An Intelligent Ball Bearing Fault Diagnosis System Using Enhanced Rotational Characteristics on Spectrogram

Abstract

Faults in the ball bearing are a major cause of failure in rotating machinery where ball bearings are used. Therefore, there is a growing demand for ball bearing fault diagnosis to prevent failures in rotating machinery. Although studies on the fault diagnosis of bearing have been conducted using temperature measurements and sound monitoring, these methods have limitations, because they are affected by external noise. Therefore, many researchers have studied vibration monitoring for bearing fault diagnosis. Among these, mel-frequency cepstral coefficients (MFCCs) and 2D convolutional neural networks (CNNs) have attracted significant attention in vibration monitoring schemes. However, the MFCC in existing studies requires a high sampling rate and an expansive frequency band utilization. In addition, 2D CNNs are highly complex. In this study, a rotational characteristic emphasis (RCE) spectrogram process and an optimized CNN were proposed to solve these problems. The RCE spectrogram process analyzes a narrow frequency band and produces low-resolution images. The optimized CNN was designed with a shallow network structure. The experimental results showed an accuracy of 0.9974 for the proposed system. The optimized CNN model has parameters of 5.81 KB and FLOPs of 1.53×106. We demonstrate that the proposed ball bearing fault diagnosis system can achieve high accuracy with low complexity. Thus, we propose a ball bearing fault diagnosis scheme that is applicable to a low sampling rate and changing rotation frequency.

Data-Driven Machine Fault Diagnosis of Multisensor Vibration Data Using Synchrosqueezed Transform and Time-Frequency Image Recognition with Convolutional Neural Network

“…It contains data collected from a test rig simulating bearing faults in a motor. The proposed IMU6DoF-SST-CNN method was successfully verified on the CWRU bearing fault dataset with thirteen classes, unlike [2,3,31] studies with a smaller number of classes. Verification of IMU6DoF-SST-CNN with thirteen classes offers a more complex and comprehensive challenge compared to the fan demonstrator datasets.…”

“…For comparison, Table 1 provides a detailed breakdown of how the proposed method differs from previous approaches in terms of datasets, faults, sensors, feature extraction, features, fusion techniques, classifiers, and overall methodology. The CWRU publicly available dataset is widely used for benchmarking fault diagnosis algorithms [2,3,31,32]. It contains data collected from a test rig simulating bearing faults in a motor.…”