[3F4] An innovative hybrid approach for better detection of bearing faults in highly noisy environments
A Kiakojouri¹, Z Lu¹, P Mirring², H Powrie³ and L Wang¹
¹University of Southampton, UK
²Schaeffler Aerospace Germany GmbH, Germany
³GE Aviation, UK
Rolling element bearings (REBs) are critical components in rotating machinery and the timely detection of faults in these bearings can prevent catastrophic failure and costly downtime. Vibration signals collected from REBs contain important information regarding the bearing health state. A localised defect in an REB produces periodic impulses in the vibration signals at relevant bearing characteristic frequencies (BCFs) that are commonly used for bearing condition monitoring. Envelope analysis has been a powerful technique in detecting BCFs in signals; however, it poses a significant challenge as it requires the selection of an effective band-pass filtering region due to the variable nature of resonant frequencies in bearing systems. A novel hybrid method for bearing fault diagnosis based on cepstrum pre-whitening (CPW) and high-pass filtering has been developed to overcome this shortcoming in envelope analysis and has proven to be a powerful technique in accurately detecting early bearing faults on a range of machines under different operating conditions. In contrast to the existing signal processing methods, the new hybrid method eliminates the need for manual parameter selection or optimisation algorithms, such as filtering band selection in enveloping analysis. Instead, it requires inputs including bearing geometries and operating conditions such as shaft rotating speed for bearing fault diagnosis. This enhances the practicality and accuracy of bearing fault diagnosis in real-world scenarios, especially when data is limited. This study evaluates the effectiveness of this hybrid method for bearing fault diagnosis in the highly noisy environments under which machines are often operated in real applications. To achieve this purpose, firstly, numerical simulations of bearing vibration signals are conducted, wherein Gaussian white noise with varying levels of signal-to-noise ratio (SNR) is added to the simulated signals. Secondly, the effectiveness of the method is tested on vibration data collected from an experimental run-to-failure bearing dataset, which contains a significant amount of background noise in the early stage of the fault progression. The results from this work show that the new hybrid method achieves high accuracy when diagnosing fault features in the simulated signals, with an SNR of down to -13 dB for the outer ring fault and -15 dB for inner ring and ball faults, which is much more effective than other methods in the literature. It is also shown to detect bearing faults at very early stages in a run-to-failure test.
Keywords: bearing fault diagnosis, envelope analysis, heavy noise environments, cepstrum pre-whitening, high-pass filtering.
Keywords: bearing fault diagnosis, envelope analysis, heavy noise environments, cepstrum pre-whitening, high-pass filtering.
