Automated gear fault detection of micron level wear in bevel gears using variational mode decomposition

Abstract

Gearboxes have an important role in power transmission systems. For such systems, vibration-based fault diagnosis techniques are frequently used to prevent premature failure and to ensure smooth transmission. We automated the fault diagnosis of gears having level of wear fault at micron using variational mode decomposition (VMD). VMD has been applied iteratively with specific input parameters. VMD decomposes the gear vibration signal into different narrowband components (NBCs) or obtained components (OCs). Various statistical features, namely kurtosis, skewness, standard deviation, root mean square, and crest factor, were extracted from the different OCs. Kruskal-Wallis test based on probability values was used to identify the significant features. For the automation of fault detection system, a comparative study was done using the random forest, multilayer perceptron, and J48 classifiers. The proposed method exhibits 96.5 % accuracy using random forest classifier with combined kurtosis, skewness, and standard deviation features.