Multistage gearbox condition monitoring using motor current signature analysis and Kolmogorov–Smirnov test

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. jective 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.