Wei Juhui, He Zhangming, Wang Jiongqi, Wang Dayi, Zhou Xuanying
College of Liberal Arts and Sciences, National University of Defense Technology, Changsha 410073, China.
Beijing Institute of Spacecraft System Engineering, China Academy of Space Technology, Beijing 100094, China.
Entropy (Basel). 2021 Feb 24;23(3):266. doi: 10.3390/e23030266.
Weak fault signals, high coupling data, and unknown faults commonly exist in fault diagnosis systems, causing low detection and identification performance of fault diagnosis methods based on T2 statistics or cross entropy. This paper proposes a new fault diagnosis method based on optimal bandwidth kernel density estimation (KDE) and Jensen-Shannon (JS) divergence distribution for improved fault detection performance. KDE addresses weak signal and coupling fault detection, and JS divergence addresses unknown fault detection. Firstly, the formula and algorithm of the optimal bandwidth of multidimensional KDE are presented, and the convergence of the algorithm is proved. Secondly, the difference in JS divergence between the data is obtained based on the optimal KDE and used for fault detection. Finally, the fault diagnosis experiment based on the bearing data from Case Western Reserve University Bearing Data Center is conducted. The results show that for known faults, the proposed method has 10% and 2% higher detection rate than T2 statistics and the cross entropy method, respectively. For unknown faults, T2statistics cannot effectively detect faults, and the proposed method has approximately 15% higher detection rate than the cross entropy method. Thus, the proposed method can effectively improve the fault detection rate.
在故障诊断系统中，弱故障信号、高耦合数据和未知故障普遍存在，这导致基于T2统计量或交叉熵的故障诊断方法的检测和识别性能较低。本文提出了一种基于最优带宽核密度估计（KDE）和 Jensen-Shannon（JS）散度分布的新型故障诊断方法，以提高故障检测性能。KDE用于解决弱信号和耦合故障检测问题，而JS散度用于解决未知故障检测问题。首先，给出了多维KDE最优带宽的公式和算法，并证明了算法的收敛性。其次，基于最优KDE得到数据间的JS散度差异，用于故障检测。最后，基于美国凯斯西储大学轴承数据中心的轴承数据进行了故障诊断实验。结果表明，对于已知故障，该方法的检测率分别比T2统计量和交叉熵方法高10%和2%。对于未知故障，T2统计量无法有效检测故障，而该方法的检测率比交叉熵方法高约15%。因此，该方法能有效提高故障检测率。
