Fault Prognosis with Stochastic Modelling on Critical Points of Discrete Processes

Thi-Bich-Lien Nguyen, Mohand Djeziri, Bouchra Ananou, Mustapha Ouladsine, and Jacques Pinaton
Submission Type: 
Full Paper
phmce_14_064.pdf323.96 KBMay 21, 2014 - 1:29pm

The primary role of a machine tool is produce the good quality parts, but a machine tool goes always through a process of degradation and wear which will affect the accuracy and precision of machining and the quality of products. Therefore, monitoring the degradation of machine tool and quantifying its health is very important. The degradation level of a machine can be qualified by an index which is called health indicator (HI). Based on the HI, fault prognosis can provide the Remaining Useful Life (RUL) of machine which is useful for an effective maintenance policy, thus, that helps to increase efficiency of operations and manufacturing. However, the HI is not usually predetermined in most Discrete Manufacturing Processes (DMP). This paper presents a new method of HI extraction based on the degradation reconstruction. The HI is then modeled with a stochastic process. For the online supervision, the RUL is estimated for each inspection time.

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Submission Keywords: 
fault prognostics
health index
Stochastic Modeling
Submission Topic Areas: 
Data-driven methods for fault detection, diagnosis, and prognosis
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