Abstract
Bearings are the key and important components of rotating machinery. Effective bearing fault diagnosis can ensure operation safety and reduce maintenance costs. This paper aims to develop a novel bearing fault diagnosis method via an improved multi-scale convolutional neural network (IMSCNN). In traditional convolutional neural network (CNN), a fixed convolutional kernel is often employed in the convolutional layer. Thus, informative features can not be fully extracted for fault diagnosis. In the proposed IMSCNN, a 1D dimensional convolutional layer is used to mitigate the effect of noise contained in vibration signals. Then, four dilated convolutional kernels with different dilation rates are integrated to extract multi-scale features through the inception structure. Experimental results from the popular CWRU and PU datasets show the superiority of the proposed method by comparison with other related methods.
Highlights
Fault Diagnosis via ImprovedBearings are regarded as critical components in rotating machinery
Multi-Layer Perceptron (MLP): it is composed of five fully connected (FC) layers
A novel convolutional neural network (CNN)-based bearing fault diagnosis method called improved multi-scale convolutional neural network (IMSCNN) is developed in this paper
Summary
Bearings are regarded as critical components in rotating machinery. And effective bearing fault diagnosis technique plays an important role in avoiding unforeseen downtime of rotating machinery. Compared to current signals [3] and acoustic emission signals [4], vibration signals [5,6]. Contain abundant information that reflects the health state of bearings. Vibration signals are widely used in bearing fault diagnosis. Fault diagnosis techniques can be categorized into two types, signal analysis, and data-driven methods. Based on the expert knowledge, features extracted from different domains are used to detect bearings health changes and assess health states. A major limitation of signal analysis methods is that comprehensive and great expert knowledge is required to determine the health states and faulty types of bearings from extracted features
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