Automated bearing fault diagnosis scheme using 2D representation of wavelet packet transform and deep convolutional neural network

Abstract

Bearings are one of the most crucial components in many industrial machines. Effective bearing fault diagnosis is essential for normal and safe machine operation. Existing fault diagnosis methods are mostly limited to manual feature characterization and traditional machine learning schemes such as support vector machines (SVM) and k-nearest neighbors (k-NN) algorithms. Unfortunately, the interpretation and engineering of such features require substantial human expertise. This paper proposes an adaptive deep convolutional neural network (ADCNN) that utilizes a two-dimensional visualization of the raw acoustic emission (AE) signal to provide bearing health state information, which serves to automate and better generalize the feature extraction and classification process. This 2D visualization tool applies a discrete wavelet packet transform (WPT) and quantifies each sub-band of the signal by defining a new evaluation metric—the degree of defectiveness ratio (DDR)— to precisely represent each fault condition, henceforth called a DDRgram. The motivation for using this DDRgram-based preprocessing scheme is that valuable information regarding rotating components is distributed across discrete frequencies, and thus bearing health conditions can be revealed by those frequency spectra. Furthermore, the proposed ADCNN is trained using an adaptive learning method to achieve improved diagnostic performance. The efficiency of the proposed fault diagnosis methodology (DDRgram + ADCNN) is verified using AE data, collected from a benchmark bearings testbed. The experimental studies demonstrate that the proposed approach outperforms existing state-of-the-art algorithms for the multi-fault classification of bearings with good accuracy.