Remaining Useful Life Estimation Based on Asynchronous Multisource Monitoring Information Fusion
An asynchronous RUL fusion estimation algorithm is presented for the hidden degradation process with multiple asynchronous monitoring sensors based on multisource information fusion. Firstly, a state-space type model is established by modeling the stochastic degradation as a Wiener process and trans...
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| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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Wiley
2017-01-01
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| Series: | Journal of Control Science and Engineering |
| Online Access: | http://dx.doi.org/10.1155/2017/4139563 |
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| _version_ | 1832564502715957248 |
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| author | Yanyan Hu Shuai Qi Xiaoling Xue Kaixiang Peng |
| author_facet | Yanyan Hu Shuai Qi Xiaoling Xue Kaixiang Peng |
| author_sort | Yanyan Hu |
| collection | DOAJ |
| description | An asynchronous RUL fusion estimation algorithm is presented for the hidden degradation process with multiple asynchronous monitoring sensors based on multisource information fusion. Firstly, a state-space type model is established by modeling the stochastic degradation as a Wiener process and transforming asynchronous indirectly observations in the fusion period to the fusion time. The statistical characteristics of involved noises and their correlations are analyzed. Secondly, the estimate of the hidden degradation state is obtained by applying Kalman filtering with correlated noises to the established state-space model, where the synchronized observations are fused. Also, the unknown model parameters are recursively identified based on the Expectation-Maximization (EM) algorithm with the Generic Algorithm (GA) adopted to solve the maximization problem. Finally, the probability distribution of RUL is obtained using the fused degradation state estimation and the updated identification result of the model parameters. Simulation results show that the proposed fusion method has better performance than the RUL estimation with single sensor. |
| format | Article |
| id | doaj-art-c62c4f06eddd401c86c36eeffb3d9064 |
| institution | Kabale University |
| issn | 1687-5249 1687-5257 |
| language | English |
| publishDate | 2017-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Journal of Control Science and Engineering |
| spelling | doaj-art-c62c4f06eddd401c86c36eeffb3d90642025-02-03T01:10:59ZengWileyJournal of Control Science and Engineering1687-52491687-52572017-01-01201710.1155/2017/41395634139563Remaining Useful Life Estimation Based on Asynchronous Multisource Monitoring Information FusionYanyan Hu0Shuai Qi1Xiaoling Xue2Kaixiang Peng3Beijing Engineering Research Center of Industrial Spectrum Imaging, School of Automation and Electrical Engineering, University of Science and Technology Beijing, Beijing 100083, ChinaBeijing Engineering Research Center of Industrial Spectrum Imaging, School of Automation and Electrical Engineering, University of Science and Technology Beijing, Beijing 100083, ChinaBeijing Engineering Research Center of Industrial Spectrum Imaging, School of Automation and Electrical Engineering, University of Science and Technology Beijing, Beijing 100083, ChinaBeijing Engineering Research Center of Industrial Spectrum Imaging, School of Automation and Electrical Engineering, University of Science and Technology Beijing, Beijing 100083, ChinaAn asynchronous RUL fusion estimation algorithm is presented for the hidden degradation process with multiple asynchronous monitoring sensors based on multisource information fusion. Firstly, a state-space type model is established by modeling the stochastic degradation as a Wiener process and transforming asynchronous indirectly observations in the fusion period to the fusion time. The statistical characteristics of involved noises and their correlations are analyzed. Secondly, the estimate of the hidden degradation state is obtained by applying Kalman filtering with correlated noises to the established state-space model, where the synchronized observations are fused. Also, the unknown model parameters are recursively identified based on the Expectation-Maximization (EM) algorithm with the Generic Algorithm (GA) adopted to solve the maximization problem. Finally, the probability distribution of RUL is obtained using the fused degradation state estimation and the updated identification result of the model parameters. Simulation results show that the proposed fusion method has better performance than the RUL estimation with single sensor.http://dx.doi.org/10.1155/2017/4139563 |
| spellingShingle | Yanyan Hu Shuai Qi Xiaoling Xue Kaixiang Peng Remaining Useful Life Estimation Based on Asynchronous Multisource Monitoring Information Fusion Journal of Control Science and Engineering |
| title | Remaining Useful Life Estimation Based on Asynchronous Multisource Monitoring Information Fusion |
| title_full | Remaining Useful Life Estimation Based on Asynchronous Multisource Monitoring Information Fusion |
| title_fullStr | Remaining Useful Life Estimation Based on Asynchronous Multisource Monitoring Information Fusion |
| title_full_unstemmed | Remaining Useful Life Estimation Based on Asynchronous Multisource Monitoring Information Fusion |
| title_short | Remaining Useful Life Estimation Based on Asynchronous Multisource Monitoring Information Fusion |
| title_sort | remaining useful life estimation based on asynchronous multisource monitoring information fusion |
| url | http://dx.doi.org/10.1155/2017/4139563 |
| work_keys_str_mv | AT yanyanhu remainingusefullifeestimationbasedonasynchronousmultisourcemonitoringinformationfusion AT shuaiqi remainingusefullifeestimationbasedonasynchronousmultisourcemonitoringinformationfusion AT xiaolingxue remainingusefullifeestimationbasedonasynchronousmultisourcemonitoringinformationfusion AT kaixiangpeng remainingusefullifeestimationbasedonasynchronousmultisourcemonitoringinformationfusion |