School of Automation Engineering, University of Electronic Science and Technology of China, Chengdu 611731, China; Department of Communication Engineering, Chengdu Technological University, Chengdu 611731, China.
School of Automation Engineering, University of Electronic Science and Technology of China, Chengdu 611731, China.
Comput Intell Neurosci. 2016;2016:7657054. doi: 10.1155/2016/7657054. Epub 2016 Sep 8.
This paper presents a novel fault diagnosis method for analog circuits using ensemble empirical mode decomposition (EEMD), relative entropy, and extreme learning machine (ELM). First, nominal and faulty response waveforms of a circuit are measured, respectively, and then are decomposed into intrinsic mode functions (IMFs) with the EEMD method. Second, through comparing the nominal IMFs with the faulty IMFs, kurtosis and relative entropy are calculated for each IMF. Next, a feature vector is obtained for each faulty circuit. Finally, an ELM classifier is trained with these feature vectors for fault diagnosis. Via validating with two benchmark circuits, results show that the proposed method is applicable for analog fault diagnosis with acceptable levels of accuracy and time cost.
本文提出了一种基于集合经验模态分解（EEMD）、相对熵和极限学习机（ELM）的模拟电路故障诊断新方法。首先，分别测量电路的标称和故障响应波形，然后使用 EEMD 方法将其分解为固有模态函数（IMF）。其次，通过比较标称 IMF 和故障 IMF，计算每个 IMF 的峭度和相对熵。接下来，为每个故障电路获得一个特征向量。最后，使用这些特征向量训练 ELM 分类器进行故障诊断。通过验证两个基准电路，结果表明该方法适用于模拟电路故障诊断，具有可接受的准确性和时间成本水平。
