Open AccessPHM Survey : Implementation of Diagnostic Methods for Monitoring Industrial Systems
Author(s) -
Abdenour Soualhi,
Bilal Elyousfi,
Yasmine Hawwari,
Kamal Medjaher,
Guy Carrault,
Hubert Razik,
François Guillet
Publication year - 2019
Publication title -
international journal of prognostics and health management
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.336
H-Index - 21
ISSN - 2153-2648
DOI - 10.36001/ijphm.2019.v10i2.2733
Subject(s) - prognostics , reliability (semiconductor) , reliability engineering , life expectancy , identification (biology) , predictive maintenance , risk analysis (engineering) , modernization theory , isolation (microbiology) , condition monitoring , preventive maintenance , fault (geology) , computer science , engineering , fault detection and isolation , business , medicine , artificial intelligence , population , electrical engineering , actuator , economic growth , environmental health , biology , power (physics) , quantum mechanics , physics , botany , seismology , microbiology and biotechnology , economics , geology
The modernization of industrial sectors involves the use of complex industrial systems and therefore requires condition based maintenance. This one aims at increasing the operational availability and reducing the life-cycle while increasing the reliability and life expectancy of industrial systems. This maintenance also called predictive maintenance is a part of an emerging philosophy called PHM ‘Prognostics and Health Management’. In this paper, the PHM will be emphasized on the existing diagnostic methods used for fault isolation and identification. This depicts an important part of the PHM as it exploits the data given by the signal-processing step and its output is treated by the prognostic part. The diagnostic is mainly classified in three categories that will be highlighted in this paper.