电子学报 ›› 2022, Vol. 50 ›› Issue (3): 643-651.DOI: 10.12263/DZXB.20210047

• 学术论文 • 上一篇    下一篇

基于LSTM-DHMM的MOSFET器件健康状态识别与故障时间预测

张明宇1, 王琦1,2, 于洋1   

  1. 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
  • 收稿日期:2021-01-06 修回日期:2021-06-26 出版日期:2022-03-25 发布日期:2022-03-25
  • 作者简介:张明宇 女,1987年生于辽宁沈阳.博士研究生.主要研究方向为故障诊断技术、健康监测.E-mail:jiabingde@126.com
    王 琦 男,1965年生于吉林梅河口.教授、博士生导师.主要研究方向为计算机技术在环境监测管理中的应用、装备状态监测及故障诊断、航空发动机污染与测试技术.E-mail:wangqi@lnut.edu.cn
    于 洋 女,1967年生于辽宁沈阳.博士、教授、博士生导师.主要研究方向为装备故障监测与健康管理.E-mail:yuy@sut.edu.cn
  • 基金资助:
    中航创新基金(sh2012-18)

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

ZHANG Ming-yu1, WANG Qi1,2, YU Yang1   

  1. 1.Shool of Information Science and Engineering,Shenyang University of Technology,Shenyang,Liaoning 110870,China
    2.Liaoning University of Technology,Jinzhou,Liaoning 121001,China
  • Received:2021-01-06 Revised:2021-06-26 Online:2022-03-25 Published:2022-03-25

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.

关键词: 故障预测与健康管理, MOSFET(Metal-Oxide-Semiconductor Field-Effect Transistor), 长短时序列, 离散隐马尔可夫模型, 自回归模型, 故障时间

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.

Key words: prognostic and health management, MOSFET(Metal-Oxide-Semiconductor Field-Effect Transistor), long short-term memory, discrete hidden Markov model, autoregressive model, fault time

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