College of Electrical and Automation Engineering., Shandong University of Science and Technology, No. 579 Qianwangang Road, Qingdao 266590, China.
Comput Intell Neurosci. 2021 Sep 10;2021:1956394. doi: 10.1155/2021/1956394. eCollection 2021.
Aimed to address the low diagnostic accuracy caused by the similar data distribution of sensor partial faults, a sensor fault diagnosis method is proposed on the basis of Grey Wolf Optimization Support Vector Machine (-GWO-SVM) in this paper. Firstly, a fusion with Kernel Principal Component Analysis (KPCA) and time-domain parameters is performed to carry out the feature extraction and dimensionality reduction for fault data. Then, an improved Grey Wolf Optimization (GWO) algorithm is applied to enhance its global search capability while speeding up the convergence, for the purpose of further optimizing the parameters of SVM. Finally, the experimental results are obtained to suggest that the proposed method performs better in optimization than the other intelligent diagnosis algorithms based on SVM, which improves the accuracy of fault diagnosis effectively.
针对传感器部分故障数据分布相似导致诊断准确率低的问题，本文提出了一种基于灰狼优化支持向量机（Grey Wolf Optimization Support Vector Machine，GWO-SVM）的传感器故障诊断方法。首先，对故障数据进行核主成分分析（Kernel Principal Component Analysis，KPCA）和时域参数融合，实现特征提取和降维。然后，应用改进的灰狼优化（Grey Wolf Optimization，GWO）算法来提高其全局搜索能力并加快收敛速度，进一步优化支持向量机的参数。最后，通过实验结果表明，与基于支持向量机的其他智能诊断算法相比，该方法在优化方面表现更好，有效提高了故障诊断的准确性。
