On the Use of Spectral Averaging of Acoustic Emission Signals for Bearing Fault Diagnostics

Abstract

For years, vibration analysis has been the industry standard for bearing fault diagnosis. However, due to the various advantages over vibration based techniques, the quantification of acoustic emission (AE) for bearing health diagnosis has been an area of interest for recent years. Additionally, most AE based methodologies to date utilize data mining technologies. Presented in this paper is a new approach, combining a heterodyne based frequency reduction technique, time synchronous resampling, and spectral averaging to process AE signals and compute condition indicators (CIs) for bearing fault diagnostics. First, the heterodyne based frequency reduction technique allows the AE signal frequency to be down shifted from several MHz to less than 50 kHz, which approaches that of vibration based methodologies. Next, the sampled AE signals are band pass filtered to retain the useful information related to the bearing defects. Last, a trigger signal is utilized to time synchronously resample the AE signals to allow the calculation of a spectral average and the extraction and evaluation of CIs for bearing fault diagnosis. The technique presented in this paper is validated using the AE signals of seeded fault steel bearings on a bearing test rig. Presented is an effective AE based approach validated to diagnose all four fault types: inner race, outer race, ball, and cage. Moreover, the effectiveness of the presented approach is established through the comparison of both AE and vibration data.

On the Use of a Single Piezoelectric Strain Sensor for Wind Turbine Planetary Gearbox Fault Diagnosis

“…Thus, the calculated fault frequencies shown in Table III could not be used as the basis for bandpass filter selection. Following the filter band optimization procedure in [45], Shannon entropy was computed on the healthy PGB signals after the implementation of different filter bands. Fig.…”

Vibration‐based wind turbine planetary gearbox fault diagnosis using spectral averaging

“…Figure displays a graphical representation of the SA for rotating machinery fault diagnosis shown in Van Hecke et al . . To implement the SA for machinery fault diagnosis, the sensor signals must be segmented by a particular size.…”

“…and Van Hecke et al . . Using the presented approach, vibration fault features for wind turbine PGB are extracted as condition indicators (CIs) for fault diagnosis.…”

Detection of on-aircraft fatigue cracks in a UH-60A planet carrier using spectral averaging

“…. The result is K Fourier transforms B k (f n ), which correspond to the K number of segments: 7where: 8) and:…”

“…This paper analyses the UH-60A on-aircraft vibration data using a time synchronous resampling-based spectral average approach. This technique has been shown to be effective in diagnosing all four bearing fault types for shaft speeds from 30 Hz to 60 Hz, using both vibration and acoustic emission signals [8][9][10] . However, the time domain signal and statistical feature combination that was found to be effective for the aforementioned bearing applications was not effective for the data used in this study.…”