Pub Date : 2019-10-01DOI: 10.1109/phm-qingdao46334.2019.8942964
Dongdong Li, Qi Li, E. Mingcheng, Zengqiang Jiang, Jing Ma
Increased axle load, heavy load, and increased speed of railway wagons need high reliability of coupler knuckle. However, the failure analysis of coupler knuckle is troublesome under incomplete lifetime data due to extensive management. In this paper, maximum likelihood estimation method is employed to with a mixture data of left truncation, interval censoring, and right censoring. The parameter estimations are done by Newton-Raphson method and bootstrap resampling. The results of case study shows that the proposed method is reasonable and could reflect the reality. These results have important application value in reality for further refinement of the maintenance strategy according to the remaining life of components, thereby reducing the maintenance cost and improving the reliability of components.
{"title":"Failure Analysis of Coupler Knuckle Considering Truncated and Censored Lifetime Data","authors":"Dongdong Li, Qi Li, E. Mingcheng, Zengqiang Jiang, Jing Ma","doi":"10.1109/phm-qingdao46334.2019.8942964","DOIUrl":"https://doi.org/10.1109/phm-qingdao46334.2019.8942964","url":null,"abstract":"Increased axle load, heavy load, and increased speed of railway wagons need high reliability of coupler knuckle. However, the failure analysis of coupler knuckle is troublesome under incomplete lifetime data due to extensive management. In this paper, maximum likelihood estimation method is employed to with a mixture data of left truncation, interval censoring, and right censoring. The parameter estimations are done by Newton-Raphson method and bootstrap resampling. The results of case study shows that the proposed method is reasonable and could reflect the reality. These results have important application value in reality for further refinement of the maintenance strategy according to the remaining life of components, thereby reducing the maintenance cost and improving the reliability of components.","PeriodicalId":259179,"journal":{"name":"2019 Prognostics and System Health Management Conference (PHM-Qingdao)","volume":"777 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":"123282445","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-qingdao46334.2019.8942943
Shigang Zhang, Xu Luo, Lei Li, Yongmin Yang
Monitoring health status of equipment is very important for risk avoiding and maintenance decision making, especially for complex safety-critical systems. Most of existing fault diagnosis systems can only generate the state of a specific system level. Models should be developed to assess the health states of the equipment in different hierarchical levels. In this paper, a model based on Bayesian networks is proposed, where determined fault diagnosis result and the fault diagnosis result with uncertainty can all be used. The model structure, how to set uncertain diagnosis result by virtual nodes and how to represent multi-states are formulated and discussed in detail. An application example on a diesel engine combustion system is given, which shows that the method proposed in this paper can realize hierarchical health assessment, including the scenarios that the diagnosis result is uncertain.
{"title":"Hierarchical Health Assessment of Equipment with Uncertain Fault Diagnosis Result","authors":"Shigang Zhang, Xu Luo, Lei Li, Yongmin Yang","doi":"10.1109/phm-qingdao46334.2019.8942943","DOIUrl":"https://doi.org/10.1109/phm-qingdao46334.2019.8942943","url":null,"abstract":"Monitoring health status of equipment is very important for risk avoiding and maintenance decision making, especially for complex safety-critical systems. Most of existing fault diagnosis systems can only generate the state of a specific system level. Models should be developed to assess the health states of the equipment in different hierarchical levels. In this paper, a model based on Bayesian networks is proposed, where determined fault diagnosis result and the fault diagnosis result with uncertainty can all be used. The model structure, how to set uncertain diagnosis result by virtual nodes and how to represent multi-states are formulated and discussed in detail. An application example on a diesel engine combustion system is given, which shows that the method proposed in this paper can realize hierarchical health assessment, including the scenarios that the diagnosis result is uncertain.","PeriodicalId":259179,"journal":{"name":"2019 Prognostics and System Health Management Conference (PHM-Qingdao)","volume":"354 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":"131284023","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-qingdao46334.2019.8942885
Li-sha Zhu, Haonan Chen, Zunling Du, Zi-Sheng Lin, Yonghui He, Hairong Han
In order to study the effect of tooth surface wear on gear dynamics, based on the Archard wear model, considering the dynamic load distribution between teeth under the geometrical normal clearance and influence of the contact point on the tooth profile points in the surrounding area, an accurate wear model of tooth surface is established and the dynamic characteristics of gear are analyzed by coupling the wear of gear surface into the gear dynamics model, and a dynamic wear calculation model of gear surface is established. The results indicate that early wear has little effect on gear dynamics, with the increase of wear cycle, the vibration of the non-resonant region is increased, while in the resonance region first invariant and then increased.
{"title":"Investigation on the Influence of Dynamic Tooth Wear on Gear Dynamic Characteristics","authors":"Li-sha Zhu, Haonan Chen, Zunling Du, Zi-Sheng Lin, Yonghui He, Hairong Han","doi":"10.1109/phm-qingdao46334.2019.8942885","DOIUrl":"https://doi.org/10.1109/phm-qingdao46334.2019.8942885","url":null,"abstract":"In order to study the effect of tooth surface wear on gear dynamics, based on the Archard wear model, considering the dynamic load distribution between teeth under the geometrical normal clearance and influence of the contact point on the tooth profile points in the surrounding area, an accurate wear model of tooth surface is established and the dynamic characteristics of gear are analyzed by coupling the wear of gear surface into the gear dynamics model, and a dynamic wear calculation model of gear surface is established. The results indicate that early wear has little effect on gear dynamics, with the increase of wear cycle, the vibration of the non-resonant region is increased, while in the resonance region first invariant and then increased.","PeriodicalId":259179,"journal":{"name":"2019 Prognostics and System Health Management Conference (PHM-Qingdao)","volume":"26 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":"125679182","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-qingdao46334.2019.8943033
Juan Wen, Bosong Pan, Luping Luo, Kewen Zhang, Quanhui Wu
In the past decades, a host of fault diagnosis methodologies have been designed and successfully used for bearings. However, most of them still have two deficiencies. (1) Traditional methods extract and select features manually according to a specific issue, but these features may be not appropriate for other tasks, leading to performance degradation of fault diagnosis. (2) Many studies assume that the dataset for model learning obey the uniform distribution as the testing dataset do, which seldom accords with the practice. To remedy these problems, we devise a novel framework for bearing fault diagnosis. First, the raw condition monitoring data are converted to 2D images with continuous wavelet transform. Then the classification model is learned with these 2D images, during which the transfer learning scheme, deep adaptation networks, is introduced for adapting the deep model trained with source data for use in new but related target domain. The presented approach is demonstrated with bearing condition monitoring information, and the results indicate it can identify bearing faults effectively under different operational conditions and has a higher accuracy than conventional approaches.
{"title":"A New Bearing Fault Diagnosis Framework With Deep Adaptation Networks For Industrial Application","authors":"Juan Wen, Bosong Pan, Luping Luo, Kewen Zhang, Quanhui Wu","doi":"10.1109/phm-qingdao46334.2019.8943033","DOIUrl":"https://doi.org/10.1109/phm-qingdao46334.2019.8943033","url":null,"abstract":"In the past decades, a host of fault diagnosis methodologies have been designed and successfully used for bearings. However, most of them still have two deficiencies. (1) Traditional methods extract and select features manually according to a specific issue, but these features may be not appropriate for other tasks, leading to performance degradation of fault diagnosis. (2) Many studies assume that the dataset for model learning obey the uniform distribution as the testing dataset do, which seldom accords with the practice. To remedy these problems, we devise a novel framework for bearing fault diagnosis. First, the raw condition monitoring data are converted to 2D images with continuous wavelet transform. Then the classification model is learned with these 2D images, during which the transfer learning scheme, deep adaptation networks, is introduced for adapting the deep model trained with source data for use in new but related target domain. The presented approach is demonstrated with bearing condition monitoring information, and the results indicate it can identify bearing faults effectively under different operational conditions and has a higher accuracy than conventional approaches.","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":"130566046","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-qingdao46334.2019.8942825
Wang Chaobing, Wu Rongzhen, Zhang Long, Cai Binghuan, Yan Lewei, Yin Wenhao
Considering the diversity, complexity and uncertainty existing in bearing vibrations, an extended intelligent identification paradigm for bearing faults was proposed based on multiscale permutation entropy (MPE) and extension theory. MPE can reflect the random degree and detect the dynamic mutation of time series over subsequent scales, while extension theory provides an approach to address the extensibility and regularity of complicated problems. In the present paradigm, MPE was employed to compute the entropies over multiple scales as an original feature vector to represent bearing vibrations, which were then graded using Fisher ratio to choose the most informative features. The chosen features were exploited to determine the classical domain and joint domain of matter elements associated with various bearing health conditions. Bearing fault pattern was assigned to the one with maximum dependence degree among the afore-constructed matter elements. An experiment was conducted on an electrical motor involving four bearing conditions including normal, inner race, outer race and rolling element faults. The test was repeated 100 times with an averaged rate of 92.2% by the proposed method which outperforms the method using multiscale sample entropy and extension theory.
{"title":"Extented Intelligent Recognition of Rolling Bearing Early Faults Using Multiscale Permutation Entropy","authors":"Wang Chaobing, Wu Rongzhen, Zhang Long, Cai Binghuan, Yan Lewei, Yin Wenhao","doi":"10.1109/phm-qingdao46334.2019.8942825","DOIUrl":"https://doi.org/10.1109/phm-qingdao46334.2019.8942825","url":null,"abstract":"Considering the diversity, complexity and uncertainty existing in bearing vibrations, an extended intelligent identification paradigm for bearing faults was proposed based on multiscale permutation entropy (MPE) and extension theory. MPE can reflect the random degree and detect the dynamic mutation of time series over subsequent scales, while extension theory provides an approach to address the extensibility and regularity of complicated problems. In the present paradigm, MPE was employed to compute the entropies over multiple scales as an original feature vector to represent bearing vibrations, which were then graded using Fisher ratio to choose the most informative features. The chosen features were exploited to determine the classical domain and joint domain of matter elements associated with various bearing health conditions. Bearing fault pattern was assigned to the one with maximum dependence degree among the afore-constructed matter elements. An experiment was conducted on an electrical motor involving four bearing conditions including normal, inner race, outer race and rolling element faults. The test was repeated 100 times with an averaged rate of 92.2% by the proposed method which outperforms the method using multiscale sample entropy and extension theory.","PeriodicalId":259179,"journal":{"name":"2019 Prognostics and System Health Management Conference (PHM-Qingdao)","volume":"81 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":"124660905","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-qingdao46334.2019.8942886
Min Qian, Yan-Fu Li
Diabetes is a chronic disease affecting a large number of human population worldwide. Accurate prediction of blood glucose plays an important role for diabetic patients to control the blood glucose in the normal range. In this paper, we use four popular data driven prediction methods for multi-steps ahead prediction with only the historical glucose values as input. Moreover, experiments are carried out to gain insight of the forecast delay phenomenon in the prediction. The reasons leading to the prediction delay are investigated, with the aim to improve the practical value of blood glucose prediction.
{"title":"Glucose Level Prediction Based on Data Driven Method","authors":"Min Qian, Yan-Fu Li","doi":"10.1109/phm-qingdao46334.2019.8942886","DOIUrl":"https://doi.org/10.1109/phm-qingdao46334.2019.8942886","url":null,"abstract":"Diabetes is a chronic disease affecting a large number of human population worldwide. Accurate prediction of blood glucose plays an important role for diabetic patients to control the blood glucose in the normal range. In this paper, we use four popular data driven prediction methods for multi-steps ahead prediction with only the historical glucose values as input. Moreover, experiments are carried out to gain insight of the forecast delay phenomenon in the prediction. The reasons leading to the prediction delay are investigated, with the aim to improve the practical value of blood glucose prediction.","PeriodicalId":259179,"journal":{"name":"2019 Prognostics and System Health Management Conference (PHM-Qingdao)","volume":"45 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":"120950628","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-qingdao46334.2019.8943061
Zhigao Chen, R. Jiang, Yi-Rong Teng
This paper proposes an improved method to evaluate the quality of preventive maintenance. This method evaluates the quality of preventive maintenance by comparing the pseudo-failure rate and the actual failure rate after the maintenance point. When using the weighting method to establish the power-law model to fit the failure data before the maintenance point, we focus on its prediction effect. When the normal function weight and the negative exponential function weight are used to estimate the model parameters, it is found that the model with negative exponential function weight has better predictive ability. To improve the accuracy of the prediction, the parameters of the negative exponential weight function are optimized. When using the power-law model to model the failure data after maintenance, we pay attention to the fitting effect. In the subsequent case study, we used two methods to evaluate the quality of preventive maintenance of a fleet of 26 buses, and the results show that the improved method is more reasonable.
{"title":"An improved method for evaluating the preventive maintenance quality of buses","authors":"Zhigao Chen, R. Jiang, Yi-Rong Teng","doi":"10.1109/phm-qingdao46334.2019.8943061","DOIUrl":"https://doi.org/10.1109/phm-qingdao46334.2019.8943061","url":null,"abstract":"This paper proposes an improved method to evaluate the quality of preventive maintenance. This method evaluates the quality of preventive maintenance by comparing the pseudo-failure rate and the actual failure rate after the maintenance point. When using the weighting method to establish the power-law model to fit the failure data before the maintenance point, we focus on its prediction effect. When the normal function weight and the negative exponential function weight are used to estimate the model parameters, it is found that the model with negative exponential function weight has better predictive ability. To improve the accuracy of the prediction, the parameters of the negative exponential weight function are optimized. When using the power-law model to model the failure data after maintenance, we pay attention to the fitting effect. In the subsequent case study, we used two methods to evaluate the quality of preventive maintenance of a fleet of 26 buses, and the results show that the improved method is more reasonable.","PeriodicalId":259179,"journal":{"name":"2019 Prognostics and System Health Management Conference (PHM-Qingdao)","volume":"8 28 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":"120966366","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-qingdao46334.2019.8942908
Liping Zhao, Lin Long, Shangxiao Yang, Sheng Lin
Traction power supply system (TPSS) is an important part of the electric railway. Lightning is one of the important factors that endanger the safe operation of TPSS. The impact of lightning on TPSS can be divided into lightning fault and lightning disturbance. Due to lightning disturbance also generates high-frequency components, which cause the relay protection mistrip in traction substation. In this paper, wavelet energy moment is used to recognition lightning disturbance of TPSS. Firstly, in order to obtain transient signals of TPSS, simulation model of the TPSS is built and it simulates three kinds of transient signals, such as normal signals, lightning fault signals and lightning disturbance signals. Then the wavelet transform is used to extract the energy moments of each frequency band of the three types of signals. Thus, wavelet energy moment statistical graphs of three types of signals are obtained, the wavelet energy moment statistical graph is analyzed and its distribution characteristics are analyzed. Based on this, the lightning disturbance recognition criterion is proposed. Finally, the recognition criterion is verified by the simulation signals. The results show that the recognition method can effectively recognize the lightning disturbance signal of the TPSS.
{"title":"A Recognition Method for Lightning Disturbance in Traction Power Supply System Based on Wavelet Energy Moment","authors":"Liping Zhao, Lin Long, Shangxiao Yang, Sheng Lin","doi":"10.1109/phm-qingdao46334.2019.8942908","DOIUrl":"https://doi.org/10.1109/phm-qingdao46334.2019.8942908","url":null,"abstract":"Traction power supply system (TPSS) is an important part of the electric railway. Lightning is one of the important factors that endanger the safe operation of TPSS. The impact of lightning on TPSS can be divided into lightning fault and lightning disturbance. Due to lightning disturbance also generates high-frequency components, which cause the relay protection mistrip in traction substation. In this paper, wavelet energy moment is used to recognition lightning disturbance of TPSS. Firstly, in order to obtain transient signals of TPSS, simulation model of the TPSS is built and it simulates three kinds of transient signals, such as normal signals, lightning fault signals and lightning disturbance signals. Then the wavelet transform is used to extract the energy moments of each frequency band of the three types of signals. Thus, wavelet energy moment statistical graphs of three types of signals are obtained, the wavelet energy moment statistical graph is analyzed and its distribution characteristics are analyzed. Based on this, the lightning disturbance recognition criterion is proposed. Finally, the recognition criterion is verified by the simulation signals. The results show that the recognition method can effectively recognize the lightning disturbance signal of the TPSS.","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":"114968678","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-qingdao46334.2019.8943009
Yupeng Chen, Yang Zhao, K. Tsui
Passenger travel pattern analysis is essential for the design and development of public transport network. Nowadays, Automated Fare Collection (AFC) systems are widely exploited in the operation and management of public transportation. The data collected from AFC systems provide valuable information to analyze passenger behavior. This research aims to investigate passenger mobility patterns from both temporal and spatial perspectives. We present a hybrid topic-clustering method for extracting travel feature and grouping passengers based on their travel patterns. Our proposed method is illustrated using a real AFC dataset of the metro transportation system in Shenzhen, China. The results showed that four temporal travel patterns were well identified. Comparison of travel behavior indicated that metro travelers with different travel time selections also have different activity areas.
{"title":"Clustering-based Travel Pattern Recognition in Rail Transportation System Using Automated Fare Collection Data","authors":"Yupeng Chen, Yang Zhao, K. Tsui","doi":"10.1109/phm-qingdao46334.2019.8943009","DOIUrl":"https://doi.org/10.1109/phm-qingdao46334.2019.8943009","url":null,"abstract":"Passenger travel pattern analysis is essential for the design and development of public transport network. Nowadays, Automated Fare Collection (AFC) systems are widely exploited in the operation and management of public transportation. The data collected from AFC systems provide valuable information to analyze passenger behavior. This research aims to investigate passenger mobility patterns from both temporal and spatial perspectives. We present a hybrid topic-clustering method for extracting travel feature and grouping passengers based on their travel patterns. Our proposed method is illustrated using a real AFC dataset of the metro transportation system in Shenzhen, China. The results showed that four temporal travel patterns were well identified. Comparison of travel behavior indicated that metro travelers with different travel time selections also have different activity areas.","PeriodicalId":259179,"journal":{"name":"2019 Prognostics and System Health Management Conference (PHM-Qingdao)","volume":"28 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":"129489228","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-qingdao46334.2019.8942853
Chiming Guo, Yongsheng Bai, R. Peng
The maintenance decision needs to consider various aspects such as cost, safety, system condition etc. This paper presents a condition-based maintenance optimization model with a risk acceptance criterion under imperfect inspection. The inspection interval can adaptively change with the system condition. In order to make the maintenance decision more reasonable, the optimization model, which minimizes the expected long-term cost rate with safety constraints, also considers the influence of imperfect inspection. The method is illustrated through the case of a feeder pipe of a nuclear power plant. The results show that the safety constraint and measurement error cannot be ignored.
{"title":"Condition-Based Maintenance Optimization with Safety Constraints under Imperfect Inspection","authors":"Chiming Guo, Yongsheng Bai, R. Peng","doi":"10.1109/phm-qingdao46334.2019.8942853","DOIUrl":"https://doi.org/10.1109/phm-qingdao46334.2019.8942853","url":null,"abstract":"The maintenance decision needs to consider various aspects such as cost, safety, system condition etc. This paper presents a condition-based maintenance optimization model with a risk acceptance criterion under imperfect inspection. The inspection interval can adaptively change with the system condition. In order to make the maintenance decision more reasonable, the optimization model, which minimizes the expected long-term cost rate with safety constraints, also considers the influence of imperfect inspection. The method is illustrated through the case of a feeder pipe of a nuclear power plant. The results show that the safety constraint and measurement error cannot be ignored.","PeriodicalId":259179,"journal":{"name":"2019 Prognostics and System Health Management Conference (PHM-Qingdao)","volume":"85 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":"128875745","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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