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

Abstract

Maintenance is one of the critical areas in operations in which a careful balance between preventive costs and the effect of failures is required. Thanks to the increasing data availability, decisionmakers can now use models to better estimate, evaluate, and achieve this balance. This work presents a maintenance scheduling model which considers prognostic information provided by a predictive system. In particular, we developed a prescriptive maintenance system based on run-to-failure signal segmentation and a Long Short Term Memory (LSTM) neural network. The LSTM network returns the prediction of the remaining useful life when a fault is present in a component. We incorporate such predictions and their inherent errors in a decision support system based on a stochastic optimization model, incorporating them via chance constraints. These constraints control the number of failed components and consider the physical distance between them to reduce sparsity and minimize the total maintenance cost. We show that this approach can compute solutions for relatively large instances in reasonable computational time through experimental results. Furthermore, the decision-maker can identify the correct operating point depending on the balance between costs and failure probability.INDEX TERMS prescriptive maintenance, chance constraints, remaining useful life.

Using machine learning and deep learning algorithms for downtime minimization in manufacturing systems: an early failure detection diagnostic service

“…Data-driven models such as physics or mathematics-based (statistical or stochastic) and ML-based [29] Knowledge-based models such as rule-based and case-based [29] Prescriptive: helps analyze and determine different options and potential outcomes to optimize maintenance and reduce reliability risks. It allows engineers to calculate the effects of varying the operating conditions to the time to failure [30][31][32][33] While it is still corrective, engineers should plan a complete Operation and Maintenance (O&M) system that incorporates CM into it [34] Offers the highest level of automation Might be able to suggest a potential solution if successfully combined with Arti cial Intelligence (AI)…”

Prescriptive maintenance for complex products with digital twin considering production planning and resource constraints

“…The chance constraints are implemented by setting N = 10, ϵ = 0.05, and γ = 0.04. For the rest parameters, we refer to the settings in [60] and [61]. We take the two widely used maintenance strategies, namely PM and PdM strategies, as benchmarks.…”

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

“…Later, Vachtsevanos and Wang [ 12 ] introduced prognostics algorithms as tools for predicting the time-to-failure of components; from this insight emerged PHM [ 13 ] as an extension of CBM to improve the predictability and remaining useful life (RUL) estimation of a component in question after a fault appears. This information can then be used as a supply for decision-making in maintenance scheduling [ 14 ].…”