  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. Further, 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 paper, 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 utilized 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.
