Modeling Localized Bearing Faults Using Inverse Gaussian Mixtures

Pavle Boškoski and Đani Juričić
Submission Type: 
Full Paper
AttachmentSizeTimestamp
phmc_13_022.pdf2.62 MBSeptember 12, 2013 - 11:46pm

Localized bearing faults exhibit specific repetitive vibrational patterns. Due to the constant angular distance between the roller elements, the vibrational patterns occur on regular angular intervals. Under constant operating conditions such patterns become easily detectable as ``periodic'' events. However, slippage or small variation in rotational speed alters their time pattern, making them non-periodic and consequently difficult to distinguish. In this paper we present an approach which models the occurrences of localized bearing fault patterns as a realization of random point process whose inter-event time intervals are governed by inverse Gaussian mixture. The applicability of the model was evaluated on vibrational signals generated by bearing models with localized surface fault.

Publication Year: 
2013
Publication Volume: 
4
Publication Control Number: 
022
Page Count: 
7
Submission Keywords: 
inverse Gaussian mixture
Bearing Faults
Bayes factor
Submission Topic Areas: 
Data-driven methods for fault detection, diagnosis, and prognosis
Modeling and simulation
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