Showing papers by "Makarand S. Kulkarni published in 2020"
TL;DR: The accuracy in the remaining useful life prediction is found to increase after fusing the correlation coefficient of the residual vibration signal based health indicator derived from the accelerometers located at multiple positions on the gearbox in comparison to data from a single accelerometer.
Abstract: This article presents an ensemble decision tree–based random forest regression methodology for remaining useful life prediction of spur gears subjected to pitting failure mode. The random forest re...
29 citations
TL;DR: This article presents a review of different types of diagnostic and prognostic approaches developed for gears, given the fact that these approaches for other components such as bearing and batteries are reviewed in the past.
Abstract: Prognostics and health management has become a significant part of component life-cycle in modern industries. The prognostics and health management framework is implemented in the industries to ide...
27 citations
10 citations
01 Dec 2020
TL;DR: The application of a reinforcement learning (RL) algorithm based on the average reward for CSMDPs in CBM is described and it is shown that the RL algorithm is used to learn the optimal maintenance decisions and inspection schedulebased on the current health state of the component.
Abstract: Condition-based maintenance (CBM) involves taking decisions on maintenance or repair based on the actual deterioration conditions of the components. The long-run average cost is minimised by choosing the right maintenance action at the right time. In this study, the CBM decision-making problem is modelled as a continuous semi-Markov decision process (CSMDP). It consists of a chain of states representing various stages of deterioration, a set of maintenance actions, their costs and scheduled inspection policy. The application of a reinforcement learning (RL) algorithm based on the average reward for CSMDPs in CBM is described. The RL algorithm is used to learn the optimal maintenance decisions and inspection schedule based on the current health state of the component.
5 citations
TL;DR: A machine learning approach to estimate the residual life of a component by incorporating the effect of human error in maintenance is presented which predicts the Remaining Useful Life (RUL) of the component while considering error induced by maintenance personnel during its maintenance.
4 citations