Quadratic Time-Frequency Analysis of Vibration Signals for Diagnosing Bearing Faults
CoRR(2024)
Abstract
Diagnosis of bearing faults is paramount to reducing maintenance costs and
operational breakdowns. Bearing faults are primary contributors to machine
vibrations, and analyzing their signal morphology offers insights into their
health status. Unfortunately, existing approaches are optimized for controlled
environments, neglecting realistic conditions such as time-varying rotational
speeds and the vibration's non-stationary nature. This paper presents a fusion
of time-frequency analysis and deep learning techniques to diagnose bearing
faults under time-varying speeds and varying noise levels. First, we formulate
the bearing fault-induced vibrations and discuss the link between their
non-stationarity and the bearing's inherent and operational parameters. We also
elucidate quadratic time-frequency distributions and validate their
effectiveness in resolving distinctive dynamic patterns associated with
different bearing faults. Based on this, we design a time-frequency
convolutional neural network (TF-CNN) to diagnose various faults in
rolling-element bearings. Our experimental findings undeniably demonstrate the
superior performance of TF-CNN in comparison to recently developed techniques.
They also assert its versatility in capturing fault-relevant non-stationary
features that couple with speed changes and show its exceptional resilience to
noise, consistently surpassing competing methods across various signal-to-noise
ratios and performance metrics. Altogether, the TF-CNN achieves substantial
accuracy improvements up to 15
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