Intelligent fault diagnosis method for rotating machinery based on vibration signal analysis and hybrid multi-object deep CNN

Abstract

Traditional labour-intensive methods always suffer from time and frequency blurring and cross-term interference when detecting fault features from vibration signals and their time–frequency representation. Such methods need essential expertise and prior knowledge to identify features. Intelligent fault diagnosis methods based on deep learning have attracted great attention because of their ability to automatically learn the representative features in fault diagnosis. Therefore, a novel multi-object deep convolutional neural network (CNN) is proposed to diagnose the faults. In this method, time domain, frequency domain, and time–frequency data are organised as the hybrid multi-object input to avoid the shortcoming of the single type of data, which cannot reveal abundant information about amplitude and frequency in vibration signals. Deep CNN with four convolutional layers is constructed to intelligently learn the distinguished features. The adjacent convolutional layer is used to sensitively capture slight changes in the input data, and the other layers are used to extract and reduce the abundant features. Random forest is used to accurately classify the fault types. The vibration signal of cylinder rolling bearing and gearbox is used to validate the proposed method. The results are compared with those of other state-of-the-art methods to show the superior performance of the proposed method.