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Fault diagnosis of gearbox which operates on low rotating speed with high fluctuations is highly important because its ignorance can led to a catastrophe. The uncertainty within the vibration signal of the gearbox can be identified by the entropy measures, on the basis of probability density function of a signal. But, under fluctuating speeds, entropies may show insignificant results, hence making them non-reliable. The aim of this article is to develop a reliable and stable technique for gear fault detection under such fluctuating speeds. Therefore, a root mean square–based probability density function is proposed to improve the efficiency of entropy measures. The fault detection capabilities of proposed technique were demonstrated experimentally. Various entropy measures, namely, Shannon entropy, Rényi entropy, approximate entropy, and sample entropy, were compared as well as evaluated for both Gaussian and proposed probability density function. The proposed technique was further validated using two condition indicators based on amplitude of probability density function. Results suggest the effective fault diagnosis using proposed method.

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Numerous studies on vibration-based techniques for fault diagnosis of wind turbine gearboxes operating under nonstationary conditions have been reported. In spite, a review on vibration-based condition monitoring and fault diagnosis techniques for gearboxes of wind turbines operating under nonstationary conditions is unavailable. Thus, the objective of this review is to discuss filtering, decomposition, entropy, and cyclostationary analysis techniques, as well as summarizing the remaining issues. This review will discuss various vibration-based diagnostic approaches developed for wind turbine gearboxes under nonstationary conditions, including both simulation and experimental approaches. Studies on dynamic models of gear systems and advanced signal-processing techniques developed for nonstationary conditions are reviewed. Additionally, the importance of multi-sensor and cointegration based approaches is discussed, and intelligent classification methods that have been used to distinguish healthy and faulty gear systems are also reviewed. Finally, the remaining research challenges are outlined.