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}
The traffic signal system plays an important role in smooth ongoing of traffic. The working of signal system will be based on the amount of traffic coming towards or passing across the junction. There must be some sort of communication needed to let the signal system know about the number of vehicles driving towards the signal point. Whenever there is a communication especially wireless, the chances of an attacker in the middle of communication can be more. To avoid attacks of the kind like same signal lasting for more time or same signal on right and left turns at the same time which might leads to vehicle crashes are to be detected and rectified for better working of the road systems. In this paper, we focus on detecting those attacks using different machine learning concepts and analyzed the results for better understanding of algorithms and their role in detecting attacks. We are applying the models on a real-time dataset and results are analyzed. Finally, the paper results out the best clustering algorithm to detect the attacks in traffic signal system data and models are compared under 4 different parameters.
{"title":"Clustering Methods for Identification of Attacks in IoT Based Traffic Signal System","authors":"Yunpeng Zhang, Chethana Dukkipati, Liang-Chieh Cheng","doi":"10.1109/SDPC.2019.00013","DOIUrl":"https://doi.org/10.1109/SDPC.2019.00013","url":null,"abstract":"The traffic signal system plays an important role in smooth ongoing of traffic. The working of signal system will be based on the amount of traffic coming towards or passing across the junction. There must be some sort of communication needed to let the signal system know about the number of vehicles driving towards the signal point. Whenever there is a communication especially wireless, the chances of an attacker in the middle of communication can be more. To avoid attacks of the kind like same signal lasting for more time or same signal on right and left turns at the same time which might leads to vehicle crashes are to be detected and rectified for better working of the road systems. In this paper, we focus on detecting those attacks using different machine learning concepts and analyzed the results for better understanding of algorithms and their role in detecting attacks. We are applying the models on a real-time dataset and results are analyzed. Finally, the paper results out the best clustering algorithm to detect the attacks in traffic signal system data and models are compared under 4 different parameters.","PeriodicalId":403595,"journal":{"name":"2019 International Conference on Sensing, Diagnostics, Prognostics, and Control (SDPC)","volume":"20 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":"132360487","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}
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}
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}
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}
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}
Hong-Mei Yan, H. Mu, X. Yi, Yuan-Yuan Yang, Guang-Liang Chen
A step-by-step fault diagnosis method based on Empirical Mode Decomposition (EMD) combined with Random Forest algorithm was proposed for actual requirements of rolling bearing vibration fault diagnosis. Firstly, the preliminary fault monitoring was carried out, and a Linear Support Vector Machine model was established by extracting the Permutation Entropy of vibration signals as characteristic parameters to judge whether the bearing was faulty or not. Then, the fault location identification and the fault degree determination were carried out, and high-dimensional characteristic parameters in time domain, frequency domain and time-frequency domain are respectively extracted as inputs of the Random Forest algorithm. Finally, through the step-by-step diagnostic test of rolling bearing vibration data, the results show that each step of diagnosis can achieve 100% diagnostic accuracy and appropriate training time, which proves that EMD and Random Forest have good effect on step-by-step fault diagnosis of rolling bearing.
{"title":"Step-by-step Fault Diagnosis of Rolling Bearings Based on EMD and Random Forest","authors":"Hong-Mei Yan, H. Mu, X. Yi, Yuan-Yuan Yang, Guang-Liang Chen","doi":"10.1109/SDPC.2019.00063","DOIUrl":"https://doi.org/10.1109/SDPC.2019.00063","url":null,"abstract":"A step-by-step fault diagnosis method based on Empirical Mode Decomposition (EMD) combined with Random Forest algorithm was proposed for actual requirements of rolling bearing vibration fault diagnosis. Firstly, the preliminary fault monitoring was carried out, and a Linear Support Vector Machine model was established by extracting the Permutation Entropy of vibration signals as characteristic parameters to judge whether the bearing was faulty or not. Then, the fault location identification and the fault degree determination were carried out, and high-dimensional characteristic parameters in time domain, frequency domain and time-frequency domain are respectively extracted as inputs of the Random Forest algorithm. Finally, through the step-by-step diagnostic test of rolling bearing vibration data, the results show that each step of diagnosis can achieve 100% diagnostic accuracy and appropriate training time, which proves that EMD and Random Forest have good effect on step-by-step fault diagnosis of rolling bearing.","PeriodicalId":403595,"journal":{"name":"2019 International Conference on Sensing, Diagnostics, Prognostics, and Control (SDPC)","volume":"1 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":"131071153","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 load simulator simulates the aerodynamic loads on actuators in flight in a laboratory. It is used to evaluate whether the performance of actuation system meets the aircraft requirements. The maneuverability and control precision of aircraft are attracting more and more attention with the development of aviation industry. Because of being a force closed-loop system, the load simulator control system is seriously influenced by the redundant force. Taking aim at this problem, this paper establishes a mathematical model of the control system and proposes using fuzzy adaptive PID control strategy to eliminate redundant force. Through the computer simulation analysis, it was demonstrated that this method is feasible in theory. The experimental results also demonstrated that the fuzzy adaptive PID control strategy can eliminate redundant force efficiently.
{"title":"Research on Load Simulator Control Methods for Aircraft Actuation System","authors":"Yong Zhou, Yubo Zhang, Jiakuan Gao","doi":"10.1109/SDPC.2019.00064","DOIUrl":"https://doi.org/10.1109/SDPC.2019.00064","url":null,"abstract":"The load simulator simulates the aerodynamic loads on actuators in flight in a laboratory. It is used to evaluate whether the performance of actuation system meets the aircraft requirements. The maneuverability and control precision of aircraft are attracting more and more attention with the development of aviation industry. Because of being a force closed-loop system, the load simulator control system is seriously influenced by the redundant force. Taking aim at this problem, this paper establishes a mathematical model of the control system and proposes using fuzzy adaptive PID control strategy to eliminate redundant force. Through the computer simulation analysis, it was demonstrated that this method is feasible in theory. The experimental results also demonstrated that the fuzzy adaptive PID control strategy can eliminate redundant force efficiently.","PeriodicalId":403595,"journal":{"name":"2019 International Conference on Sensing, Diagnostics, Prognostics, and Control (SDPC)","volume":"28 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":"131195210","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}
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