基于改进多层核超限学习机的模拟电路故障诊断

doi:10.3969/j.issn.1000-1093.2021.02.013

摘要/Abstract

摘要： 为兼顾模拟电路多故障诊断的实用性和诊断精度，基于仿真诊断模型的测试性应用框架，结合深度学习与核方法的优势，提出一种多层单纯形优化核超限学习机（ML-SOKELM）方法。将有效初选后的数据集输入多层核超限学习机逐层提取故障特征并进行诊断；训练过程中，将各层核参数向量视为待优化变量，运用单纯形法对其进行联合优化。实验结果表明：与常见的深度学习方法相比，ML-SOKELM方法对主观经验依赖性更低，在训练时间大大缩短的同时，还能获得与之相当的准确率；与流行的核方法相比，ML-SOKELM方法在不同模糊度阈值下均能获得较高的诊断准确率。

关键词: 模拟电路, 核超限学习机, 深度学习, 故障诊断, 模糊组

Abstract: A multi-layer simplex optimized kernel extreme learning machine (ML-SOKELM) method is proposed based on the testability application framework of simulation-based diagnostic model, which combines the deep learning and kernel-based method. ML-SOKELM method is used to to improve the practicability and accuracy of multi-fault diagnosis of analog circuits. The multi-layer kernel extreme learning machine (ML-KELM) extracts the fault features layer by layer and gets the diagnosed results with original data after primary selection. During the training process, the proposed method is used to optimize the kernel parameters of all layers. The diagnosed results show that, compared with the common deep learning methods, ML-SOKELM methed is less dependent on subjective experience and achieves considerable accuracy while greatly shortening the training time. ML-SOKELM can achieve higher diagnostic accuracy under different ambiguity thresholds in comparison with popular kernel-based methods.

Key words: analogcircuit, kernelextremelearningmachine, deeplearning, faultdiagnosis, ambiguitygroup

中图分类号:

TM133

朱敏，许爱强，许晴，李睿峰. 基于改进多层核超限学习机的模拟电路故障诊断[J]. 兵工学报, 2021, 42(2): 356-369.

ZHU Min， XU Aiqiang， XU Qing， LI Ruifeng. Fault Diagnosis of Analog Circuits Based on Improved Multilayer Kernel Extreme Learning Machine[J]. Acta Armamentarii, 2021, 42(2): 356-369.

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