In recent years, machine learning techniques have been applied to test the fault type in high-voltage circuit breakers (HVCBs). Most related research involves in improving the classification method for higher precision. Nevertheless, as an important part of the diagnosis, the feature information description of the vibration signal of an HVCB has been neglected; in particular, its diversity and significance are rarely considered in many real-world fault-diagnosis applications. Therefore, in this paper, a hybrid feature transformation is proposed to optimize the diagnosis performance for HVCB faults. First, we introduce a nonlinear feature mapping in the wavelet package time- frequency energy rate feature space based on random forest binary coding to extend the feature width. Then, a stacked autoencoder neural network is used for compressing the feature depth. Finally, five typical classifiers are applied in the hybrid feature space based on the experimental dataset. The superiority of the proposed feature optimal approach is verified by comparing the results in the three abovementioned feature spaces.

中文翻译：

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使用基于随机森林和堆叠自动编码器的混合特征转换方法进行高压断路器故障诊断


近年来，机器学习技术已被应用于测试高压断路器（HVCB）的故障类型。大多数相关研究涉及改进分类方法以获得更高的精度。然而，作为诊断的重要组成部分，高压断路器振动信号的特征信息描述一直被忽视；特别是，在许多现实世界的故障诊断应用中很少考虑其多样性和重要性。因此，本文提出了一种混合特征变换来优化 HVCB 故障的诊断性能。首先，我们在小波包时频能量率特征空间中引入基于随机森林二进制编码的非线性特征映射以扩展特征宽度。然后，使用堆叠式自动编码器神经网络来压缩特征深度。最后，基于实验数据集，将五种典型分类器应用于混合特征空间。通过比较上述三个特征空间中的结果，验证了所提出的特征优化方法的优越性。