A Robust Feature Extraction for Automatic Fault Diagnosis of Rolling Bearings Using Vibration Signals

Bearing faults are one of the main reasons for rotary machine failure. Monitoring bearing vibration signals is an effective method for diagnosing faults and preventing catastrophic failures in rotary mechanisms. The state-of-the-art vibration monitoring algorithms are mainly based on frequency or time-frequency domain analysis of rotary machines that are operating in steady state. However, the steady state assumption is not valid in applications where the loads and speeds are time-varying. Finding a method for capturing the variability in vibration signals, which are caused by varying loads and speeds, is still an open research problem with potentially many applications in emerging areas such as electric vehicles. In this paper, we address the problem of vibration signal monitoring by applying a feature extraction algorithm to rotary machine signals measured by accelerometers. The proposed method, which is based on the wavelet scattering transform, achieves overall high accuracy while being computationally affordable for real-time implementation purposes. In order to verify the effectiveness of the proposed methodology, we apply our technique to a well-known vibration benchmark dataset with variable load. Our algorithm can diagnose various faults with different intensities with an average accuracy of 99% and thus effectively outperforming all prior reported work on this dataset.