This paper presents an approach for reliability analysis of repairable systems with two-unit parallel structure considering Common Cause Failure (CCF) and maintenance correlation based on GO methodology. First, the GO algorithm for dealing with CCF is introduced. Then, the common cause failure probability formulas of two-unit parallel structure considering maintenance correlation are deduced based on Markov theory. Furthermore, the analysis process of the new GO method is formulated. Finally, the dynamic availability analysis of HTOSS is conducted by the GO method. And the result is compared with the result of system considering CCF, and the result of system without considering CCF and maintenance correlation. The results show that the CCF and maintenance correlation are not ignored for reliability analysis of such system. All in all, this study not only widens the application of GO method. But it also provides guidance and an approach for reliability analysis of repairable systems with two-unit parallel structure considering CCF and maintenance correlation.
{"title":"Reliability Analysis of Hydraulic Transmission Oil Supply System Considering Common Cause Failure and Maintenance Correlation with Success Oriented","authors":"Xinlei Wang, Hongwei Zhang, Zhe Wang, X. Yi","doi":"10.1109/SDPC.2019.00085","DOIUrl":"https://doi.org/10.1109/SDPC.2019.00085","url":null,"abstract":"This paper presents an approach for reliability analysis of repairable systems with two-unit parallel structure considering Common Cause Failure (CCF) and maintenance correlation based on GO methodology. First, the GO algorithm for dealing with CCF is introduced. Then, the common cause failure probability formulas of two-unit parallel structure considering maintenance correlation are deduced based on Markov theory. Furthermore, the analysis process of the new GO method is formulated. Finally, the dynamic availability analysis of HTOSS is conducted by the GO method. And the result is compared with the result of system considering CCF, and the result of system without considering CCF and maintenance correlation. The results show that the CCF and maintenance correlation are not ignored for reliability analysis of such system. All in all, this study not only widens the application of GO method. But it also provides guidance and an approach for reliability analysis of repairable systems with two-unit parallel structure considering CCF and maintenance correlation.","PeriodicalId":403595,"journal":{"name":"2019 International Conference on Sensing, Diagnostics, Prognostics, and Control (SDPC)","volume":"29 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115600546","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}
{"title":"SDPC 2019 TOC","authors":"Xinbo Qian, Lujie Zhao","doi":"10.1109/sdpc.2019.00004","DOIUrl":"https://doi.org/10.1109/sdpc.2019.00004","url":null,"abstract":"","PeriodicalId":403595,"journal":{"name":"2019 International Conference on Sensing, Diagnostics, Prognostics, and Control (SDPC)","volume":"30 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115693552","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}
In order to improve the reliability of The Control Rod Drive Mechanism (CRDM), a reliability trade-off method was proposed, which aims at the total cost-reliability. Firstly, the Goal-Oriented (GO) model of the CRDM was established. Then, the total cost-reliability composed of Research and Development (R&D) production cost and operation and maintenance cost was used as the objective function of the model. Finally, based on GO methodology and Genetic Algorithm (GA), the reliability trade-off optimization allocation results of the CRDM were obtained, which proves the feasibility of the proposed method.
{"title":"The Reliability Optimization Allocation Method of Control Rod Drive Mechanism Based on GO Method","authors":"H. Mu, Hong-Mei Yan, X. Yi, Yuan-Yuan Yang, Guang-Liang Chen, Xue Dong","doi":"10.1109/SDPC.2019.00176","DOIUrl":"https://doi.org/10.1109/SDPC.2019.00176","url":null,"abstract":"In order to improve the reliability of The Control Rod Drive Mechanism (CRDM), a reliability trade-off method was proposed, which aims at the total cost-reliability. Firstly, the Goal-Oriented (GO) model of the CRDM was established. Then, the total cost-reliability composed of Research and Development (R&D) production cost and operation and maintenance cost was used as the objective function of the model. Finally, based on GO methodology and Genetic Algorithm (GA), the reliability trade-off optimization allocation results of the CRDM were obtained, which proves the feasibility of the proposed method.","PeriodicalId":403595,"journal":{"name":"2019 International Conference on Sensing, Diagnostics, Prognostics, and Control (SDPC)","volume":"190 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114186577","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}
In order to predict lubrication oil degradation, it is important to analyze the degradation trajectory in detail. Lubrication oil degradation is influenced by numerous factors e.g. oil replenishment, oil filtering, operating conditions and system maintenance etc. that need to be considered for accurate degradation prediction. Degradation trajectory prediction provides the remaining useful life (RUL). Whereas the analysis of degradation influencing factors with their roles in prediction provides opportunity to extend or control the RUL. This paper analyzes the lubrication oil degradation trajectory under the influence of oil replenishment. We consider a data correction strategy and prognosis for lubrication oil degradation using the auto-regressive integrated moving-average (ARIMA) and Bayesian dynamic linear model (BDLM) approaches. Degradation data is generated using model-based simulations. The prediction models are then tested on the simulated degradation data set. This study exemplifies the method to find the underlying degradation model considering and identifying the degradation influencing factors.
{"title":"Lubrication Oil Degradation Trajectory Prognosis with ARIMA and Bayesian Models","authors":"M. Tanwar, N. Raghavan","doi":"10.1109/SDPC.2019.00114","DOIUrl":"https://doi.org/10.1109/SDPC.2019.00114","url":null,"abstract":"In order to predict lubrication oil degradation, it is important to analyze the degradation trajectory in detail. Lubrication oil degradation is influenced by numerous factors e.g. oil replenishment, oil filtering, operating conditions and system maintenance etc. that need to be considered for accurate degradation prediction. Degradation trajectory prediction provides the remaining useful life (RUL). Whereas the analysis of degradation influencing factors with their roles in prediction provides opportunity to extend or control the RUL. This paper analyzes the lubrication oil degradation trajectory under the influence of oil replenishment. We consider a data correction strategy and prognosis for lubrication oil degradation using the auto-regressive integrated moving-average (ARIMA) and Bayesian dynamic linear model (BDLM) approaches. Degradation data is generated using model-based simulations. The prediction models are then tested on the simulated degradation data set. This study exemplifies the method to find the underlying degradation model considering and identifying the degradation influencing factors.","PeriodicalId":403595,"journal":{"name":"2019 International Conference on Sensing, Diagnostics, Prognostics, and Control (SDPC)","volume":"8 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114195049","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}
Multi-label learning algorithm handles cases in which each sample is related with several labels synchronously. As is known to all, each label might possess its own peculiarities, such as LIFT algorithm, i.e. multi-label learning with Label-specific Features. It constructs feature by performing cluster techniques based on negative and positive training samples of each label. However, the main drawback of this kind of algorithm is the large amounts of irrelevant features or redundant features in its feature space. To solve this problem, this paper puts forward an effective algorithm named LEFS, i.e. multi-label Feature Selection based on Label-specific features with fuzzy Entropy. The approaches proposed are examined on the two realistic multi-label benchmark data sets, which are compared with several multi-label learning approaches. A few features are selected from original features to fed classifier, but they remain the same or even slightly improve accuracy from 91.82% to 92.49% on data set- Medical. Results of another data sets are similar to that of the Medical. Experiment results show that these approaches can not only decrease the dimension of the construct features, but also gain an effective classification performance compared with three well-established multi-label learning approaches.
{"title":"Multi-label Feature Selection based on Label-specific Features","authors":"Zhijian Yin, Xingxing Li, Hualin Zhan","doi":"10.1109/SDPC.2019.00137","DOIUrl":"https://doi.org/10.1109/SDPC.2019.00137","url":null,"abstract":"Multi-label learning algorithm handles cases in which each sample is related with several labels synchronously. As is known to all, each label might possess its own peculiarities, such as LIFT algorithm, i.e. multi-label learning with Label-specific Features. It constructs feature by performing cluster techniques based on negative and positive training samples of each label. However, the main drawback of this kind of algorithm is the large amounts of irrelevant features or redundant features in its feature space. To solve this problem, this paper puts forward an effective algorithm named LEFS, i.e. multi-label Feature Selection based on Label-specific features with fuzzy Entropy. The approaches proposed are examined on the two realistic multi-label benchmark data sets, which are compared with several multi-label learning approaches. A few features are selected from original features to fed classifier, but they remain the same or even slightly improve accuracy from 91.82% to 92.49% on data set- Medical. Results of another data sets are similar to that of the Medical. Experiment results show that these approaches can not only decrease the dimension of the construct features, but also gain an effective classification performance compared with three well-established multi-label learning approaches.","PeriodicalId":403595,"journal":{"name":"2019 International Conference on Sensing, Diagnostics, Prognostics, and Control (SDPC)","volume":"95 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124741834","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}
Yun-peng Cao, Pan Hu, Kehui Zeng, Shuying Li, B. He, Weixing Feng
A prediction algorithm for the remaining useful life (RUL) of gas turbine based on the health degree (HD) is proposed in the paper. According to the historical data of the monitoring parameters, the degradation trend of the gas turbine and parameters can be obtained to achieve the purpose of predicting the remaining useful life, and provide the basis for subsequent fault diagnosis and maintenance work. Firstly, the fuzzy analytic hierarchy process (FAHP) is used to construct the calculation model of gas turbine HD. Secondly, the acceleration change point analysis method is combined with the kernel density estimation method to determine the gas turbine fault threshold. On this basis, this paper proposes a new prediction algorithm-- the splicing prediction algorithm based on HD and establishes the RUL prediction model of the gas turbine. Finally, the test data set in C-MAPSS is used for case analysis, and the predicted RUL is compared with the real value to obtain the prediction accuracy. The results show that the proposed prediction algorithm can predict the RUL of some data that meets the degradation detection, and the prediction accuracy is 86.67%, which proves the validity and feasibility of the proposed method.
{"title":"A Coupling Prediction Algorithm for Gas Turbine Remaining Useful Life Based on Health Degree","authors":"Yun-peng Cao, Pan Hu, Kehui Zeng, Shuying Li, B. He, Weixing Feng","doi":"10.1109/SDPC.2019.00094","DOIUrl":"https://doi.org/10.1109/SDPC.2019.00094","url":null,"abstract":"A prediction algorithm for the remaining useful life (RUL) of gas turbine based on the health degree (HD) is proposed in the paper. According to the historical data of the monitoring parameters, the degradation trend of the gas turbine and parameters can be obtained to achieve the purpose of predicting the remaining useful life, and provide the basis for subsequent fault diagnosis and maintenance work. Firstly, the fuzzy analytic hierarchy process (FAHP) is used to construct the calculation model of gas turbine HD. Secondly, the acceleration change point analysis method is combined with the kernel density estimation method to determine the gas turbine fault threshold. On this basis, this paper proposes a new prediction algorithm-- the splicing prediction algorithm based on HD and establishes the RUL prediction model of the gas turbine. Finally, the test data set in C-MAPSS is used for case analysis, and the predicted RUL is compared with the real value to obtain the prediction accuracy. The results show that the proposed prediction algorithm can predict the RUL of some data that meets the degradation detection, and the prediction accuracy is 86.67%, which proves the validity and feasibility of the proposed method.","PeriodicalId":403595,"journal":{"name":"2019 International Conference on Sensing, Diagnostics, Prognostics, and Control (SDPC)","volume":"2 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124800002","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}
In order to quantitatively evaluate the circuit reliability design level of board-level electronic products, based on the four influencing factors of electrical stress derating design, tolerance design, signal/power integrity design and key function circuit design, the circuit reliability design evaluation method for board-level electronic products is proposed and application cases are given Firstly, based on the functional performance requirements and design information of the board-level circuit, the circuit reliability design evaluation criteria are proposed. Then, the evaluation parameters are extracted through simulation, testing, etc., and the reliability design level of the circuit is analyzed, and the quantitative evaluation results are given. Finally, the method is applied in the actual circuit, which proves the feasibility and effectiveness of the method.
{"title":"Evaluation Method for Circuit Reliability Design of Board-level Electronic Products","authors":"C. Zhang, Fengming Lu, Wenzheng Xu","doi":"10.1109/SDPC.2019.00077","DOIUrl":"https://doi.org/10.1109/SDPC.2019.00077","url":null,"abstract":"In order to quantitatively evaluate the circuit reliability design level of board-level electronic products, based on the four influencing factors of electrical stress derating design, tolerance design, signal/power integrity design and key function circuit design, the circuit reliability design evaluation method for board-level electronic products is proposed and application cases are given Firstly, based on the functional performance requirements and design information of the board-level circuit, the circuit reliability design evaluation criteria are proposed. Then, the evaluation parameters are extracted through simulation, testing, etc., and the reliability design level of the circuit is analyzed, and the quantitative evaluation results are given. Finally, the method is applied in the actual circuit, which proves the feasibility and effectiveness of the method.","PeriodicalId":403595,"journal":{"name":"2019 International Conference on Sensing, Diagnostics, Prognostics, and Control (SDPC)","volume":"8 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128042175","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}
The structure of the tank fire control system is complex, the fault information acquisition is difficult, and the fault features are more, the maintenance cost is high, and the fault prediction and health management problems need to be solved urgently. The machine learning algorithm of support vector classifier is used to predict the fault of the fire control computer and sensor subsystem. In order to better carry out the fire control system health management, the fault prediction of the fire control system not only stays in the identification of the "normal" and "fault" states, but also distinguishes different types of fault states. The least squares support vector multiclassifier based on decision directed acyclic graph is selected for prediction. The improved separation measure is introduced to improve the decision directed acyclic graph, which reduces the error caused by improper initial sequence. The particle swarm optimization algorithm is used to optimize the parameters of the least squares support vector classifier, which improves the classification accuracy. The experimental test of the tank fire control computer proves that the proposed method has high reliability and effectiveness.
{"title":"Fault Prediction Algorithm for Fire Control System Based on Improved Support Vector Machine","authors":"Yingshun Li, Wei-Zhou Jia, X. Yi","doi":"10.1109/SDPC.2019.00016","DOIUrl":"https://doi.org/10.1109/SDPC.2019.00016","url":null,"abstract":"The structure of the tank fire control system is complex, the fault information acquisition is difficult, and the fault features are more, the maintenance cost is high, and the fault prediction and health management problems need to be solved urgently. The machine learning algorithm of support vector classifier is used to predict the fault of the fire control computer and sensor subsystem. In order to better carry out the fire control system health management, the fault prediction of the fire control system not only stays in the identification of the \"normal\" and \"fault\" states, but also distinguishes different types of fault states. The least squares support vector multiclassifier based on decision directed acyclic graph is selected for prediction. The improved separation measure is introduced to improve the decision directed acyclic graph, which reduces the error caused by improper initial sequence. The particle swarm optimization algorithm is used to optimize the parameters of the least squares support vector classifier, which improves the classification accuracy. The experimental test of the tank fire control computer proves that the proposed method has high reliability and effectiveness.","PeriodicalId":403595,"journal":{"name":"2019 International Conference on Sensing, Diagnostics, Prognostics, and Control (SDPC)","volume":"11 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121230053","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}
The safety boundary of Aero-engine performance parameters is one of the essential criteria for measuring aero-engine performance. However, due to the differences among individuals and discrepancies among the working environments, the fixed theoretical boundary is no longer sufficient for engineering needs. In this paper, a method based on fuzzy C-means (FCM) and Extreme Learning Machine (ELM) is proposed to extract and predict the safety boundary for aero-engine performance parameters. Firstly, the residuals between the predicted values and the actual values are used as the quantitative basis to extract the safe boundary. And then the ELM algorithm is used to forecast the safety boundary for next period of time. The method mentioned in this paper enhances the accuracy and generalization of safety boundary due to improvement for specific situations. The effectiveness of this method has been verified by simulation case.
{"title":"Safety Boundary Extraction Using FCM and Prediction Using ELM for Aero-engine Performance Parameters*","authors":"Yingshun Li, Danyang Li, Ximing Sun, X. Yi","doi":"10.1109/SDPC.2019.00012","DOIUrl":"https://doi.org/10.1109/SDPC.2019.00012","url":null,"abstract":"The safety boundary of Aero-engine performance parameters is one of the essential criteria for measuring aero-engine performance. However, due to the differences among individuals and discrepancies among the working environments, the fixed theoretical boundary is no longer sufficient for engineering needs. In this paper, a method based on fuzzy C-means (FCM) and Extreme Learning Machine (ELM) is proposed to extract and predict the safety boundary for aero-engine performance parameters. Firstly, the residuals between the predicted values and the actual values are used as the quantitative basis to extract the safe boundary. And then the ELM algorithm is used to forecast the safety boundary for next period of time. The method mentioned in this paper enhances the accuracy and generalization of safety boundary due to improvement for specific situations. The effectiveness of this method has been verified by simulation case.","PeriodicalId":403595,"journal":{"name":"2019 International Conference on Sensing, Diagnostics, Prognostics, and Control (SDPC)","volume":"21 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127812875","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}
The gas path parameter deviations as crucial parameters can assist each airline to realize the performance state trend analysis, life prediction and fault diagnosis of aero-engine. However, the calculation of gas path parameter deviations is complicated and the calculation models are also mastered by the original equipment manufacturer (OEM), which makes it burdensome for airlines to independently analyze the gas path performance of the aeroengine. At present, airlines have accumulated a large number of samples of gas path parameter deviations, which makes it possible to establish a regression model between gas path parameters and its deviations by data-driven method. In order to enhance the analysis capability of airline in gas path performance, we apply the residual learning blocks to the back propagation (BP) neural network based on the learning mechanism of the residual networks (ResNets). According to the solution characteristics of gas path parameter deviations, the regression models for the gas path parameter deviations are established based on Res-BP neural network. The screening for nonlinear independent variables of regression model is carried out by mean impact value (MIV) method, and then the input and output of Res-BP neural network can be determined. After the regression model training, the test set is tested by the proposed regression model. By comparing with BP neural network regression model and traditional regression model, the proposed regression model manifests higher prediction accuracy and generalization performance on the three key gas path parameter deviations, which is of great guiding significance for the aero-engine condition monitoring.
{"title":"Regression Model for Civil Aero-engine Gas Path Parameter Deviations Based on Res-BP Neural Network","authors":"Xingjie Zhou, Xu-yun Fu, Minghang Zhao, S. Zhong","doi":"10.1109/SDPC.2019.00042","DOIUrl":"https://doi.org/10.1109/SDPC.2019.00042","url":null,"abstract":"The gas path parameter deviations as crucial parameters can assist each airline to realize the performance state trend analysis, life prediction and fault diagnosis of aero-engine. However, the calculation of gas path parameter deviations is complicated and the calculation models are also mastered by the original equipment manufacturer (OEM), which makes it burdensome for airlines to independently analyze the gas path performance of the aeroengine. At present, airlines have accumulated a large number of samples of gas path parameter deviations, which makes it possible to establish a regression model between gas path parameters and its deviations by data-driven method. In order to enhance the analysis capability of airline in gas path performance, we apply the residual learning blocks to the back propagation (BP) neural network based on the learning mechanism of the residual networks (ResNets). According to the solution characteristics of gas path parameter deviations, the regression models for the gas path parameter deviations are established based on Res-BP neural network. The screening for nonlinear independent variables of regression model is carried out by mean impact value (MIV) method, and then the input and output of Res-BP neural network can be determined. After the regression model training, the test set is tested by the proposed regression model. By comparing with BP neural network regression model and traditional regression model, the proposed regression model manifests higher prediction accuracy and generalization performance on the three key gas path parameter deviations, which is of great guiding significance for the aero-engine condition monitoring.","PeriodicalId":403595,"journal":{"name":"2019 International Conference on Sensing, Diagnostics, Prognostics, and Control (SDPC)","volume":"9 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128987098","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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