Next Article in Journal
Generation Z Satisfaction with Smart Homestays: ASCI and Web Crawler Insights from China
Previous Article in Journal
A Robust Trajectory Multi-Bernoulli Filter for Superpositional Sensors
 
 
Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
Journals
Electronics
Volume 13
Issue 20
10.3390/electronics13204002
electronics-logo
Submit to this Journal Review for this Journal Propose a Special Issue
► Article Menu

Article Menu

share Share announcement Help format_quote Cite question_answer Discuss in SciProfiles
first_page
settings
Order Article Reprints
Font Type:
Arial Georgia Verdana
Font Size:
Aa Aa Aa
Line Spacing:
Column Width:
Background:
Article

An Analytical Approach for IGBT Life Prediction Using Successive Variational Mode Decomposition and Bidirectional Long Short-Term Memory Networks

by
Kaitian Deng
1,
Xianglian Xu
1,*,
Fang Yuan
2,*,
Tianyu Zhang
1,
Yuli Xu
1,
Tunzhen Xie
1,
Yuanqing Song
1 and
Ruiqing Zhao
1
1
School of Automation, Wuhan University of Technology, Wuhan 430070, China
2
School of Power and Mechanical Engineering, Wuhan University, Wuhan 430072, China
*
Authors to whom correspondence should be addressed.
Electronics 2024, 13(20), 4002; https://doi.org/10.3390/electronics13204002
Submission received: 10 September 2024 / Revised: 3 October 2024 / Accepted: 8 October 2024 / Published: 11 October 2024
Browse Figures
Versions Notes

Abstract

The precise estimation of the operational lifespan of insulated gate bipolar transistors (IGBT) holds paramount significance for ensuring the efficient and uncompromised safety of industrial equipment. However, numerous methodologies and models currently employed for this purpose often fall short of delivering highly accurate predictions. The analytical approach that combines the Pattern Optimization Algorithm (POA) with Successive Variational Mode Decomposition (SVMD) and Bidirectional Long Short-term Memory (BiLSTM) network is introduced. Firstly, SVMD is employed as an unsupervised feature learning method to partition the data into intrinsic modal functions (IMFs), which are used to eliminate noise and preserve the essential signal. Secondly, the BiLSTM network is integrated for supervised learning purposes, enabling the prediction of the decomposed sequence. Additionally, the hyperparameters of BiLSTM and the penalty coefficients of SVMD are optimized utilizing the POA technique. Subsequently, the various modal functions are predicted utilizing the trained prediction model, and the individual mode predictions are subsequently aggregated to yield the model’s definitive final life prediction. Through case studies involving IGBT aging datasets, the optimal prediction model was formulated and its lifespan prediction capability was validated. The superiority of the proposed method is demonstrated by comparing it with benchmark models and other state-of-the-art methods.
Keywords: IGBT life prediction; successive variational mode decomposition; pelican optimization algorithm; bidirectional long short-term memory IGBT life prediction; successive variational mode decomposition; pelican optimization algorithm; bidirectional long short-term memory

Share and Cite

MDPI and ACS Style

Deng, K.; Xu, X.; Yuan, F.; Zhang, T.; Xu, Y.; Xie, T.; Song, Y.; Zhao, R. An Analytical Approach for IGBT Life Prediction Using Successive Variational Mode Decomposition and Bidirectional Long Short-Term Memory Networks. Electronics 2024, 13, 4002. https://doi.org/10.3390/electronics13204002

AMA Style

Deng K, Xu X, Yuan F, Zhang T, Xu Y, Xie T, Song Y, Zhao R. An Analytical Approach for IGBT Life Prediction Using Successive Variational Mode Decomposition and Bidirectional Long Short-Term Memory Networks. Electronics. 2024; 13(20):4002. https://doi.org/10.3390/electronics13204002

Chicago/Turabian Style

Deng, Kaitian, Xianglian Xu, Fang Yuan, Tianyu Zhang, Yuli Xu, Tunzhen Xie, Yuanqing Song, and Ruiqing Zhao. 2024. "An Analytical Approach for IGBT Life Prediction Using Successive Variational Mode Decomposition and Bidirectional Long Short-Term Memory Networks" Electronics 13, no. 20: 4002. https://doi.org/10.3390/electronics13204002

APA Style

Deng, K., Xu, X., Yuan, F., Zhang, T., Xu, Y., Xie, T., Song, Y., & Zhao, R. (2024). An Analytical Approach for IGBT Life Prediction Using Successive Variational Mode Decomposition and Bidirectional Long Short-Term Memory Networks. Electronics, 13(20), 4002. https://doi.org/10.3390/electronics13204002

Note that from the first issue of 2016, this journal uses article numbers instead of page numbers. See further details here.

Article Metrics

Back to TopTop