A Prognostic Framework for Wheel Treads Integrating Parameter Correlation and Multiple Uncertainties
As crucial rotary components of high-speed trains, wheel treads in realistic operation environment usually suffer severe cyclic shocks, which damage the health status and ultimately cause safety risks. Timely and precise health prognosis based on vibration signals is an effective technology to mitig...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2020-01-01
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| Series: | Applied Sciences |
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| Online Access: | https://www.mdpi.com/2076-3417/10/2/467 |
| _version_ | 1819364941687160832 |
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| author | Guifa Huang Yu Zhao Han Wang Xiaobing Ma Deyao Tang |
| author_facet | Guifa Huang Yu Zhao Han Wang Xiaobing Ma Deyao Tang |
| author_sort | Guifa Huang |
| collection | DOAJ |
| description | As crucial rotary components of high-speed trains, wheel treads in realistic operation environment usually suffer severe cyclic shocks, which damage the health status and ultimately cause safety risks. Timely and precise health prognosis based on vibration signals is an effective technology to mitigate such risks. In this work, a new parameter-related Wiener process model is proposed to capture multiple uncertainties existed in on-site prognosis of wheel treads. The proposed model establishes a quantitative relationship between degradation rate and variations, and integrates uncertainties via heterogeneity analysis of both criterions. A maximum-likelihood-based method is presented to initialize the unknown model parameters, followed by a recursive update algorithm with fully utilization of historical lifetime information. An investigation of real-world wheel tread signals demonstrates the superiority of the proposed model in accuracy improvement. |
| first_indexed | 2024-12-24T23:06:57Z |
| format | Article |
| id | doaj.art-a55731a1f1de4d2b8a99d4940c457952 |
| institution | Directory Open Access Journal |
| issn | 2076-3417 |
| language | English |
| last_indexed | 2024-12-24T23:06:57Z |
| publishDate | 2020-01-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Applied Sciences |
| spelling | doaj.art-a55731a1f1de4d2b8a99d4940c4579522022-12-21T16:35:01ZengMDPI AGApplied Sciences2076-34172020-01-0110246710.3390/app10020467app10020467A Prognostic Framework for Wheel Treads Integrating Parameter Correlation and Multiple UncertaintiesGuifa Huang0Yu Zhao1Han Wang2Xiaobing Ma3Deyao Tang4School of Reliability and Systems Engineering, Beihang University, Beijing 100191, ChinaSchool of Reliability and Systems Engineering, Beihang University, Beijing 100191, ChinaSchool of Reliability and Systems Engineering, Beihang University, Beijing 100191, ChinaSchool of Reliability and Systems Engineering, Beihang University, Beijing 100191, ChinaBeijing Tangzhi Science and Technology Development Co., Ltd., Beijing 100043, ChinaAs crucial rotary components of high-speed trains, wheel treads in realistic operation environment usually suffer severe cyclic shocks, which damage the health status and ultimately cause safety risks. Timely and precise health prognosis based on vibration signals is an effective technology to mitigate such risks. In this work, a new parameter-related Wiener process model is proposed to capture multiple uncertainties existed in on-site prognosis of wheel treads. The proposed model establishes a quantitative relationship between degradation rate and variations, and integrates uncertainties via heterogeneity analysis of both criterions. A maximum-likelihood-based method is presented to initialize the unknown model parameters, followed by a recursive update algorithm with fully utilization of historical lifetime information. An investigation of real-world wheel tread signals demonstrates the superiority of the proposed model in accuracy improvement.https://www.mdpi.com/2076-3417/10/2/467condition monitoringremaining useful lifewiener process modelrecursive algorithmmultiple uncertaintywheel tread |
| spellingShingle | Guifa Huang Yu Zhao Han Wang Xiaobing Ma Deyao Tang A Prognostic Framework for Wheel Treads Integrating Parameter Correlation and Multiple Uncertainties Applied Sciences condition monitoring remaining useful life wiener process model recursive algorithm multiple uncertainty wheel tread |
| title | A Prognostic Framework for Wheel Treads Integrating Parameter Correlation and Multiple Uncertainties |
| title_full | A Prognostic Framework for Wheel Treads Integrating Parameter Correlation and Multiple Uncertainties |
| title_fullStr | A Prognostic Framework for Wheel Treads Integrating Parameter Correlation and Multiple Uncertainties |
| title_full_unstemmed | A Prognostic Framework for Wheel Treads Integrating Parameter Correlation and Multiple Uncertainties |
| title_short | A Prognostic Framework for Wheel Treads Integrating Parameter Correlation and Multiple Uncertainties |
| title_sort | prognostic framework for wheel treads integrating parameter correlation and multiple uncertainties |
| topic | condition monitoring remaining useful life wiener process model recursive algorithm multiple uncertainty wheel tread |
| url | https://www.mdpi.com/2076-3417/10/2/467 |
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