly reliable in rolling bearing fault diagnosis. As a concluding remark, we would like to thank all the authors and reviewers for their invaluable contributions to the Special Issue on Recent Progress on Electro-mechanical System Prognostics and Health Management. We believe the research articles included in this special issue offer some new insights into the electromechanical system reliability and prognostics, and will benefit both the academia and industry through the research and development on the electromechanical system reliability and prognostics. Specifically, we would like to thank the Editor-in-Chief, Professor Ninoslav Stojadinovic, for giving us the opportunity to organize this special issue. Qiang Miao School of Aeronautics and Astronautics, Sichuan University, No.24 South Section 1, Yihuan Road, Chengdu 610065, China E-mail address: [email protected] Datong Liu School of Electrical Engineering and Automation, Harbin Institute of Technology (HIT), #3033, Science Park of HIT, No.2, Yikuang Street, NanGang District, Harbin 150080, China E-mail address: [email protected]
Contents lists available at ScienceDirect
Microelectronics Reliability journal homepage: www.elsevier.com/locate/microrel
Editorial
Recent progress on electro-mechanical system prognostics and health management
As complexity and intelligence of electromechanical system continuously improve, probability of failure gradually increases and maintenance and security costs are getting higher, correspondingly. Failure of electromechanical system may lead to huge economic losses and adverse social effects, and even catastrophic accidents. Therefore, prognostics and health management (PHM) with efficient prognostic technology has become highly desirable and a very important research area as a result of need to provide electromechanical system with system level health management. However, operating environment of electromechanical system is usually complicated, and uncertainty associate with loading conditions further imposes difficulty in PHM implementation. The motivation of this special issue stems from the growing interest to develop reliability and prognosis solutions in electromechanical systems. Fifteen research articles were selected from PHM-2016 Chengdu, which cover several topics of electromechanical system health management and prognostics. Each article was blindly reviewed by normally three reviewers consisting of guest editor and external reviewers. The first paper in this special issue is IETM Centered Intelligent Maintenance System Integrating Fuzzy Semantic Inference and Data Fusion, by G. Niu, and H. Li. This work presents a novel interactive electronic technical manual (IETM) centered intelligent maintenance system, which integrates diagnosis strategies of experience-based manual interpretation, rule-based fuzzy semantic inference and conditionbased data fusion. The proposed method was evaluated by two experiments of fault diagnosis for electric multiple units (EMU) trains. Experiment results show that intelligent, convenient, accurate and flexible diagnosis advantages can be obtained, which are beneficial to maintenance implementation. The second paper in this special issue is Health Assessment and Management of Wind Turbine Blade Based on the Fatigue Test Data, by X. Bai, Z. An, Y. Hou, and Q. Ma. This work defines a health degree indicator by the Grey Relation (GR) model, which can be used to quantitatively assess wind turbine blade health condition. The availability, reliability and artificial test result are taken as three indexes of the health degree. As a result, an approach and a certain theoretical guidance for the health assessment and management of wind turbine blade are established. The third paper in this special issue is A Deep Learning-Based Recognition Method for Degradation Monitoring of Ball Screw with MultiSensor Data Fusion, by L. Zhang, H. Gao,J. Wen, S. Li, and Q. Liu. This work proposes an intelligent ball screw degradation recognition method based on deep belief networks (DBN) and multi-sensor data fusion. A
http://dx.doi.org/10.1016/j.microrel.2017.07.089 0026-2714/© 2017 Published by Elsevier Ltd.
case study using dataset collected from the degradation test of ball screw is conducted to validate the proposed method. The fourth paper in this special issue is Condition Multi-classification and Evaluation of System Degradation Process Using An Improved Support Vector Machine, by Q. Miao, X. Zhang, Z. Liu, and H. Zhang. The purpose of this work is to realize multi-state condition classification of system degradation process, and an improved support vector machine with a new voting scheme is proposed. Lifetime experimental data collected from a cooling fan bearing accelerated test is used to validate the method, and the comparison study shows that the proposed method improves the classification accuracy. The fifth paper in this special issue is Coupling Damage and Reliability Modeling for Creep and Fatigue of Solder Joint by Y. Chen, Y. Jin, and R. Kang. This work focuses on two dominant failure modes of the solder joint, including the low-cycle fatigue due to temperature cycling and the creep brought by continuous high temperature. A coupling damage model considering both low-cycle fatigue and creep is established. A case study of a lead-free solder joint is conducted to validate the proposed method. The sixth paper in this special issue is Multi-frequency Weak Signal Detection Based on Multi-segment Cascaded Stochastic Resonance for Rolling Bearings, by W. Guo, Z. Zhou, C. Chen, and X. Li. This work proposes an improved mechanism for the stochastic resonance (SR), called multi-segment cascaded stochastic resonance (MS-CSR), so as to solve the problem of noise impact on incipient fault detection. In the case study section, simulated data and real data collected from a faulty bearing are used to validate the MS-CSR method. Comparison results show that the proposed method is useful for detecting weak signal with multiple characteristic frequencies. The seventh paper in this special issue is EMA Remaining Useful Life Prediction with Weighted Bagging GPR Algorithm by Y. Zhang, D Liu, J. Yu, Y. Peng, and X. Peng. This work is an investigation on electro-mechanical actuator (EMA) remaining useful life (RUL) prediction, which is critical for the PHM development of avionics system. Due to the uncertainty and nonlinearity existing in RUL prediction of EMA, a weighted bagging Gaussian process regression (WB_GPR) algorithm combining ensemble learning is proposed in this work. A case study utilizing the sensor data collected from the EMA testbed is conducted to validate the proposed WB_GPR, and it can be concluded that the WB_GPR is superior in the RUL prediction with lower error rate and standard deviation. The eighth paper in this special issue is Quantitative Selection of Sensor Data Based on Improved Permutation Entropy for System Remaining
196
Editorial
Useful Life Prediction, by L. Liu, S. Wang, D. Liu, and Y. Peng. The purpose of this work is to solve the problem of condition data selection in the process of RUL prediction, and the quantitative metric of an improved permutation entropy is defined. Two case studies are carried out to evaluate the proposed metric, which prove the advantage of the proposed approach. The ninth paper in this special issue is Service Reliability Modeling and Evaluation of Active-active Cloud Data Center Based on the IT Infrastructure, by X. Li, Y. Liu, R. Kang, and L. Xiao. This work focuses on the active-active data center, which is a typical mode of the cloud data center. A method based on the queuing theory and graph theory is proposed for the service reliability modeling of IT infrastructure. The tenth paper in this special issue is An Optimal Evaluating Method for Uncertainty Metrics in Reliability Based on Uncertain Data Envelopment Analysis, by T. Zu, M. Wen, and R. Kang. This work proposes an uncertainty evaluation model to realize objective evaluation on some popular reliability uncertainty metrics. An evaluating index system is defined from the aspects of capability and adaptability, and an evaluating method based on uncertain data envelopment analysis is proposed. The evaluating method is validated with a numerical example, which shows that final metric choices vary with different requirements. The eleventh paper in this special issue is Remaining Useful Life Prediction of Lithium-ion Battery Using An Improved UPF Method Based on MCMC, by X. Zhang, Q. Miao, and Z. Liu. This work proposes an improved unscented particle filter (IUPF) method for lithium-ion battery RUL prediction. The Markov chain Monte Carlo (MCMC) is utilized to solve the problem of sample impoverishment in UPF algorithm. A case study using the lithium-ion battery life test data is conducted, and the proposed method demonstrates its effectiveness for the battery RUL prediction. The twelfth paper in this special issue is Research on Fault Diagnosis of Airborne Fuel Pump Based on EMD and Probabilistic Neural Networks, by X. Jiao, B. Jing, Y. Huang, J. Li, and G. Xu. This work develops an experimental platform of airborne fuel transfusion system, and proposes a fault diagnosis model based on empirical mode decomposition and probabilistic neural networks. Case study utilizing the data obtained from the experimental platform shows that only one pressure sensor and one y-axis vibration sensor are needed to achieve 100% airborne fuel pump fault diagnosis. The thirteenth paper in this special issue is Current Similarity Based Open-Circuit Fault Diagnosis for Induction Motor Drives with Discrete Wavelet Transform, by F. Wu, Y. Hao, J. Zhao, and Y. Liu. This work proposes a real-time single and multiple transistor open-circuit fault diagnosis based on current similarity analysis. The discrete wavelet
transform is used for pre-treatment of three-phase output currents, and the Euclidean distance measurement is used to realize fault diagnosis through the current similarity. Simulation and experimental results show high efficiency of the proposed method. The fourteenth paper in this special issue is A Fault Detection Strategy Using the Enhancement Ensemble Empirical Mode Decomposition and Random Decrement Technique, by J. Xiang, and Y. Zhong. This work presents a new fault detection method that combines the fast ensemble empirical mode decomposition (EEMD) and the random decrement technique (RDT). The proposed method utilizes EEMD for signal decomposition, and the RDT for extraction of impulsive component. The fifteenth paper in this special issue is Deep Neural Networksbased Rolling Bearing Fault Diagnosis, by Z. Chen, S. Deng, X. Chen, C. Li, RV Sanchez, and H. Qin. This work employs three deep neural network models (i.e., Deep Boltzmann Machines, Deep Belief Networks and Stacked Auto-Encoders) to identify rolling bearing fault condition, and four preprocessing schemes including time domain, frequency domain and time-frequency domain are considered for feature extraction. The analysis results show that the deep neural network models are highly reliable in rolling bearing fault diagnosis. As a concluding remark, we would like to thank all the authors and reviewers for their invaluable contributions to the Special Issue on Recent Progress on Electro-mechanical System Prognostics and Health Management. We believe the research articles included in this special issue offer some new insights into the electromechanical system reliability and prognostics, and will benefit both the academia and industry through the research and development on the electromechanical system reliability and prognostics. Specifically, we would like to thank the Editor-in-Chief, Professor Ninoslav Stojadinovic, for giving us the opportunity to organize this special issue. Qiang Miao School of Aeronautics and Astronautics, Sichuan University, No.24 South Section 1, Yihuan Road, Chengdu 610065, China E-mail address: [email protected] Datong Liu School of Electrical Engineering and Automation, Harbin Institute of Technology (HIT), #3033, Science Park of HIT, No.2, Yikuang Street, NanGang District, Harbin 150080, China E-mail address: [email protected]