Abstract
The performance of the Prognostics and Health Management (PHM) depends both on the functioning of the measurement acquisition system and on the actual state of the system being monitored. The dependencies between these systems must be considered when developing optimal inspection and maintenance strategies. This paper presents a methodology to support the definition maintenance strategies for PHM-equipped industrial systems. The methodology employs influence diagrams when seeking to maximize the expected utility of system operation. The optimization problem is solved by mixed-integer linear programming, subject to budget and technical constraints. Chance constraints can be also included, for instance to curtail risks based on measures such as the Value at Risk (VaR) and the Conditional Value at Risk (CVaR) of system operation. The viability of the methodology is demonstrated by optimizing the inspection and maintenance strategy for a gas turbine equipped with PHM solution. The computation of the Value of Perfect Information (VoPI) provides additional insights on maintenance management.
Highlights
Digitalization is a fundamental driver of Industry 4.0, a novel paradigm which enhances production efficiency through information and communication technologies [1,2]
The above considerations suggest that the definition of optimal maintenance strategy in Predictive Maintenance must be framed as a multi-stage decision problem, encoding the mutual dependence between the performances of the Prognostics and Health Management (PHM) solution and the sensor validation algorithms [23,24,25]
We employ Decision Programming to identify the optimal inspection and maintenance strategy for an industrial system with realistic PHM capabilities and sensor validation algorithms: each combination of states of the nodes of the influence diagram is mapped onto the two-stage decision maximizing the system utility
Summary
Digitalization is a fundamental driver of Industry 4.0, a novel paradigm which enhances production efficiency through information and communication technologies [1,2]. The above considerations suggest that the definition of optimal maintenance strategy in Predictive Maintenance must be framed as a multi-stage decision problem, encoding the mutual dependence between the performances of the PHM solution and the sensor validation algorithms [23,24,25] On this topic, Driessen et al [26] present a cost evaluation of maintenance strategies for a single-component system, which is periodically subject to imperfect inspections. We employ Decision Programming to identify the optimal inspection and maintenance strategy for an industrial system with realistic PHM capabilities and sensor validation algorithms: each combination of states of the nodes of the influence diagram is mapped onto the two-stage decision maximizing the system utility.
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