Aggregation of Health Assessment Indicators of Industrial Systems

Rizzolo, Ludovic; Abichou, Bouthaina; Voisin, Alexandre; Kosayyer, Naïm

doi:10.2991/eusflat.2011.32

Cited by 11 publications

(7 citation statements)

References 11 publications

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“…In any case, there are also AHI models that are used as tools in the field of Prognostics and Health Management (P.H.M.) (Ludovic et al 2011) (Abichou et al 2012;Abichou et al 2015).…”

Section: Modelsmentioning

confidence: 99%

Strategic view of an assets health index for making long-term decisions in different industries

Fuente¹,

Guillén²,

Crespo³

et al. 2018

Safety and Reliability – Safe Societies in a Changing World

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“…In any case, there are also AHI models that are used as tools in the field of Prognostics and Health Management (P.H.M.) (Ludovic et al 2011) (Abichou et al 2012;Abichou et al 2015).…”

Section: Modelsmentioning

confidence: 99%

Strategic view of an assets health index for making long-term decisions in different industries

Fuente¹,

Guillén²,

Crespo³

et al. 2018

Safety and Reliability – Safe Societies in a Changing World

View full text Add to dashboard Cite

“…These indicators are considered as a snapshot of the system condition/degradation in comparison to a reference, considering various aspects such as performances, environment etc. (Rizzolo, Abichou, Voisin, & Kosayyer, 2011). An important issue, in manufacturing context, is the identification of parameters to be monitored on the asset and the elaboration of the degradation indicators.…”

Section: Problem Statement On Indicator Definition In the Frame Of Pr...mentioning

confidence: 99%

A priori indicator identification to support predictive maintenance: application to machine tool

Laloix,

Vu,

VOISIN

et al. 2018

PHME_CONF

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Predictive maintenance requires the identification of the parameters to be monitored and sensors to be implemented on a system. In industrial companies, usually such goal is tackled by implementing sensor and after see if one can extract some indicators related to degradation. The lack of methodology makes the benefits of predictive maintenance to be lower than expected. Indeed, its implementation is done by “the rule of the thumb” using some metrics “a posteriori” in order to show the relevancy of the instrumentation. Hence, a structured approach is required in order to define “a priori” the most suitable indicators to be relevant for degradation monitoring and related instrumentation to be implemented. Thus, the paper presents a methodology based on a coupled approach of FMECA and Hazard Operability analysis (HAZOP) which aim is to contribute to the deployment of predictive maintenance strategies by clearly identify pertinent indicator. This approach is based on the formalization of concepts of knowledge which permit to constitute the first pillars of predictive maintenance approach. The formalization step leads to promote meta-model and reference model of knowledge. The feasibility and the interests of such approach are shown on the case of machine tool GROB G520 located in RENAULT Cléon Factory. It consists in particularizing the reference model proposed to identify automatically and in a more efficient the right indicators/parameters on which the predictive maintenance of this machine tool should be based.

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“…According to this indicator vector, an aggregation of its values is needed in order to gather spread information over non homogeneous local indicators into a fused index called health index [18]. The Choquet integral [15] is used as aggregation operator for its ability to encompass the importance and interdependencies of indicators involved in fusion process.…”

Section: Health Check-up: Fused Health Indicesmentioning

confidence: 99%

Bottom-up capacities inference for health indicator fusion within multi-level industrial systems

Abichou

Voisin

Iung

2012

2012 IEEE Conference on Prognostics and Health Management

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In Prognostics and Health Management (PHM) considerations, data fusion technique is a corner step as it enables gathering non homogeneous condition information to provide a synthesizing view on component situations. Nevertheless with regards to complex industrial systems, efficient health monitoring should be addressed not only at component level but also at higher system abstraction levels since the decision making procedure relies on the global industrial system state. To face this issue, one way consists first in gathering, in the form of vector, the different indicators carrying most important health information related to each system function. Then, a health state has to be computed by fusing each indicator vector taking into account their relevance and their relationships in accordance with the considered system level. It is proposed in this paper to use Choquet integral as an aggregation operator to support this health state development for each function within the system breaking up. It implies to formalize the capacities (Choquet integral parameters) from a bottom-up approach allowing to infer capacities of each system function from those computed at component level. It leads to trace the relevance of health information across the system levels. The feasibility and interest of this fusion approach are shown on an application structured with two system levels.

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Aggregation of Health Assessment Indicators of Industrial Systems

Cited by 11 publications

References 11 publications

Strategic view of an assets health index for making long-term decisions in different industries

Strategic view of an assets health index for making long-term decisions in different industries

A priori indicator identification to support predictive maintenance: application to machine tool

Bottom-up capacities inference for health indicator fusion within multi-level industrial systems

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