Next Article in Journal
Effective Crack Damage Detection Using Multilayer Sparse Feature Representation and Incremental Extreme Learning Machine
Next Article in Special Issue
Application of Self-Organizing Neural Networks to Electrical Fault Classification in Induction Motors
Previous Article in Journal
Microencapsulation of Tomato (Solanum lycopersicum L.) Pomace Ethanolic Extract by Spray Drying: Optimization of Process Conditions
Previous Article in Special Issue
Fault Prediction Model of High-Power Switching Device in Urban Railway Traction Converter with Bi-Directional Fatigue Data and Weighted LSM
Article Menu
Issue 3 (February-1) cover image

Export Article

Open AccessArticle
Appl. Sci. 2019, 9(3), 613; https://doi.org/10.3390/app9030613

A Stochastic Deterioration Process Based Approach for Micro Switches Remaining Useful Life Estimation

1
School of Mechatronic Engineering, Changchun University of Technology, Changchun 130012, China
2
Crrc Changchun Rail Way Vehicles Co., Ltd., Changchun 130012, China
3
Key Laboratory of Bionic Engineering of Ministry of Education, Jilin University, Changchun 130025, China
4
COSMA Automotive (Shanghai) CO., LTD., Changchun 130000, China
*
Author to whom correspondence should be addressed.
Received: 25 December 2018 / Revised: 29 January 2019 / Accepted: 8 February 2019 / Published: 12 February 2019
(This article belongs to the Special Issue Fault Detection and Diagnosis in Mechatronics Systems)
Full-Text   |   PDF [6557 KB, uploaded 13 February 2019]   |  
  |   Review Reports

Abstract

Real-time prediction of remaining useful life (RUL) is one of the most essential works in prognostics and health management (PHM) of the micro-switches. In this paper, a linear degradation model based on an inverse Kalman filter to imitate the stochastic deterioration process is proposed. First, Bayesian posterior estimation and expectation maximization (EM) algorithm are used to estimate the stochastic parameters. Second, an inverse Kalman filter is delivered to solve the errors in the initial parameters. In order to improve the accuracy of estimating nonlinear data, the strong tracking filtering (STF) method is used on the basis of Bayesian updating Third, the effectiveness of the proposed approach is validated on an experimental data relating to micro-switches for the rail vehicle. Additionally, it proposes another two methods for comparison to illustrate the effectiveness of the method with an inverse Kalman filter in this paper. In conclusion, a linear degradation model based on an inverse Kalman filter shall deal with errors in RUL estimation of the micro-switches excellently. View Full-Text
Keywords: micro-switches; remaining useful life; linear degradation model; inverse Kalman filter micro-switches; remaining useful life; linear degradation model; inverse Kalman filter
Figures

Figure 1

This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).
SciFeed

Share & Cite This Article

MDPI and ACS Style

Zhang, B.; Shao, Y.; Chang, Z.; Sun, Z.; Sui, Y. A Stochastic Deterioration Process Based Approach for Micro Switches Remaining Useful Life Estimation. Appl. Sci. 2019, 9, 613.

Show more citation formats Show less citations formats

Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Related Articles

Article Metrics

Article Access Statistics

1

Comments

[Return to top]
Appl. Sci. EISSN 2076-3417 Published by MDPI AG, Basel, Switzerland RSS E-Mail Table of Contents Alert
Back to Top