An analytic approach to monitor main bearing health

In piston engines, failure of main bearings can lead to total engine failure causing huge financial as well as reputation costs for the organization. In this paper, an end-to-end analytic system, using data and domain, is described to develop a cumulative damage model to monitor the health of the ma...

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Bibliographic Details
Published inLife cycle reliability and safety engineering Vol. 9; no. 2; pp. 213 - 218
Main Authors Shah, Tapan, Karnik, Aditya, Narayanan, Babu
Format Journal Article
LanguageEnglish
Published Singapore Springer Singapore 01.06.2020
Subjects
Civil Engineering
Engineering
Mechanical Engineering
Original Research
Quality Control
Reliability
Safety and Risk
Cumulative damage models
Main bearing
Anomaly detection
Prognostics
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Summary:In piston engines, failure of main bearings can lead to total engine failure causing huge financial as well as reputation costs for the organization. In this paper, an end-to-end analytic system, using data and domain, is described to develop a cumulative damage model to monitor the health of the main bearing using data obtained from engine lube oil analysis. The key outputs of the monitoring system are: (1) A multivariate baseline cumulative damage suffered by a ‘typical’ main bearing as a function of age. (2) Use of a 1-class support vector machine (SVM) to predict an impending engine failure because of a main bearing failure at least X days in advance. (3) Devise a ranking scheme for condition-based replacement of main bearings. The analytic system has been deployed for multiple railroad customers with a precision of over 90%.
ISSN:2520-1352
2520-1360
DOI:10.1007/s41872-019-00098-9