Abstract
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.
Highlights
PHM was widely considered by industrials as an approach for the health management of systems
This paper reviewed the most methods used in the diagnostic of faults in industrial systems
These methods reinforced by prognosis methods can lead to an effective reduction of unplanned outages and increase reliability and dependability of systems and reduces maintenance costs
Summary
PHM was widely considered by industrials as an approach for the health management of systems. The PHM is composed of a key milestone conditioning its success: the diagnostic This latter is defined as a process of detection and location of faults. The diagnostic represents the information obtained by sensors in a space composed by features These features generate a representation space allowing the identification of faults (Jardine et al, 2006). (Atoui et al, 2016; Benkouider et al, 2012; Ghosh et al, 2011; Lin et al, 2004; Maurya et al, 2007; Siswantoro et al, 2016; Zhao et al, 2013) highlighted a new family called hybrid methods in order to optimize the diagnostic performance International Journal of Prognostics and Health Management, ISSN 2153-2648, 2019 033
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