Next Article in Journal
An FBG Optical Approach to Thermal Expansion Measurements under Hydrostatic Pressure
Next Article in Special Issue
Dimension-Factorized Range Migration Algorithm for Regularly Distributed Array Imaging
Previous Article in Journal
Loose Coupling of Wearable-Based INSs with Automatic Heading Evaluation
Article Menu
Issue 11 (November) cover image

Export Article

Open AccessArticle
Sensors 2017, 17(11), 2524; https://doi.org/10.3390/s17112524

Remaining Useful Life Estimation of Insulated Gate Biploar Transistors (IGBTs) Based on a Novel Volterra k-Nearest Neighbor Optimally Pruned Extreme Learning Machine (VKOPP) Model Using Degradation Data

1
School of Automation Engineering, University of Electronic Science and Technology of China, Chengdu 611731, China
2
Quanzhou Institute of Equipment Manufacturing, Haixi Institutes, Chinese Academy of Sciences, Quanzhou 362200, China
*
Author to whom correspondence should be addressed.
Received: 27 September 2017 / Revised: 28 October 2017 / Accepted: 30 October 2017 / Published: 3 November 2017
(This article belongs to the Special Issue Sensor Signal and Information Processing)
View Full-Text   |   Download PDF [6686 KB, uploaded 3 November 2017]   |  

Abstract

The insulated gate bipolar transistor (IGBT) is a kind of excellent performance switching device used widely in power electronic systems. How to estimate the remaining useful life (RUL) of an IGBT to ensure the safety and reliability of the power electronics system is currently a challenging issue in the field of IGBT reliability. The aim of this paper is to develop a prognostic technique for estimating IGBTs’ RUL. There is a need for an efficient prognostic algorithm that is able to support in-situ decision-making. In this paper, a novel prediction model with a complete structure based on optimally pruned extreme learning machine (OPELM) and Volterra series is proposed to track the IGBT’s degradation trace and estimate its RUL; we refer to this model as Volterra k-nearest neighbor OPELM prediction (VKOPP) model. This model uses the minimum entropy rate method and Volterra series to reconstruct phase space for IGBTs’ ageing samples, and a new weight update algorithm, which can effectively reduce the influence of the outliers and noises, is utilized to establish the VKOPP network; then a combination of the k-nearest neighbor method (KNN) and least squares estimation (LSE) method is used to calculate the output weights of OPELM and predict the RUL of the IGBT. The prognostic results show that the proposed approach can predict the RUL of IGBT modules with small error and achieve higher prediction precision and lower time cost than some classic prediction approaches. View Full-Text
Keywords: remaining useful life; IGBT; prediction model; VKOPP; degradation data remaining useful life; IGBT; prediction model; VKOPP; degradation data
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).

Share & Cite This Article

MDPI and ACS Style

Liu, Z.; Mei, W.; Zeng, X.; Yang, C.; Zhou, X. Remaining Useful Life Estimation of Insulated Gate Biploar Transistors (IGBTs) Based on a Novel Volterra k-Nearest Neighbor Optimally Pruned Extreme Learning Machine (VKOPP) Model Using Degradation Data. Sensors 2017, 17, 2524.

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]
Sensors EISSN 1424-8220 Published by MDPI AG, Basel, Switzerland RSS E-Mail Table of Contents Alert
Back to Top