LAUSR

Vibration-based condition monitoring techniques are commonly used to detect and diagnose failures of rolling bearings. Accuracy and delay in detecting and diagnosing different types of failures are the main performance measures in condition monitoring. Achieving high accuracy with low delay improves system reliability and prevents catastrophic equipment failure. Furthermore, delay is crucial to remote condition monitoring and time-sensitive industrial applications. While most of the proposed methods focus on accuracy, slight attention has been paid to addressing the delay introduced in the condition monitoring process. In this article, we attempt to bridge this gap and propose a hybrid method for vibration-based condition monitoring and fault diagnosis of rolling bearings that outperforms previous methods in terms of accuracy and delay. Specifically, we address the overall delay in vibration-based condition monitoring systems and introduce the concept of system delay to assess it. Then, we present the proposed method for condition monitoring. It uses wavelet packet transform (WPT) and Fourier analysis to decompose short-duration input segments of the vibration signal into elementary waveforms and obtain their spectral contents. Accordingly, energy concentration in the spectral components—caused by defect-induced transient vibrations—is used to extract a small number of features with high discriminative capabilities. Consequently, Bayesian-optimization-based random forest (RF) algorithm is used to classify healthy and faulty operating conditions under varying motor speeds. The experimental results show that the proposed method can achieve high accuracy with low system delay.