Determine the Remaining Useful Life in Rotating Equipment, based on Prognostics and the combination of Degradation Processes, Using the ARMRS & Theory of Evidence

ABSTRACT Condition Assessment is one of the most significant techniques of the equipment health management. PHM methodology cycle, is a developed form of Condition Based Maintenance (CBM). Condition Assessment is the most important step of this cycle. In this study, based on the model presented, using equipment Condition Assessment, the Remaining Useful Life (RUL) is estimated. Using the simulation and forecasting of a new feature for vibration of the equipment by Autoregressive Markov Regime Switching (ARMRS) method, equipment health condition is determined. Prior to forecasting the condition of the equipment, the equipment degradation state is determined by the fuzzy C-means clustering method. Based on the current state of equipment and pre-determined state of degradation, the Remaining Useful Life of the equipment is estimated. In order to evaluate the model, sensor data for PHM 2012 challenge have been used to forecast the bearing’s Remaining Useful Life And the results of the study have been compared with the winning results. One of the specifications of the proposed model is to determine the confidence intervals for Remaining Useful Life. Its innovations include the use of fuzzy clustering and evidence theory to integrate data and use Autoregressive Markov Regime Switching to prognosis.