Pub Date : 2019-10-01DOI: 10.1109/phm-qingdao46334.2019.8942875
Zhang Yang, Xu Dong, Cheng Hongwei
Considering the difficulty in designing large electromechanical systems and high cost for construction, the system redundancy design often prepares a variety of use schemes in advance, while the cost-benefit ratio of parallel, voting and other redundant methods employed by electronic equipment is not efficient enough. Based on discrete simulation algorithm, this paper proposes a method to model and calculate the mission reliability block diagram for multiple use schemes system, which is easy to understand and applicable, and helpful to get a quick result.
{"title":"A Modeling and Calculating Method for Mission Reliability of Multiple Use Schemes System","authors":"Zhang Yang, Xu Dong, Cheng Hongwei","doi":"10.1109/phm-qingdao46334.2019.8942875","DOIUrl":"https://doi.org/10.1109/phm-qingdao46334.2019.8942875","url":null,"abstract":"Considering the difficulty in designing large electromechanical systems and high cost for construction, the system redundancy design often prepares a variety of use schemes in advance, while the cost-benefit ratio of parallel, voting and other redundant methods employed by electronic equipment is not efficient enough. Based on discrete simulation algorithm, this paper proposes a method to model and calculate the mission reliability block diagram for multiple use schemes system, which is easy to understand and applicable, and helpful to get a quick result.","PeriodicalId":259179,"journal":{"name":"2019 Prognostics and System Health Management Conference (PHM-Qingdao)","volume":"26 12","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"120941437","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-qingdao46334.2019.8942975
Weitao Pan, Y. Y. A. Abuker, L. Mao
This paper investigates effectiveness of various features in fault diagnosis of polymer electrolyte membrane fuel cell (PEMFC) system, including RMSF (root mean square frequency), ACSD (autocorrelation standard deviation) and kurtosis. Test data is collected from a PEMFC system with various conditions, such as normal operation, flooding and drying out scenarios. By extracting selected features from PEMFC voltage, the performance of various features in isolating PEMFC states is investigated using k-means clustering. Results demonstrate that the combination of RMSF and ACSD could provide reliable fault diagnostic performance. Moreover, kurtosis might be used as a fast diagnostic indicator for various PEMFC degradation mechanisms.
{"title":"Investigation of Feature Effectiveness in Polymer Electrolyte Membrane Fuel Cell Fault Diagnosis","authors":"Weitao Pan, Y. Y. A. Abuker, L. Mao","doi":"10.1109/phm-qingdao46334.2019.8942975","DOIUrl":"https://doi.org/10.1109/phm-qingdao46334.2019.8942975","url":null,"abstract":"This paper investigates effectiveness of various features in fault diagnosis of polymer electrolyte membrane fuel cell (PEMFC) system, including RMSF (root mean square frequency), ACSD (autocorrelation standard deviation) and kurtosis. Test data is collected from a PEMFC system with various conditions, such as normal operation, flooding and drying out scenarios. By extracting selected features from PEMFC voltage, the performance of various features in isolating PEMFC states is investigated using k-means clustering. Results demonstrate that the combination of RMSF and ACSD could provide reliable fault diagnostic performance. Moreover, kurtosis might be used as a fast diagnostic indicator for various PEMFC degradation mechanisms.","PeriodicalId":259179,"journal":{"name":"2019 Prognostics and System Health Management Conference (PHM-Qingdao)","volume":"58 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":"125363625","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-qingdao46334.2019.8942832
Chongfeng Zhao, Jinglin Wang, Yongzhi Qu, Liu Hong, Zidong Liu, Yuegang Tan
The transmission error of a gear pair is one of the main excitations that cause the vibration and noise problems of gearboxes. The root causes of the transmission error include the gear manufacturing errors, the installing errors and the elastic deformation of meshing gear teeth. Although the transmission error have a significant influence on the dynamics of gear pairs, most of the previous studies just employ simplified mathematical functions to qualitatively represent its periodicity. Only recently, the experimental study was conducted to investigate the detailed properties of the transmission errors in quasi-static conditions, which requires strong expertise and costly precision equipment. Therefore, to give a quick evaluation of the properties of transmission error, this paper proposes a virtual model to numerically predict the transmission error of a spur gear pair in the static condition. The model is capable to simulate the transmission error that is caused by typical gear manufacturing and installing errors like the indexing errors and run-out errors. The simulated transmission errors agree with the experimental phenomenon observed in previous published works. The proposed virtual model has the potential to assist in the in-depth analysis and modeling of dynamic behavior of gear transmissions.
{"title":"A Virtual Model To Predict The Influence Of Indexing Errors On The Transmission Error Of Spur Gears","authors":"Chongfeng Zhao, Jinglin Wang, Yongzhi Qu, Liu Hong, Zidong Liu, Yuegang Tan","doi":"10.1109/phm-qingdao46334.2019.8942832","DOIUrl":"https://doi.org/10.1109/phm-qingdao46334.2019.8942832","url":null,"abstract":"The transmission error of a gear pair is one of the main excitations that cause the vibration and noise problems of gearboxes. The root causes of the transmission error include the gear manufacturing errors, the installing errors and the elastic deformation of meshing gear teeth. Although the transmission error have a significant influence on the dynamics of gear pairs, most of the previous studies just employ simplified mathematical functions to qualitatively represent its periodicity. Only recently, the experimental study was conducted to investigate the detailed properties of the transmission errors in quasi-static conditions, which requires strong expertise and costly precision equipment. Therefore, to give a quick evaluation of the properties of transmission error, this paper proposes a virtual model to numerically predict the transmission error of a spur gear pair in the static condition. The model is capable to simulate the transmission error that is caused by typical gear manufacturing and installing errors like the indexing errors and run-out errors. The simulated transmission errors agree with the experimental phenomenon observed in previous published works. The proposed virtual model has the potential to assist in the in-depth analysis and modeling of dynamic behavior of gear transmissions.","PeriodicalId":259179,"journal":{"name":"2019 Prognostics and System Health Management Conference (PHM-Qingdao)","volume":"15 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":"125511518","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-qingdao46334.2019.8942997
Hongwei Qiao, Li Zhan, Jie Liu, Lin Zhang, Zhangchun Tang, Jia Xie
Due to the development of science and technology, many electronic products still need a long time to degrade and fail under the condition of accelerated life test, especially under the harsh test conditions such as nuclear radiation, and it brings great challenges to research on the reliability of electronic products. In order to obtain the performance index of electronic product degradation failure, this paper proposes to use support vector regression(SVR) method to predict the performance degradation index of AP1117E series linear voltage stabilizer under nuclear radiation stress, and use the degradation data obtained from the test and the predicted degradation data to complete the reliability evaluation of the device. The prediction method proposed in this paper is used in the actual reliability assessment engineering project, and it has played a certain suggestive role for future reliability assessment work.
{"title":"Degradation Trend Prediction of Linear Regulator Based on SVR Under Nuclear Radiation Stress","authors":"Hongwei Qiao, Li Zhan, Jie Liu, Lin Zhang, Zhangchun Tang, Jia Xie","doi":"10.1109/phm-qingdao46334.2019.8942997","DOIUrl":"https://doi.org/10.1109/phm-qingdao46334.2019.8942997","url":null,"abstract":"Due to the development of science and technology, many electronic products still need a long time to degrade and fail under the condition of accelerated life test, especially under the harsh test conditions such as nuclear radiation, and it brings great challenges to research on the reliability of electronic products. In order to obtain the performance index of electronic product degradation failure, this paper proposes to use support vector regression(SVR) method to predict the performance degradation index of AP1117E series linear voltage stabilizer under nuclear radiation stress, and use the degradation data obtained from the test and the predicted degradation data to complete the reliability evaluation of the device. The prediction method proposed in this paper is used in the actual reliability assessment engineering project, and it has played a certain suggestive role for future reliability assessment work.","PeriodicalId":259179,"journal":{"name":"2019 Prognostics and System Health Management Conference (PHM-Qingdao)","volume":"18 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":"115121856","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-qingdao46334.2019.8942995
P. Lu, Ming Li, Liqiang Zhang, Liqin Zhou
Energy store batteries play very important roles in the marine energy power station. Monitoring batteries’ state for PHM study is necessary. Electrochemical Impedance Spectroscopy (EIS) is commonly used in battery state monitor. It can acquire detailed health features of batteries. This paper purposes a Fast-EIS measuring method, including the hardware design and frequency-mixing measuring algorithm based on the Fast Fourier Transformation (FFT). It can measure the EIS with a frequency range from 0.01Hz to 10kHz, and reduce 2/3 of the measuring time compared to the commercial electrochemical workstation, with enough accuracy. This method is suitable for implementation and engineering applications.
{"title":"A Novel Fast-EIS Measuring Method And Implementation for Lithium-ion Batteries","authors":"P. Lu, Ming Li, Liqiang Zhang, Liqin Zhou","doi":"10.1109/phm-qingdao46334.2019.8942995","DOIUrl":"https://doi.org/10.1109/phm-qingdao46334.2019.8942995","url":null,"abstract":"Energy store batteries play very important roles in the marine energy power station. Monitoring batteries’ state for PHM study is necessary. Electrochemical Impedance Spectroscopy (EIS) is commonly used in battery state monitor. It can acquire detailed health features of batteries. This paper purposes a Fast-EIS measuring method, including the hardware design and frequency-mixing measuring algorithm based on the Fast Fourier Transformation (FFT). It can measure the EIS with a frequency range from 0.01Hz to 10kHz, and reduce 2/3 of the measuring time compared to the commercial electrochemical workstation, with enough accuracy. This method is suitable for implementation and engineering applications.","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":"122072451","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-qingdao46334.2019.8942869
Liangliang Zhai, Kun Yang, Biao Hu, Shuai Li
As one of the main techniques of equipment condition monitoring, oil monitoring technology plays an extremely important role in evaluating the current state of equipment and predicting the development trend of equipment. In this paper, the LSTM neural networks was established by the historical data collected by a power plant. Using the cross validation method, and compared whit the popular time series prediction algorithm LSM, ARIMA, BPNN, SVR and RFR in the same test set, LSTM got the lowest RMSE value 42.26, which validates the applicability and accuracy of the LSTM neural network in the prediction of oil particle contamination.
{"title":"Study on the oil particle contamination forecasting Using LSTM network","authors":"Liangliang Zhai, Kun Yang, Biao Hu, Shuai Li","doi":"10.1109/phm-qingdao46334.2019.8942869","DOIUrl":"https://doi.org/10.1109/phm-qingdao46334.2019.8942869","url":null,"abstract":"As one of the main techniques of equipment condition monitoring, oil monitoring technology plays an extremely important role in evaluating the current state of equipment and predicting the development trend of equipment. In this paper, the LSTM neural networks was established by the historical data collected by a power plant. Using the cross validation method, and compared whit the popular time series prediction algorithm LSM, ARIMA, BPNN, SVR and RFR in the same test set, LSTM got the lowest RMSE value 42.26, which validates the applicability and accuracy of the LSTM neural network in the prediction of oil particle contamination.","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":"122136566","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-qingdao46334.2019.8942996
Jiawei Gu, Yanxue Wang
Traditional intelligent fault diagnosis techniques for rotating machines have two limitations: 1) Big data with fault information is not available in some cases; 2) The training and testing data are often drawn under discrepant distribution. Thus, transfer component analysis (TCA) has been designed to reduce the distance of marginal distribution between domains. The joint distribution adaptation (JDA) was proposed to simultaneously reduced the difference between the conditional distribution and marginal distribution in source or target domains. However, these two distributions are often treated equally in these existing methods, which will lead to poor performance in practical applications. Therefore, a cross-domain feature extraction method based on balanced distribution adaptation algorithm(BDA) has been proposed, which can adaptively utilize the importance of difference between marginal distribution and conditional distribution. It should be noted that several existing cross domain feature extraction methods can be treated as special cases of BDA. As a new method in the field of transfer learning, BDA is an effective cross-domain feature extraction method. The validity of the BDA algorithm has been successfully evaluated in the actual data set in this paper.
{"title":"A Cross Domain Feature Extraction Method for Bearing Fault diagnosis based on Balanced Distribution Adaptation","authors":"Jiawei Gu, Yanxue Wang","doi":"10.1109/phm-qingdao46334.2019.8942996","DOIUrl":"https://doi.org/10.1109/phm-qingdao46334.2019.8942996","url":null,"abstract":"Traditional intelligent fault diagnosis techniques for rotating machines have two limitations: 1) Big data with fault information is not available in some cases; 2) The training and testing data are often drawn under discrepant distribution. Thus, transfer component analysis (TCA) has been designed to reduce the distance of marginal distribution between domains. The joint distribution adaptation (JDA) was proposed to simultaneously reduced the difference between the conditional distribution and marginal distribution in source or target domains. However, these two distributions are often treated equally in these existing methods, which will lead to poor performance in practical applications. Therefore, a cross-domain feature extraction method based on balanced distribution adaptation algorithm(BDA) has been proposed, which can adaptively utilize the importance of difference between marginal distribution and conditional distribution. It should be noted that several existing cross domain feature extraction methods can be treated as special cases of BDA. As a new method in the field of transfer learning, BDA is an effective cross-domain feature extraction method. The validity of the BDA algorithm has been successfully evaluated in the actual data set in this paper.","PeriodicalId":259179,"journal":{"name":"2019 Prognostics and System Health Management Conference (PHM-Qingdao)","volume":"13 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":"129499953","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-qingdao46334.2019.8943021
Zixuan Yu, Tingting Huang, Xin Wu, Kun Zhou
A digital multimeter working in the marine environment suffers from complex environmental stresses of time-varying temperature, relative humidity and salinity. It is used to measure five indicators of Resistance (R), Direct Current Voltage (DCV), Alternating Current Voltage (ACV), Direct Current (DC) and Alternating Current (AC), and there is an interactive relationship between the five indicators due to the complex structure among the components of multimeter.Considering the measurement errors data of five indicators of digital multimeter as the degradation signal, this paper establishes a degradation model to predict the reliability of each single indicator considering the time-varying environmental stresses based on Brownian motion. The typical D-vine copula is utilized to describe the correlations of multiple performance indicators, the parameters of the optimal D-vine model can be estimated by maximum likelihood estimation. In this paper, lifetime of the multimeter working in marine environment with multiple-performance indicators can be predicted accurately. A case study is presented as an application of this method.
{"title":"Degradation Modeling of Digital Multimeter with Multiple-performance Indicators in Multi-stress Dynamic Marine Environment Based on Vine Copula","authors":"Zixuan Yu, Tingting Huang, Xin Wu, Kun Zhou","doi":"10.1109/phm-qingdao46334.2019.8943021","DOIUrl":"https://doi.org/10.1109/phm-qingdao46334.2019.8943021","url":null,"abstract":"A digital multimeter working in the marine environment suffers from complex environmental stresses of time-varying temperature, relative humidity and salinity. It is used to measure five indicators of Resistance (R), Direct Current Voltage (DCV), Alternating Current Voltage (ACV), Direct Current (DC) and Alternating Current (AC), and there is an interactive relationship between the five indicators due to the complex structure among the components of multimeter.Considering the measurement errors data of five indicators of digital multimeter as the degradation signal, this paper establishes a degradation model to predict the reliability of each single indicator considering the time-varying environmental stresses based on Brownian motion. The typical D-vine copula is utilized to describe the correlations of multiple performance indicators, the parameters of the optimal D-vine model can be estimated by maximum likelihood estimation. In this paper, lifetime of the multimeter working in marine environment with multiple-performance indicators can be predicted accurately. A case study is presented as an application of this method.","PeriodicalId":259179,"journal":{"name":"2019 Prognostics and System Health Management Conference (PHM-Qingdao)","volume":"54 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":"129601289","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-qingdao46334.2019.8942962
Gang Tang, Chaoren Qin, Zhi Xu, Ying Chen
In the prognostic and health management of rotating machinery, the characteristic frequency of early weak fault is usually difficult to be extracted. To overcome this difficulty, this paper presents a weak fault feature enhancement method of acoustic data for rolling bearings based on variational mode decomposition (VMD). Firstly, the acoustic data is decomposed into some band-limited intrinsic mode functions (BLIMF) by the optimized VMD. Then an adaptive signal-to-noise ratio (ASNR) estimation method is proposed to determine the optimal BLIMF. Finally, the fault types of rolling bearings are identified through Hilbert envelope transform. Experimental results show that the presented method can effectively enhance the feature for early weak fault in rolling bearings with acoustic data.
{"title":"Weak Fault Feature Enhancement of Acoustic Data Based on Variational Mode Decomposition","authors":"Gang Tang, Chaoren Qin, Zhi Xu, Ying Chen","doi":"10.1109/phm-qingdao46334.2019.8942962","DOIUrl":"https://doi.org/10.1109/phm-qingdao46334.2019.8942962","url":null,"abstract":"In the prognostic and health management of rotating machinery, the characteristic frequency of early weak fault is usually difficult to be extracted. To overcome this difficulty, this paper presents a weak fault feature enhancement method of acoustic data for rolling bearings based on variational mode decomposition (VMD). Firstly, the acoustic data is decomposed into some band-limited intrinsic mode functions (BLIMF) by the optimized VMD. Then an adaptive signal-to-noise ratio (ASNR) estimation method is proposed to determine the optimal BLIMF. Finally, the fault types of rolling bearings are identified through Hilbert envelope transform. Experimental results show that the presented method can effectively enhance the feature for early weak fault in rolling bearings with acoustic data.","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":"129652514","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-qingdao46334.2019.8942925
Tao Gao, Pu Chen, Mei Han
Maintenance efficiency is the key to modern high-tech warfare. As the basis of condition-based maintenance, PHM(prognostics and health management) technology greatly improves maintenance efficiency by enabling the system with prediction power. To provide theoretical foundation for new equipment maintenance reform, the impact of PHM technology on maintenance support mode was analyzed from the member-level and the regional-level on aspects of maintenance support system, maintenance support resources, maintenance analysis and decision-making process. It is hoped that the results of the proposed study may contribute to the current reform of support modes for aircraft maintenance.
维修效率是现代高技术战争的关键。PHM(prognostics and health management)技术作为基于状态维护的基础，通过赋予系统预测能力，极大地提高了维护效率。从维修保障体系、维修保障资源、维修分析与决策过程等方面，从成员层面和区域层面分析了PHM技术对新装备维修保障模式的影响，为新装备维修改革提供理论依据。希望所提出的研究结果可以对当前飞机维修支持模式的改革有所贡献。
{"title":"Study on the Influence of PHM Technology on Aircraft Maintenance Support Mode","authors":"Tao Gao, Pu Chen, Mei Han","doi":"10.1109/phm-qingdao46334.2019.8942925","DOIUrl":"https://doi.org/10.1109/phm-qingdao46334.2019.8942925","url":null,"abstract":"Maintenance efficiency is the key to modern high-tech warfare. As the basis of condition-based maintenance, PHM(prognostics and health management) technology greatly improves maintenance efficiency by enabling the system with prediction power. To provide theoretical foundation for new equipment maintenance reform, the impact of PHM technology on maintenance support mode was analyzed from the member-level and the regional-level on aspects of maintenance support system, maintenance support resources, maintenance analysis and decision-making process. It is hoped that the results of the proposed study may contribute to the current reform of support modes for aircraft maintenance.","PeriodicalId":259179,"journal":{"name":"2019 Prognostics and System Health Management Conference (PHM-Qingdao)","volume":"152 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":"124249087","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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