Gearbox fault diagnosis using RMS based probability density function and entropy measures for fluctuating speed conditions

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.