Abstract
Feature extraction from a signal is the most important step in signal-based fault diagnosis. Deep learning or deep neural network (DNN) is an effective method to extract features from signals. In this paper, a novel vibration signal-based bearing fault diagnosis method using DNN is proposed. First, the measured vibration signals are transformed into a new data form called multiple-domain image-representation. By this transformation, the task of signal-based fault diagnosis is transferred into the task of image classification. After that, a DNN with a multi-branch structure is proposed to handle the multiple-domain image representation data. The multi-branch structure of the proposed DNN helps to extract features in multiple domains simultaneously, and to lead to better feature extraction. Better feature extraction leads to a better performance of fault diagnosis. The effectiveness of the proposed method was verified via the experiments conducted with actual bearing fault signals and its comparisons with well-established published methods.
Highlights
Rolling element bearings are the most important components in rotary machines
The proposed algorithm can be applied as an automatic fault detection process for the early detection of bearing faults; it helps to reduce the failure rate of machinery and save repair costs
This paper proposed a novel method of bearing fault diagnosis based on vibration signals
Summary
Rolling element bearings are the most important components in rotary machines. The health condition of bearings has a profound effect on the performance of the machines. Intelligent signal-based fault diagnosis is the most popular approach in machine health monitoring. The signal types used for diagnosing can be vibration signal [2,3], acoustic emission signal [4], or current signal [5,6]. Among those types of signals, vibration signal is exploited most extensively because vibration signal is easy to measure and can provide highly accurate information about the bearing health condition [7]. The fault diagnosis performance of the signal-based approach highly depends on the procedure of feature extraction in which discriminating features are extracted from vibration signals. After extracting the fault features from the fault signals, an intelligent decision-maker based on machine learning algorithms is exploited to determine the type of fault occurring
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