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Open AccessArticle

Failure Type Prediction Using Physical Indices and Data Features for Solenoid Valve

1
School of Computer Science and Engineering, Central South University, Changsha 410075, China
2
School of Automation, Central South University, Changsha 410075, China
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Author to whom correspondence should be addressed.
This paper is an extended version of our paper published in 2019 10st International Conference on Prognostics and Health Management, Hyatt Regency San Francisco Airport, Burlingame, CA, USA, 17–19 June 2019.
Appl. Sci. 2020, 10(4), 1323; https://doi.org/10.3390/app10041323
Received: 2 January 2020 / Revised: 1 February 2020 / Accepted: 11 February 2020 / Published: 15 February 2020
(This article belongs to the Section Computing and Artificial Intelligence)
A high-speed solenoid valve is a key component of the braking system. Accurately predicting the failure type of the solenoid valve is an important guarantee for safe operation of the braking system. However, electrical, magnetic, and mechanical coupling aging mechanism; individual differences; and uncertainty of aging processes have remained major challenges. To address this problem, a method combining physical indices and data features is proposed to predict the failure type of solenoid valve. Firstly, the mechanism model of the solenoid valve is established and five physical indices are extracted from the driven current curve. Then, the frequency band energy characteristics are obtained from the current change rate curve of the solenoid valve by wavelet packet decomposition. Combining physical indices and frequency band energy characteristics into a comprehensive feature vector, we applied random forest to both predict and classify the failure type. We generate a data set consisting of 60 high-speed solenoid valves periodically switched under accelerated aging test conditions, including driven current, final failure type, and switching cycles. The prediction result shows that the proposed method achieves 95.95% and 94.68% precision for the two failures using the driven current data of the 3000th cycle and has better prediction performance than other algorithms. View Full-Text
Keywords: failure type prediction; high-speed solenoid valve; wavelet packet decomposition; random forest failure type prediction; high-speed solenoid valve; wavelet packet decomposition; random forest
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MDPI and ACS Style

Peng, J.; Tang, X.; Chen, B.; Jiang, F.; Yang, Y.; Zhang, R.; Gao, D.; Zhang, X.; Huang, Z. Failure Type Prediction Using Physical Indices and Data Features for Solenoid Valve. Appl. Sci. 2020, 10, 1323.

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