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Demodulation of vibration signal to detect faults in machinery has been a prominent prevalent technique that is discussed by a number of authors. This paper deals with the demodulation of the current signal of an induction motor driving a multistage gearbox for its fault detection. This multistage gearbox has three gear ratios, and thus, three rotating shafts and their corresponding gear mesh frequencies (GMFs). The gearbox is loaded electrically by a generator feeding an electrical resistance bank. Amplitude demodulation and frequency demodulation are applied to the current drawn by the induction motor for detecting the rotating shaft frequencies and GMFs, respectively. Discrete wavelet transform is applied to the demodulated current signal for denoising and removing the intervening neighboring features. Spectrum of a particular level, which comprises the GMFs, is used for gear fault detection. [ABSTRACT FROM AUTHOR]

Abstract: Even though there are a number of condition monitoring and analysis techniques, researchers are in search of a simple and easy way to monitor vibration of a gearbox, which is an omnipresent and an important power transmission component in any machinery. Motor current signature analysis (MCSA) has been the most recent addition as a non-intrusive and easy to measure condition monitoring technique. The objective of this paper is to detect artificially introduced defects in gears of a multistage automotive transmission gearbox at different gear operations using MCSA as a condition monitoring technique and Kolmogorov–Smirnov (KS) test as an analysis technique assuming that any defect or load has a specific probability distribution. Empirical cumulative distribution functions (ECDF) are used to differentiate these probability distributions. Steady as well as fluctuating load conditions on the gearbox are tested for both vibration and current signatures during different gear operations. It is concluded that combined MCSA and KS test can be an effective way to monitor and detect faults in gears. [Copyright &y& Elsevier]