This paper presents an integrated approach for the detection and classification of the faults of rolling bearing in rotary machines. Permutation entropy (PE) is integrated with a flexible analytical wavelet transform (FAWT). The signals from healthy and faulty bearing systems with different operating conditions are decomposed by FAWT. To compare effectiveness of the proposed methodology, dyadic discrete wavelet transform (DWT) is also used in integration with PE. PE values of decomposed signals are calculated and are then fed as feature vectors to support vector machine (SVM) classifier for the classification of different types and fault sizes. The classification results of both approaches are compared. The results demonstrate the effectiveness and robustness of FAWT-integrated-PE over the DWT integrated with PE, for detection of bearing faults and their classification.

本文提出了一种用于旋转机械中滚动轴承故障检测和分类的综合方法。置换熵（PE）与灵活的分析小波变换（FAWT）集成在一起。FAWT对来自运行状况不同的健康轴承系统和故障轴承系统的信号进行分解。为了比较所提出方法的有效性，二元离散小波变换（DWT）也与PE集成使用。计算分解信号的PE值，然后将其作为特征向量馈入支持向量机（SVM）分类器，以对不同类型和故障大小进行分类。比较两种方法的分类结果。结果证明了FAWT集成PE的有效性和鲁棒性优于与PE集成的DWT，