In this paper, the vibration performances of a three-phase asynchronous motor with air-gap eccentricity were studied The governing equations of the eccentric rotor-bearing system was established by considering the nonlinear bearing contact forces and the unbalanced magnetic pull (UMP) acting on the rotor. The UMP was calculated at the axial position of the supporting bearings, by integrating the total air gap distribution along the axial and circumferential direction. The air gap distribution can be deduced from the stator, rotor MMF harmonics and their related harmonics. By substituting the UMP into the governing equations, numerical responses of the rotor-bearing system under air-gap eccentricity can be simulated. Results show that the shaft orbits at both two ends of the shaft reside on their eccentric positions, i.e., the orbits of two shaft ends with symmetric angular eccentricity have the opposite phase direction. Besides, in the power spectrum of the simulated acceleration, the second harmonic component turns out to be notable compared with the fundamental frequency
{"title":"Vibration Performance of a Three-Phase Asynchronous Motor With Air-Gap Eccentricity","authors":"Shen Chen, Xiong Xin, Zheng Shaoshuai, Xu Gang-hui","doi":"10.1109/phm-qingdao46334.2019.8943067","DOIUrl":"https://doi.org/10.1109/phm-qingdao46334.2019.8943067","url":null,"abstract":"In this paper, the vibration performances of a three-phase asynchronous motor with air-gap eccentricity were studied The governing equations of the eccentric rotor-bearing system was established by considering the nonlinear bearing contact forces and the unbalanced magnetic pull (UMP) acting on the rotor. The UMP was calculated at the axial position of the supporting bearings, by integrating the total air gap distribution along the axial and circumferential direction. The air gap distribution can be deduced from the stator, rotor MMF harmonics and their related harmonics. By substituting the UMP into the governing equations, numerical responses of the rotor-bearing system under air-gap eccentricity can be simulated. Results show that the shaft orbits at both two ends of the shaft reside on their eccentric positions, i.e., the orbits of two shaft ends with symmetric angular eccentricity have the opposite phase direction. Besides, in the power spectrum of the simulated acceleration, the second harmonic component turns out to be notable compared with the fundamental frequency","PeriodicalId":259179,"journal":{"name":"2019 Prognostics and System Health Management Conference (PHM-Qingdao)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129619335","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Battery life estimation is a key part for lithium cells for a long cycle life. The purpose of this paper is to develop a life extrapolation model for evaluating the lithium-ion battery cycle life with accelerated degradation test (ADT) data. An ADT is carried out including lithium-ion battery cells discharged with different current. The ADT data are used for parameterization with the accelerated model and distribution model. The lifetime of normal working condition is obtained by the fusion of accelerated model and accelerated data. To improve ability for life extrapolation, the proposed method is modeled with uncertainty expression by confidence lower limits and confidence lower limits for the reliability for the extrapolated life. Finally, extrapolation trajectory with uncertainty expression is obtained and the extrapolation result indicates that the proposed model can provide more accurate estimates with life extrapolation. In addition, the remaining useful life corresponding to any discharge current can be also calculated.
{"title":"Life Extrapolation Model for Lithium-ion Battery with Accelerated Degradation Test","authors":"Yandong Hou, Wei Wu, Yuchen Song, Chen Yang, Datong Liu, Yu Peng","doi":"10.1109/phm-qingdao46334.2019.8943027","DOIUrl":"https://doi.org/10.1109/phm-qingdao46334.2019.8943027","url":null,"abstract":"Battery life estimation is a key part for lithium cells for a long cycle life. The purpose of this paper is to develop a life extrapolation model for evaluating the lithium-ion battery cycle life with accelerated degradation test (ADT) data. An ADT is carried out including lithium-ion battery cells discharged with different current. The ADT data are used for parameterization with the accelerated model and distribution model. The lifetime of normal working condition is obtained by the fusion of accelerated model and accelerated data. To improve ability for life extrapolation, the proposed method is modeled with uncertainty expression by confidence lower limits and confidence lower limits for the reliability for the extrapolated life. Finally, extrapolation trajectory with uncertainty expression is obtained and the extrapolation result indicates that the proposed model can provide more accurate estimates with life extrapolation. In addition, the remaining useful life corresponding to any discharge current can be also calculated.","PeriodicalId":259179,"journal":{"name":"2019 Prognostics and System Health Management Conference (PHM-Qingdao)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130048441","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2019-10-01DOI: 10.1109/phm-qingdao46334.2019.8942981
Pengfei Guo, Jun Wu, Xuebing Xu, Yiwei Cheng, Yuanhang Wang
Hydraulic system is a vital transmission system for its high stability and fast reaction as well as high transmission ratio. Whereas, hydraulic systems usually operate in a tough environment and need to be ensure for normal operating, which make it essential to precisely detect the health status of every significant component in a hydraulic system. A novel health condition monitoring method for hydraulic system is proposed in this paper based on ensemble support vector machine. Firstly, statistical features are extract from multiple sensor signals to describe the health condition characteristics of the hydraulic system. Then, the extracted features are selected using Pearson correlation coefficient. Finally, the health condition identification is realized based on ensemble support vector machine with stacking algorithm. The experimental results show that the proposed method for health condition identification of the hydraulic system is better than the other methods.
{"title":"Health condition monitoring of hydraulic system based on ensemble support vector machine","authors":"Pengfei Guo, Jun Wu, Xuebing Xu, Yiwei Cheng, Yuanhang Wang","doi":"10.1109/phm-qingdao46334.2019.8942981","DOIUrl":"https://doi.org/10.1109/phm-qingdao46334.2019.8942981","url":null,"abstract":"Hydraulic system is a vital transmission system for its high stability and fast reaction as well as high transmission ratio. Whereas, hydraulic systems usually operate in a tough environment and need to be ensure for normal operating, which make it essential to precisely detect the health status of every significant component in a hydraulic system. A novel health condition monitoring method for hydraulic system is proposed in this paper based on ensemble support vector machine. Firstly, statistical features are extract from multiple sensor signals to describe the health condition characteristics of the hydraulic system. Then, the extracted features are selected using Pearson correlation coefficient. Finally, the health condition identification is realized based on ensemble support vector machine with stacking algorithm. The experimental results show that the proposed method for health condition identification of the hydraulic system is better than the other methods.","PeriodicalId":259179,"journal":{"name":"2019 Prognostics and System Health Management Conference (PHM-Qingdao)","volume":"27 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132167445","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2019-10-01DOI: 10.1109/phm-qingdao46334.2019.8942924
Songhua Hao, Jun Yang, Hao Wang, Kun Wang
In recent decades, the gas insulated transmission line (GIL) has been developed as a reliable technology for electric power transmission. The GIL are subject to degradation-shock-based competing failure modes of SF6 gas leakage and partial discharge. To maintain the high reliability of the GIL in its long lifetime, condition-based maintenance (CBM) can be designed based on inspected degradation levels. However, due to the complex inner structures and limited inspection environment of the GIL, the SF6 leakage rate cannot always be perfectly inspected. Therefore, this paper proposes a new CBM strategy with imperfect inspections for the GIL. Based on the concepts of false positive (FP) and false negative (FN) incurred by imperfect inspections, long run cost rate is computed under three different scenarios of maintenance actions, i.e., ending up with corrective maintenance (CM) for soft failure, CM for hard failure, and preventive maintenance (PM). The optimal inspection interval and preventive maintenance threshold are obtained by minimizing the long run cost rate. A numerical example illustrates the effectiveness of the proposed strategy, and sensitivity analysis is conducted to study the effects of imperfect inspection cost and inspection error.
{"title":"Condition-based Maintenance with Imperfect Inspections for the GIL Subject to Continuous Degradation and Random Shocks","authors":"Songhua Hao, Jun Yang, Hao Wang, Kun Wang","doi":"10.1109/phm-qingdao46334.2019.8942924","DOIUrl":"https://doi.org/10.1109/phm-qingdao46334.2019.8942924","url":null,"abstract":"In recent decades, the gas insulated transmission line (GIL) has been developed as a reliable technology for electric power transmission. The GIL are subject to degradation-shock-based competing failure modes of SF6 gas leakage and partial discharge. To maintain the high reliability of the GIL in its long lifetime, condition-based maintenance (CBM) can be designed based on inspected degradation levels. However, due to the complex inner structures and limited inspection environment of the GIL, the SF6 leakage rate cannot always be perfectly inspected. Therefore, this paper proposes a new CBM strategy with imperfect inspections for the GIL. Based on the concepts of false positive (FP) and false negative (FN) incurred by imperfect inspections, long run cost rate is computed under three different scenarios of maintenance actions, i.e., ending up with corrective maintenance (CM) for soft failure, CM for hard failure, and preventive maintenance (PM). The optimal inspection interval and preventive maintenance threshold are obtained by minimizing the long run cost rate. A numerical example illustrates the effectiveness of the proposed strategy, and sensitivity analysis is conducted to study the effects of imperfect inspection cost and inspection error.","PeriodicalId":259179,"journal":{"name":"2019 Prognostics and System Health Management Conference (PHM-Qingdao)","volume":"56 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125493379","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2019-10-01DOI: 10.1109/phm-qingdao46334.2019.8943055
Yongcheng Gao, Jun Zhou, Kankan Wu, Guangquan Zhao, Cong Hu
Traditional turbine engine health indicator (HI) construction methods generally require manual feature extraction, feature selection and even feature fusion, besides, training labels need to be designed in advance, which make the whole procedure time consuming and not universal. Therefore, this paper proposes a novel unsupervised construction method of turbine engine health indicator based on stacked denoising autoencoders (SDAE). In this method, the deep structure of autoencoders adaptively extracts features of raw turbine engine monitoring signals in an unsupervised way to obtain its health indicator. Experimental results on CMAPSS engine dataset show that the HI curves constructed by the proposed method can well reflect the degradation process of turbine engine during the whole life cycle, and have better correlation and monotonicity compared to the traditional HI construction methods. Moreover, the proposed method does not need to rely on complex signal processing measures, the whole process is carried out in an unsupervised manner with a certain degree of versatility.
{"title":"Construction Method of Turbine Engine Health Indicator Based on Deep Learning","authors":"Yongcheng Gao, Jun Zhou, Kankan Wu, Guangquan Zhao, Cong Hu","doi":"10.1109/phm-qingdao46334.2019.8943055","DOIUrl":"https://doi.org/10.1109/phm-qingdao46334.2019.8943055","url":null,"abstract":"Traditional turbine engine health indicator (HI) construction methods generally require manual feature extraction, feature selection and even feature fusion, besides, training labels need to be designed in advance, which make the whole procedure time consuming and not universal. Therefore, this paper proposes a novel unsupervised construction method of turbine engine health indicator based on stacked denoising autoencoders (SDAE). In this method, the deep structure of autoencoders adaptively extracts features of raw turbine engine monitoring signals in an unsupervised way to obtain its health indicator. Experimental results on CMAPSS engine dataset show that the HI curves constructed by the proposed method can well reflect the degradation process of turbine engine during the whole life cycle, and have better correlation and monotonicity compared to the traditional HI construction methods. Moreover, the proposed method does not need to rely on complex signal processing measures, the whole process is carried out in an unsupervised manner with a certain degree of versatility.","PeriodicalId":259179,"journal":{"name":"2019 Prognostics and System Health Management Conference (PHM-Qingdao)","volume":"36 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116117658","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2019-10-01DOI: 10.1109/phm-qingdao46334.2019.8942984
Xin Wang, Wenyi Liu, Mengchen Shan
In this paper, wind turbine fault state and normal working conditions, using the classical transfer function model in Control theory, are characterized by the external fault of the whole system. Considering the internal impact with each other in the whole wind turbine power system, main idea of the paper tries to explore whether the final impact of the fault signal on the system can be quantified.
{"title":"Fault Diagnosis Analysis of Wind Turbine Gear Based on Transfer Function Model","authors":"Xin Wang, Wenyi Liu, Mengchen Shan","doi":"10.1109/phm-qingdao46334.2019.8942984","DOIUrl":"https://doi.org/10.1109/phm-qingdao46334.2019.8942984","url":null,"abstract":"In this paper, wind turbine fault state and normal working conditions, using the classical transfer function model in Control theory, are characterized by the external fault of the whole system. Considering the internal impact with each other in the whole wind turbine power system, main idea of the paper tries to explore whether the final impact of the fault signal on the system can be quantified.","PeriodicalId":259179,"journal":{"name":"2019 Prognostics and System Health Management Conference (PHM-Qingdao)","volume":"39 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115634775","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2019-10-01DOI: 10.1109/phm-qingdao46334.2019.8942900
Jianmin Zhou, Chenchen Zhang, Faling Wang
Bearings used in the wind turbine generators (WTGs) will subject to different degrees of damage during operation, including all kinds of vibration and shock. In this paper, a vibration-based performance degradation assessment method for high-speed shaft wind turbine bearings is proposed using fusion of Hidden Markov Model (HMM) and Fuzzy C-means Model (FCM). The wavelet packet decomposition is used to extract the energy of the wavelet packet nodes of the whole life cycle vibration signal. The autoregressive model (AR) extracts the coefficients and residual of the wavelet packet nodes, and takes the two features as the combined features. The FCM is established using the normal and failure samples and the HMM is established using the normal samples. The two degradation indicators which was obtained by imputing the under test data to FCM and HMM model are input to the FCM model as the input characteristic. Then the performance degradation curve is obtained. Finally, Mahalanobis distance (MD) and FCM models are combined to compare and illustrate. The method combines the advantages of spatial statistical distance model and probabilistic statistical model. Then the WTG bearing’s experimental data are used and the experimental results of AR model combined with FCM model are compared to verify the conclusions of this paper. The experimental analysis shows that the method is consistent with the performance degradation trend of rolling bearings and has certain adaptability.
{"title":"A Method for Performance Degradation Assessment of Wind Turbine Bearings Based on Hidden Markov Model and Fuzzy C-means Model","authors":"Jianmin Zhou, Chenchen Zhang, Faling Wang","doi":"10.1109/phm-qingdao46334.2019.8942900","DOIUrl":"https://doi.org/10.1109/phm-qingdao46334.2019.8942900","url":null,"abstract":"Bearings used in the wind turbine generators (WTGs) will subject to different degrees of damage during operation, including all kinds of vibration and shock. In this paper, a vibration-based performance degradation assessment method for high-speed shaft wind turbine bearings is proposed using fusion of Hidden Markov Model (HMM) and Fuzzy C-means Model (FCM). The wavelet packet decomposition is used to extract the energy of the wavelet packet nodes of the whole life cycle vibration signal. The autoregressive model (AR) extracts the coefficients and residual of the wavelet packet nodes, and takes the two features as the combined features. The FCM is established using the normal and failure samples and the HMM is established using the normal samples. The two degradation indicators which was obtained by imputing the under test data to FCM and HMM model are input to the FCM model as the input characteristic. Then the performance degradation curve is obtained. Finally, Mahalanobis distance (MD) and FCM models are combined to compare and illustrate. The method combines the advantages of spatial statistical distance model and probabilistic statistical model. Then the WTG bearing’s experimental data are used and the experimental results of AR model combined with FCM model are compared to verify the conclusions of this paper. The experimental analysis shows that the method is consistent with the performance degradation trend of rolling bearings and has certain adaptability.","PeriodicalId":259179,"journal":{"name":"2019 Prognostics and System Health Management Conference (PHM-Qingdao)","volume":"101 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124941556","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2019-10-01DOI: 10.1109/phm-qingdao46334.2019.8942938
Xiaoliang Ling, Yazhou Zhang, Ping Li
This paper studies a system exposed to shocks, and the effect of the corresponding shocks may be fatal or accumulated. Assume the spare unit for the system has a random lead-time, we study the periodic inspection policy of this system. We formulate a model for the sake of minimizing the average cost per unit time. We give a numerical example to calculate the optimal inspection time.
{"title":"Periodic Inspection Policies of a System Subject to Shocks with Random Lead-time","authors":"Xiaoliang Ling, Yazhou Zhang, Ping Li","doi":"10.1109/phm-qingdao46334.2019.8942938","DOIUrl":"https://doi.org/10.1109/phm-qingdao46334.2019.8942938","url":null,"abstract":"This paper studies a system exposed to shocks, and the effect of the corresponding shocks may be fatal or accumulated. Assume the spare unit for the system has a random lead-time, we study the periodic inspection policy of this system. We formulate a model for the sake of minimizing the average cost per unit time. We give a numerical example to calculate the optimal inspection time.","PeriodicalId":259179,"journal":{"name":"2019 Prognostics and System Health Management Conference (PHM-Qingdao)","volume":"27 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114272008","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2019-10-01DOI: 10.1109/phm-qingdao46334.2019.8942891
M. Long, Hu Fang, Zhou Yuege, Yao Zemin, Pang Bo
Optical cable communication is an important development direction of spacecraft bus transmission due to its advantages of large capacity, light weight and low loss. This paper introduces the structure of optical cables for spacecraft and summarizes the main failure modes. During the on-orbit service, the product characteristics tend to deteriorate with the increase of service time. Loss coefficient is a sensitive parameter to characterize the degradation process. That is, loss coefficient has obvious degradation in the lifetime. In view of the degradation behavior, the loss coefficient is taken as the sensitive degradation parameter. The data obtained from accelerated test are used to establish the performance degradation model of optical cables, to clarify the degradation law. The particle filter method is used to predict the degradation trend and the service life, so as to provide guidance for engineering application.
{"title":"Research on Life Assessment Method of Spacecraft Optical Cable Based on Degradation Data","authors":"M. Long, Hu Fang, Zhou Yuege, Yao Zemin, Pang Bo","doi":"10.1109/phm-qingdao46334.2019.8942891","DOIUrl":"https://doi.org/10.1109/phm-qingdao46334.2019.8942891","url":null,"abstract":"Optical cable communication is an important development direction of spacecraft bus transmission due to its advantages of large capacity, light weight and low loss. This paper introduces the structure of optical cables for spacecraft and summarizes the main failure modes. During the on-orbit service, the product characteristics tend to deteriorate with the increase of service time. Loss coefficient is a sensitive parameter to characterize the degradation process. That is, loss coefficient has obvious degradation in the lifetime. In view of the degradation behavior, the loss coefficient is taken as the sensitive degradation parameter. The data obtained from accelerated test are used to establish the performance degradation model of optical cables, to clarify the degradation law. The particle filter method is used to predict the degradation trend and the service life, so as to provide guidance for engineering application.","PeriodicalId":259179,"journal":{"name":"2019 Prognostics and System Health Management Conference (PHM-Qingdao)","volume":"7 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125263285","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2019-10-01DOI: 10.1109/phm-qingdao46334.2019.8943059
Yuefeng Liu, Gong Zhang, Chenrong Zhang, Yuhui Yang, Lina Zhang
With the development of the industry, the performance of large and complex systems is constantly increasing and the complexity is increasing. In the process of using mechanical equipment, there is often a phenomenon of downtime and the most of the reasons is that the related parts are faulty. As one of the foremost tasks of prognostic and health management (PHM) and condition based maintenance (CBM), the prediction of remaining useful life (RUL) for mechanical equipment is receiving more and more attention. By knowing the RUL of the equipment, it can play an important role in maintaining related equipment in advance. It is more effective than the traditional regular maintenance and post-repair maintenance, thus avoiding the occurrence of malfunctions and the reduction of property loss. This paper focuses on the AI-based RUL prediction methods and explains the strengths and weaknesses of each of these methods and summarizes the latest literature on various methods in the last few years. Finally, the present methods and future trends are discussed and hot spots for the future are given.
{"title":"Research Progress on Data Driven-based RUL Prediction Methods of Mechanical Equipment","authors":"Yuefeng Liu, Gong Zhang, Chenrong Zhang, Yuhui Yang, Lina Zhang","doi":"10.1109/phm-qingdao46334.2019.8943059","DOIUrl":"https://doi.org/10.1109/phm-qingdao46334.2019.8943059","url":null,"abstract":"With the development of the industry, the performance of large and complex systems is constantly increasing and the complexity is increasing. In the process of using mechanical equipment, there is often a phenomenon of downtime and the most of the reasons is that the related parts are faulty. As one of the foremost tasks of prognostic and health management (PHM) and condition based maintenance (CBM), the prediction of remaining useful life (RUL) for mechanical equipment is receiving more and more attention. By knowing the RUL of the equipment, it can play an important role in maintaining related equipment in advance. It is more effective than the traditional regular maintenance and post-repair maintenance, thus avoiding the occurrence of malfunctions and the reduction of property loss. This paper focuses on the AI-based RUL prediction methods and explains the strengths and weaknesses of each of these methods and summarizes the latest literature on various methods in the last few years. Finally, the present methods and future trends are discussed and hot spots for the future are given.","PeriodicalId":259179,"journal":{"name":"2019 Prognostics and System Health Management Conference (PHM-Qingdao)","volume":"341 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125301782","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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