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Fault Diagnosis of Helical Gear Box Using Variational Mode Decomposition with Naïve Bayes and Bayes Net Classifiers through Vibration Signals

A. Kannan, V. Sugumaran, M. Amarnath, K. P. Soman


Gear is a very important machine component which finds use in most of the machines which requires some sort of power transmission or reduction. Unattended faults in gears can be catastrophic for the machine leading to halts in production and consequent economic loss. The requirement for a gear fault detection and diagnosis system is thus emphasized. Vibration signals are often used in fault diagnosis applications along with Fast Fourier transform method. However it is not effective with non-stationary signals like those obtained from gears in motion. Therefore, development of new methodologies to obtain diagnostic information from such signals is required. This paper details the use of vibration signals obtained from gears in good and simulated faulty conditions after performing preprocessing with Variational Mode Decomposition (VMD). VMD decomposes the vibration signals into various modes by identifying a compact frequency support around its central frequency so that adding all the modes reconstructs the original signal. Alternating Direction Multiplier Method (ADMM) is used in VMD to find the intrinsic mode functions. Descriptive statistical features extracted from VMD preprocessed signals, classified using Naïve Bayes and Bayes Net classifiers, and corresponding classification accuracies were calculated. The results were compared with the accuracy obtained from the statistical features extracted from the raw signal and decision tree classifier.

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