Extraction of Weak Bearing Fault Signatures from Non-stationary Signals Using Parallel Wavelet Denoising

Condition monitoring is a central aspect in the health assessment and maintenance of industrial machinery. Vibration analysis is the most widely used technique for fault detection in rotating machinery. However, the technique can become difficult to apply in the case of machinery with non-stationary duty cycles due to the time-varying characteristics of the machine vibrations. The vibration signature of an incipient fault in rotating machinery is typically weak when compared to other sources of excitation. Due to these limitations, many methods have been proposed to increase the signal to noise ratio (SNR) of the signals as well as their applicability to non-steady operation. These include the separation of the random fault signatures from the deterministic components in the signal as well as techniques based on optimising the filtering of the signal to increase SNR. This work presents a method for extracting weak fault signatures from non-stationary signals using a reference signal from a parallel operating component on the same machine. The method, which is based on wavelet de-noising, employs a reference signal to adapt noise thresholds in the time and scale domain. Tests were performed using simulated non-stationary vibration signals. The proposed technique is shown to be effective at increasing the SNR when combined with envelope analysis to detect and diagnose faults.