Bearing Fault Detection Using Scalogram and Switchable Normalization-Based CNN (SN-CNN)

Abstract

Bearings play a vital role in all rotating machinery, and their failure is one of the significant causes of machine breakdown leading to a profound loss of safety and property. Therefore, the failure of rolling element bearings should be detected early while the machine fault is small. This paper presents the model that detects bearing failures using the continuous wavelet transform and classifies them using a switchable normalization-based convolutional neural network (SN-CNN). State-of-the-art accuracy was achieved with the proposed model using the Case Western Reserve University bearing dataset, which serves as the primary dataset for validating various algorithms for bearing failure detection. Batch normalization techniques were also employed and compared to the proposed model. The spectrogram images were also used as input for further comparison.

CNN-Based Fault Detection for Smart Manufacturing

“…This classifier function ensures that the outputs are positive values ranging from 0 to 1, which are the probabilities for each class [5,29].…”

“…ReLU is a non-saturated activation function, which solves the problem of gradient vanishing to some extent and speeds up convergence; ReLU outputs positive numbers as they are and sets negative numbers to zero as they are. If the input is negative, ReLU will not work at all [5]. This function is defined as follows:…”

“…Again, the mechanical vibration signals are considered rich sources of information for the appropriate analysis of processes related to bearing faults. These vibration signals can provide enough information about the location and type of the fault, which is helpful for fault diagnosis [1,5].…”

“…Again, the mechanical vibration signals are considered rich sources of information for the appropriate analysis of processes related to bearing faults. These vibration signals can provide enough information about the location and type of the fault, which is helpful for fault diagnosis [1,5]. The working process of the ball bearing system consists of sensors placed in different locations in the equipment, via which the signals are transferred to the data acquisition system for additional processing.…”

“…Figure 2 shows the vibration data collection process of the CWRU ball bearing system. The performance of fault detection methods depends both on the quality of the vibration signals collected and on the efficiency of the applied signal processing and feature extraction methods [5,6]. Traditionally, the maintenance of these REBs used to be a posterior task, usually taking place after the occurrence of the fault.…”