Health Status Identification and Fault Time Prediction of MOSFET Device Based on LSTM-DHMM

ZHANG Ming-yu; WANG Qi; YU Yang

doi:10.12263/DZXB.20210047

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Health Status Identification and Fault Time Prediction of MOSFET Device Based on LSTM-DHMM

PAPERS | 更新时间：2025-12-11

- Health Status Identification and Fault Time Prediction of MOSFET Device Based on LSTM-DHMM
- ACTA ELECTRONICA SINICA Vol. 50, Issue 3, Pages: 643-651(2022)
- 作者机构：
  
  1.沈阳工业大学信息科学与工程学院，辽宁沈阳 110870
  2.辽宁工业大学，辽宁锦州 121001
- 作者简介：
- 基金信息：
- DOI：10.12263/DZXB.20210047
  CLC： TH165+.3;TN386.1
- Received：06 January 2021，
  
  Revised：2021-06-26，
  
  Published：25 March 2022
- 稿件说明：
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张明宇,王琦,于洋.基于LSTM-DHMM的MOSFET器件健康状态识别与故障时间预测[J].电子学报,2022,50(03):643-651.

ZHANG Ming-yu,WANG Qi,YU Yang.Health Status Identification and Fault Time Prediction of MOSFET Device Based on LSTM-DHMM[J].ACTA ELECTRONICA SINICA,2022,50(03):643-651.
张明宇,王琦,于洋.基于LSTM-DHMM的MOSFET器件健康状态识别与故障时间预测[J].电子学报,2022,50(03):643-651. DOI： 10.12263/DZXB.20210047.

ZHANG Ming-yu,WANG Qi,YU Yang.Health Status Identification and Fault Time Prediction of MOSFET Device Based on LSTM-DHMM[J].ACTA ELECTRONICA SINICA,2022,50(03):643-651. DOI： 10.12263/DZXB.20210047.

摘要

针对MOSFET（Metal-Oxide-Semiconductor Field-Effect Transistor）器件故障预测与健康管理问题，提出了一种长短时记忆（Long Short-Term Memory

LSTM）算法与离散隐马尔可夫模型（Discrete Hidden Markov Model

DHMM）相结合的故障预测新方法.该方法利用LSTM算法预测器件状态发展趋势；用自回归（AutoRegressive

AR）模型提取故障信息特征；以DHMM建立特征向量和退化等级之间的映射关系；在LSTM-DHMM模型预测结果的基础上，结合失效阈值排除虚警并预测故障时间，预测误差小于10%，精度较高.与GRU-DHMM（Gated Recurrent Unit Discrete Hidden Markov Model）、GRU-SVM（Gated Recurrent Unit Support Vector Machine）、LSTM-SVM（Long Short-Term Memory Support Vector Machine）方法进行对比分析，结果表明，LSTM-DHMM的预测准确率高于其他三种方案，能有效识别实验器件健康状态、较好预测故障时间，具有有效性和优越性.

Abstract

Aiming at the problem of MOSFET（Metal-Oxide-Semiconductor Field-Effect Transistor） device prognostic and health management

a fault prediction method combining long short term memory(LSTM) algorithm and discrete hidden Markov model(DHMM) is proposed to identify the health status and predict the fault time of MOSFET devices. In this method

LSTM algorithm is used to predict the development trend of device state; autoregressive(AR) model is used as the feature extraction method; DHMM is used to establish the mapping relationship between feature vector and degradation level; based on the prediction results of LSTM-DHMM model

false alarm is eliminated and fault time is predicted by combining with the failure threshold. The prediction error is less than 10% and the accuracy is high. Compared with single-stress GRU-DHMM（Gated Recurrent Unit Discrete Hidden Markov Model）、GRU-SVM（Gated Recurrent Unit Support Vector Machine） and LSTM-SVM（Long Short-Term Memory Support Vector Machine）

the proposed method is superior to the other four schemes in prediction accuracy and rationality

the results show that the prediction accuracy of the proposed method is higher than that of the other three schemes

and the proposed method can effectively identify the health state of the experimental devices and predict the fault time well

which is effective and superior.

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