Volume 19, Issue 10 (October 2019)                   Modares Mechanical Engineering 2019, 19(10): 2351-2365 | Back to browse issues page

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1- School of Industrial Engineering, Iran University of Science and Technology, Tehran, Iran
2- School of Industrial Engineering, Iran University of Science and Technology, Tehran, Iran , moini@iust.ac.ir
Abstract:   (6769 Views)
Condition assessment is one of the most significant techniques of equipment health, repairs, maintenance, and management. Prognostics and Health Managemen (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 presented model, the Remaining Useful Life (RUL) is estimated using equipment condition assessment. Using the simulation and forecasting of a new feature for the vibration of the equipment (Kurtosis-Entropy) by Autoregressive Markov Regime Switching (AMRS) 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, the experimental data from the FEMTO-ST Institute, which is provided to estimate the remaining useful life of the bearing, was used and the results of the study are compared with the rival models. The innovation of this paper is the use of fuzzy C-means, a new approach to evidence theory for data fusion, and the use of the Markov switching model for prediction.
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Article Type: Original Research | Subject: Vibration
Received: 2018/07/30 | Accepted: 2019/02/13 | Published: 2019/10/22

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