Estimation of the value of prognostic information for condition-based and predictive maintenance

William Fauriat (1) , Enrico Zio (2)

1 LGI - Laboratoire Génie Industriel - EA 2606
2 SSEC - Chaire Sciences des Systèmes et Défis Energétiques EDF/ECP/Supélec

For components subject to degradation, cost-efficient maintenance is necessary. Periodic or continuous collection of information, reducing uncertainty on the component's state of health, generally leads to a better-informed and, thus, more efficient maintenance. Processing condition monitoring data to estimate the current and future health states of the component, can prove valuable. In this paper, it is proposed to quantify the Value of Information (VoI) that may be obtained from state estimation and prediction procedures, with known precision, applied for condition-based and predictive maintenance. VoI is computed numerically using gamma process paths and on the basis of the optimization of the parameters of different maintenance strategies.

Mots clés: Prognostics; Maintenance optimization; Remaining Useful Life; Value of Information; Predictive Maintenance; Condition-Based Maintenance

Domaines: Sciences de l'ingénieur [physics]

William Fauriat, Enrico Zio. Estimation of the value of prognostic information for condition-based and predictive maintenance. European Safety and Reliability Conference, Sep 2019, Hanover, Germany. ⟨hal-02381856⟩