Abstract
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%.
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