Xue Jiaqi, Ma Biao, Chen Man, Zhang Qianqian, Zheng Liangjie
School of Mechanical Engineering, Beijing Institute of Technology, Beijing 100811, China.
Key Laboratory of Science and Technology for National Defense, Beijing Institute of Technology, Beijing 100811, China.
Entropy (Basel). 2021 Dec 20;23(12):1704. doi: 10.3390/e23121704.
The multi-disc wet clutch is widely used in transmission systems as it transfers the torque and power between the gearbox and the driving engine. During service, the buckling of the friction components in the wet clutch is inevitable, which can shorten the lifetime of the wet clutch and decrease the vehicle performance. Therefore, fault diagnosis and online monitoring are required to identify the buckling state of the friction components. However, unlike in other rotating machinery, the time-domain features of the vibration signal lack efficiency in fault diagnosis for the wet clutch. This paper aims to present a new fault diagnosis method based on multi-speed Hilbert spectrum entropy to classify the buckling state of the wet clutch. Firstly, the wet clutch is classified depending on the buckling degree of the disks, and then a bench test is conducted to obtain vibration signals of each class at varying speeds. By comparing the accuracy of different classifiers with and without entropy, Hilbert spectrum entropy shows higher efficiency than time-domain features for the wet clutch diagnosis. Thus, the classification results based on multi-speed entropy achieve even better accuracy.
多片湿式离合器广泛应用于传动系统中，因为它能在变速箱和驱动发动机之间传递扭矩和动力。在使用过程中，湿式离合器中的摩擦部件不可避免地会发生屈曲，这会缩短湿式离合器的使用寿命并降低车辆性能。因此，需要进行故障诊断和在线监测来识别摩擦部件的屈曲状态。然而，与其他旋转机械不同，振动信号的时域特征在湿式离合器的故障诊断中缺乏效率。本文旨在提出一种基于多速希尔伯特谱熵的新型故障诊断方法，以对湿式离合器的屈曲状态进行分类。首先，根据盘片的屈曲程度对湿式离合器进行分类，然后进行台架试验，以获取不同转速下各类别的振动信号。通过比较有无熵的不同分类器的准确性，希尔伯特谱熵在湿式离合器诊断中显示出比时域特征更高的效率。因此，基于多速熵的分类结果具有更高的准确性。
