No abstract

In recent years, acoustic emission (AE) sensors and AE-based techniques have been developed and tested for gearbox fault diagnosis. In general, AE-based techniques require much higher sampling rates than vibration analysis-based techniques for gearbox fault diagnosis. Therefore, it is questionable whether an AE-based technique would give a better or at least the same performance as the vibration analysis-based techniques using the same sampling rate. To answer the question, this paper presents a comparative study for gearbox tooth damage level diagnostics using AE and vibration measurements, the first known attempt to compare the gearbox fault diagnostic performance of AE- and vibration analysis-based approaches using the same sampling rate. Partial tooth cut faults are seeded in a gearbox test rig and experimentally tested in a laboratory. Results have shown that the AE-based approach has the potential to differentiate gear tooth damage levels in comparison with the vibration-based approach. While vibration signals are easily affected by mechanical resonance, the AE signals show more stable performance.

Planetary gearboxes (PGBs) are widely used in the drivetrain of wind turbines. Any PGB failure could lead to breakdown of the whole drivetrain and major loss of wind turbines. Therefore, PGB fault diagnosis is important in reducing the downtime and maintenance cost and improving the reliability and lifespan of wind turbines. PGB fault diagnosis has been done mostly through vibration analysis over the past years. Vibration signals theoretically have an amplitude modulation (AM) effect caused by time-variant vibration transfer paths due to the rotation of planet carrier and sun gear, and therefore, their spectral structure is complex. Strain sensor signals, on the other hand, are closely correlated to torsional vibration, which is less sensitive to the AM effect caused by rotating vibration transfer path. Thus, it is potentially easy and effective to diagnose PGB faults via stain sensor signal analysis. In this paper, a new method using a single piezoelectric strain sensor for PGB fault diagnosis is presented. The method is validated on a set of seeded localized faults on all gears, namely, sun gear, planetary gear, and ring gear. The validation results have shown a satisfactory PGB fault diagnostic performance using strain sensor signal analysis.

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.

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.