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.

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The widespread deployment of industrial wind projects will require a more proactive maintenance strategy in order to be more cost competitive. This paper describes an ongoing research project on developing online lubrication oil condition monitoring and degradation detection tools using commercially available online sensors. In particular, an investigation on particle contamination of lubrication oil is reported. Methods are presented for online lubrication oil condition monitoring and remaining useful life prediction using viscosity and dielectric constant sensors along with particle filtering technique. Physical models are derived in order to establish the mathematical relationship between lubrication oil degradation and particle contamination level. Laboratory experiments are performed to validate the accuracy of the developed models by comparing viscosity and dielectric constant sensor outputs of different particle concentration levels with those simulated by the lubricant deterioration physical models. A case study on lubrication oil degradation detection and remaining useful life prediction is provided. Discussions on the potential for extrapolating the presented methods to typical wind turbine gearbox oil and the practical implementation of particle filter‐based approach for online wind turbine gearbox oil remaining useful life prediction are also included. Copyright © 2014 John Wiley & Sons, Ltd.

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.

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