Aircraft Engine Prognostics Based on Informative Sensor Selection and Adaptive Degradation Modeling with Functional Principal Component Analysis
Engine prognostics are critical to improve safety, reliability, and operational efficiency of an aircraft. With the development in sensor technology, multiple sensors are embedded or deployed to monitor the health condition of the aircraft engine. Thus, the challenge of engine prognostics lies in ho...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2020-02-01
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| Series: | Sensors |
| Subjects: | |
| Online Access: | https://www.mdpi.com/1424-8220/20/3/920 |
| _version_ | 1798038693643026432 |
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| author | Bin Zhang Kai Zheng Qingqing Huang Song Feng Shangqi Zhou Yi Zhang |
| author_facet | Bin Zhang Kai Zheng Qingqing Huang Song Feng Shangqi Zhou Yi Zhang |
| author_sort | Bin Zhang |
| collection | DOAJ |
| description | Engine prognostics are critical to improve safety, reliability, and operational efficiency of an aircraft. With the development in sensor technology, multiple sensors are embedded or deployed to monitor the health condition of the aircraft engine. Thus, the challenge of engine prognostics lies in how to model and predict future health by appropriate utilization of these sensor information. In this paper, a prognostic approach is developed based on informative sensor selection and adaptive degradation modeling with functional data analysis. The presented approach selects sensors based on metrics and constructs health index to characterize engine degradation by fusing the selected informative sensors. Next, the engine degradation is adaptively modeled with the functional principal component analysis (FPCA) method and future health is prognosticated using the Bayesian inference. The prognostic approach is applied to run-to-failure data sets of C-MAPSS test-bed developed by NASA. Results show that the proposed method can effectively select the informative sensors and accurately predict the complex degradation of the aircraft engine. |
| first_indexed | 2024-04-11T21:43:40Z |
| format | Article |
| id | doaj.art-3ce3a11bc74c4908b75cfdb1767bc091 |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-04-11T21:43:40Z |
| publishDate | 2020-02-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-3ce3a11bc74c4908b75cfdb1767bc0912022-12-22T04:01:28ZengMDPI AGSensors1424-82202020-02-0120392010.3390/s20030920s20030920Aircraft Engine Prognostics Based on Informative Sensor Selection and Adaptive Degradation Modeling with Functional Principal Component AnalysisBin Zhang0Kai Zheng1Qingqing Huang2Song Feng3Shangqi Zhou4Yi Zhang5School of Advanced Manufacturing Engineering, Chongqing University of Posts and Telecommunications, Chongqing 400065, ChinaSchool of Advanced Manufacturing Engineering, Chongqing University of Posts and Telecommunications, Chongqing 400065, ChinaSchool of Automation, Chongqing University of Posts and Telecommunications, Chongqing 400065, ChinaSchool of Advanced Manufacturing Engineering, Chongqing University of Posts and Telecommunications, Chongqing 400065, ChinaSchool of Advanced Manufacturing Engineering, Chongqing University of Posts and Telecommunications, Chongqing 400065, ChinaSchool of Advanced Manufacturing Engineering, Chongqing University of Posts and Telecommunications, Chongqing 400065, ChinaEngine prognostics are critical to improve safety, reliability, and operational efficiency of an aircraft. With the development in sensor technology, multiple sensors are embedded or deployed to monitor the health condition of the aircraft engine. Thus, the challenge of engine prognostics lies in how to model and predict future health by appropriate utilization of these sensor information. In this paper, a prognostic approach is developed based on informative sensor selection and adaptive degradation modeling with functional data analysis. The presented approach selects sensors based on metrics and constructs health index to characterize engine degradation by fusing the selected informative sensors. Next, the engine degradation is adaptively modeled with the functional principal component analysis (FPCA) method and future health is prognosticated using the Bayesian inference. The prognostic approach is applied to run-to-failure data sets of C-MAPSS test-bed developed by NASA. Results show that the proposed method can effectively select the informative sensors and accurately predict the complex degradation of the aircraft engine.https://www.mdpi.com/1424-8220/20/3/920prognosticsaircraft enginesensor selectiondegradation modelingfunctional principal component analysisbayesian inference |
| spellingShingle | Bin Zhang Kai Zheng Qingqing Huang Song Feng Shangqi Zhou Yi Zhang Aircraft Engine Prognostics Based on Informative Sensor Selection and Adaptive Degradation Modeling with Functional Principal Component Analysis Sensors prognostics aircraft engine sensor selection degradation modeling functional principal component analysis bayesian inference |
| title | Aircraft Engine Prognostics Based on Informative Sensor Selection and Adaptive Degradation Modeling with Functional Principal Component Analysis |
| title_full | Aircraft Engine Prognostics Based on Informative Sensor Selection and Adaptive Degradation Modeling with Functional Principal Component Analysis |
| title_fullStr | Aircraft Engine Prognostics Based on Informative Sensor Selection and Adaptive Degradation Modeling with Functional Principal Component Analysis |
| title_full_unstemmed | Aircraft Engine Prognostics Based on Informative Sensor Selection and Adaptive Degradation Modeling with Functional Principal Component Analysis |
| title_short | Aircraft Engine Prognostics Based on Informative Sensor Selection and Adaptive Degradation Modeling with Functional Principal Component Analysis |
| title_sort | aircraft engine prognostics based on informative sensor selection and adaptive degradation modeling with functional principal component analysis |
| topic | prognostics aircraft engine sensor selection degradation modeling functional principal component analysis bayesian inference |
| url | https://www.mdpi.com/1424-8220/20/3/920 |
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