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 (α-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.

中文翻译：

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基于-灰狼优化-支持向量机的传感器故障诊断方法

针对传感器局部故障数据分布相似导致诊断准确率低的问题，提出一种基于灰狼优化支持向量机( α)的传感器故障诊断方法。-GWO-SVM）在本文中。首先，融合核主成分分析（KPCA）和时域参数，对故障数据进行特征提取和降维。然后，应用改进的灰狼优化（GWO）算法在加速收敛的同时增强其全局搜索能力，以进一步优化支持向量机的参数。最后，实验结果表明，该方法在优化上优于其他基于支持向量机的智能诊断算法，有效提高了故障诊断的准确性。