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

Abstract

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.

Predictive maintenance of actuators in linear systems: A receding horizon set‐theoretic approach

“…Repairs performed too soon or too late could incur in added expenses and/or severe component failures. In order to guarantee the above mentioned properties, it is essential to develop effective supervision and diagnosis methods providing resiliency and fault tolerance 2,3 …”

“…For these reasons, prognostics and health monitoring (PHM) for actuators are gaining interest among researchers 3,5,6 . Among them, a particular focus is related to the determination of the actuators' healthy status in order to estimate their time‐to‐failure or remaining‐useful‐lifetime (RUL) 7 .…”

“…For these reasons, prognostics and health monitoring (PHM) for actuators are gaining interest among researchers. 3,5,6 Among them, a particular focus is related to the determination of the actuators' healthy status in order to estimate their time-to-failure or remaining-useful-lifetime (RUL). 7 The latter quantity can be exploited in a maintenance plan to schedule future interventions to prevent undesired monitored device crash.…”

A RUL Estimation System from Clustered Run-to-Failure Degradation Signals

“…This evolution has added many sensors for better control, higher system reliability, and information availability. Given this improvement in data availability, new adequate maintenance policies can be developed [ 1 ]. Thus, maintenance policies have evolved from waiting to fix the system when a failure appears (known as reactive maintenance) to predictive maintenance, where intervention is scheduled with the information obtained from fault detection methods.…”

Improving Prescriptive Maintenance by Incorporating Post-Prognostic Information Through Chance Constraints

“…As systems grow, so will the number of detections and diagnoses, and what maintenance areas need is to have reliable plans that help them balance the cost of preventive measures with the ones caused by undetected or untreated failures [13], [14]. In this setting, prescriptive analytics tools might hold the key to improving the efficiency and efficacy of these complex systems, taking advantage of the plethora of operational data sources that are now available, if these systems can handle the uncertainties inherent with prognostic Researchers have recently been dealing with uncertainty and component connections in maintenance planning from the decision-making perspective [14]- [16]. Covering both aspects will be essential for large and complex production facilities like wind farms [17], solar generators, and even scientific instruments such as the ALMA radio telescope [5].…”