Bearing fault diagnosis based on a new acoustic emission sensor technique

Abstract

The diagnosis of bearing health by quantifying acoustic emission data has been an area of interest for recent years due to the numerous advantages over vibration-based techniques. However, most acoustic emission-based methodologies to date are data-driven technologies. This research takes a novel approach combining a heterodyne-based frequency reduction technique, time synchronous resampling, and spectral averaging to process acoustic emission signals and extract condition indicators for bearing fault diagnosis. The heterodyne technique allows the acoustic emission signal frequency to be shifted from several megahertz to less than 50 kHz, which is comparable to that of vibration-based techniques. Then, the digitized signal is band-pass filtered to retain the information associated with the bearing defects. Finally, the tachometer signal is used to time synchronously resample the acoustic emission data, allowing the computation of a spectral average which in turn enables the extraction and evaluation of condition indicators for bearing fault diagnosis. The presented technique is validated using the acoustic emission signals of seeded fault steel bearings on a bearing test rig. The result is an effective acoustic emission-based approach validated to diagnose all four fault types: inner race, outer race, ball, and cage.

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

“…There are numerous condition indicators in literature that quantify accelerometer signals to aid in rotating machinery fault diagnosis. For example, some recent studies have developed effective CIs that accomplish bearing fault diagnosis by quantifying both accelerometer and AE signals [8][9][10][23][24] . A major difference among the available CIs is in the method of computation.…”

“…Furthermore, CIs can be extracted from a raw signal or a signal being processed by a signal processing technique, such as TSA or spectral averaging. Previously, the inverse Fourier transform of the square magnitude of the spectral average in combination with the RMS and peak CIs has been used to successfully diagnose bearing faults at shaft speeds of 30 Hz and higher for both AE and vibration signals [8][9][10] . However, because the aforementioned analysis signal and CI combination tested in those studies were not effective for shaft speeds less than 30 Hz, such work was left for future investigation.…”

“…By introducing a log into the inverse Fourier transform, the signal being analysed is similar to a power cepstrum that has been utilised in [25] for gear analysis. It should also be mentioned that although the introduction of the log was found to be effective for the UH-60A application, it was not effective for the high-speed bearing data used in [8][9][10] . Formally, the input signal used for CI computation is given as: 11) where the input signal x is the time domain signal used for CI computation and ifft{log[SA(f n )]} is the inverse Fourier transform of the log of the spectral average result SA(f n ) obtained by Equation (10).…”

“…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.…”

Health Monitoring on the Spacecraft Bearings in High-Speed Rotating Systems by Using the Clustering Fusion of Normal Acoustic Parameters

“…A number of techniques for the detection of abnormal conditions of rolling element bearings are currently available, which include vibration and acoustics [5,6], acoustic emission [7], lubricating oil monitoring [8], and temperature [9]. As a sensitive and effective method, vibration and acoustic measurements are widely used to detect the defects in bearings [10].…”