In recent years, vibration-based intelligent fault diagnosis of high-voltage circuit breakers (HVCBs) exhibits excellent performance. It requires a reliable machine learning method to develop an automatically diagnostic model to recognize the mechanical state from vibration signals. However, the traditional machine learning methods tend to produce unstable diagnostic results under the case of sampling asynchrony caused by the fluctuation of the control voltage. To address this problem, an improved kernel extreme learning machine (K-ELM) called triangular global alignment kernel (TGAK) extreme learning machine (TGAK-ELM) was presented in this study, which was developed by introducing TGAK into K-ELM. The TGAK is an elastic kernel which was designed by considering all the possible alignments between samples. Therefore, it provides a flexible similarity measure for samples, resulting in the improvement of the diagnostic performance. Experiments on the 35kV HVCB verified the effectiveness of the proposed method. Compared to other state-of-the-art machine learning methods, the proposed TGAK-ELM produced better diagnostic results. And further experiments on eight datasets picked from UCR repository suggested the applicability of TGAK-ELM in other fields.

近年来，基于振动的高压断路器（HVCB）智能故障诊断表现出出色的性能。它需要可靠的机器学习方法来开发自动诊断模型，以从振动信号中识别机械状态。但是，传统的机器学习方法在由控制电压波动引起的采样异步的情况下往往会产生不稳定的诊断结果。为了解决这个问题，本研究提出了一种改进的内核极限学习机（K-ELM），称为三角全局对齐内核（TGAK）极限学习机（TGAK-ELM），它是通过将TGAK引入K-ELM来开发的。TGAK是一种弹性核，是通过考虑样品之间所有可能的比对而设计的。所以，它为样品提供了一种灵活的相似性度量，从而提高了诊断性能。在35kV HVCB上进行的实验证明了该方法的有效性。与其他最新的机器学习方法相比，提出的TGAK-ELM产生了更好的诊断结果。对从UCR存储库中选取的8个数据集的进一步实验表明TGAK-ELM在其他领域的适用性。