A modeling framework for deteriorating control system and predictive maintenance of actuators

Langeron, Yves; Grall, Antoine; Barros, Anne

doi:10.1016/j.ress.2015.03.028

Cited by 41 publications

(11 citation statements)

References 31 publications

(52 reference statements)

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“…They also used the results in the configuration of the LQR controller parameters. The results obtained in (Vieira et al, 2015), were used by Langeron et al in (Langeron, Grall, & Barros, 2015) to model a closed loop system that can fully implement the control law regardless of its degradation. The system is controlled by a LQR in which its parameters are adapted using the degradation level of the actuators through its RUL.…”

Section: Automatic Controlmentioning

confidence: 99%

Review of Post-Prognostics Decision-Making in Prognostics and Health Management

Bougacha

Varnier

Zerhouni

2021

IJPHM

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Mainly, the prognostics and health management (PHM) process is based on three processes: the data acquisition and health assessment process in which sensors signals are acquired and processed, the diagnostic and prognostic process in which the source of failure is detected and the remaining useful life (RUL) is predicted and finally the decision- making process that refers to the term management in prognostics and health management. This paper reviews in the literature about the different aspects of decision-making in the context of PHM. The selected papers are subject to con- tent assessment and grouped according to the decision type. Additionally, this paper presents a synthesis of the previous works that helps identify new trends and deficiencies in the decision-making process. The synthesis can guide efforts for future work.

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Section: Automatic Controlmentioning

confidence: 99%

Review of Post-Prognostics Decision-Making in Prognostics and Health Management

Bougacha

Varnier

Zerhouni

2021

IJPHM

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“…-Control Decisions: Some control loops use health monitoring of the actuator in the adjustment of the controller parameters. Moreover, some works adapt the controller setpoints according to the actuator health state ( (Pereira et al, 2010), (Langeron, Grall, & Barros, 2015)). -Logistics: The needs of the system in terms of raw materials and spare parts are determined by its health state.…”

Section: Classical Phm and Motivationmentioning

confidence: 99%

Enhancing Decisions in Prognostics and Health Management Framework

Bougacha

Varnier

2021

IJPHM

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Prognostics and health management have become increasingly important in recent years. Many research studies focus on a crucial phase consisting of predicting the remaining useful life of equipment or a component. However, this step is often carried out without taking into account the decisions that will be taken later. This article aims to propose a modification of the existing PHM framework to combine the prognostics and decision-making phases in a closed loop. In this paper, the presented framework is described and some elements for its implementation are proposed. A simplifiedexample is developed to illustrate the presented methodology of post-prognostic decision enhancement.

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“…This choice is due to the three following reasons. Firstly, the Gamma deterioration process has been justified by diverse practical applications (e.g., fatigue crack growth [65], carbon-film resistors deterioration [66], corrosion damage mechanism [67], SiC MOSFET threshold voltage deterioration [68], actuator performance loss [69]) and considered appropriate by experts [70]. Secondly, using the homogeneous Gamma process can make the mathematical formulation feasible.…”

Section: A System Modelingmentioning

confidence: 99%

A Parametric Predictive Maintenance Decision-Making Framework Considering Improved System Health Prognosis Precision

2019

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Health prognosis is an advanced process to forecast the future state of systems, structures and components. Even if it is now recognized as a key enabling step for the maintenance performance improvement on systems and structures, the issue of post-prognosis maintenance decision-making (i.e., how to use prognosis results to eventually make maintenance decisions) remains open. Faced with this situation, we propose a parametric predictive maintenance decision framework that can take into account properly the system remnant life in maintenance decisions. Unlike more classical frameworks, it uses the estimated precision on the prognosis of the system residual useful life as a condition index to decide for and to schedule the interventions on the system. The proposed framework is developed for a single-unit stochastically deteriorating system, maintained through inspection and replacement operations. Using results from the theory of semi-regenerative phenomena, the analytical maintenance cost model is derived for the long-run expected maintenance cost rate. The proposed maintenance decision structure is compared to a classical benchmark framework; numerical experiments evidence the performance and the robustness of the new framework, and confirm the benefit of basing maintenance decisions explicitly on the precision of the system health prognosis (and not only on e.g., the mean value of the estimated residual life).

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A modeling framework for deteriorating control system and predictive maintenance of actuators

Cited by 41 publications

References 31 publications

Review of Post-Prognostics Decision-Making in Prognostics and Health Management

Review of Post-Prognostics Decision-Making in Prognostics and Health Management

Enhancing Decisions in Prognostics and Health Management Framework

A Parametric Predictive Maintenance Decision-Making Framework Considering Improved System Health Prognosis Precision

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