Abstract
A naïve Bayes classifier is used for classifying the faults of a gear box. The rotating equipment like gear box has enough scope for condition monitoring its conditions as it plays a significant role as a prime mover in many engineering applications. This paper discusses about the extraction of Morlet wavelet coefficients from the vibration signal for different conditions of the gear box under investigation, and application of naïve Bayes classifier for its condition monitoring and diagnostics. In this paper fault diagnostics of spur bevel gear box is treated as a pattern classification problem. The major steps in pattern classification are feature extraction, and classification. This work investigates the usefulness of statistical features of Morlet wavelet coefficients and naïve Bayes for classification. The results show that the developed method can reliably diagnose different conditions of the gear box and the algorithm can be applied for online applications of supervised machine learning.
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