Using an Explainable Machine Learning Approach to Minimize Opportunistic Maintenance Interventions

Lourenço, Afonso; Fernandes, Marta; Canito, Alda; Almeida, Ana; Marreiros, Goreti

doi:10.1007/978-3-031-18697-4_4

Cited by 3 publications

(2 citation statements)

References 29 publications

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“…Articles Shapley Additive Explanations (V-A1) [72], [96], [99], [117], [118], [130], [132] [36]- [38], [42], [66], [75]- [77], [120], [131], [134], [135] Local Interpretable Model-agnostic Explanations (V-A2) [35]- [38], [44], [50], [51], [54], [61], [66], [76], [84] Feature Importance (V-A3) [34], [54], [67], [85], [86], [93], [101], [115], [137], [139] Layer-wise Relevance Propagation (V-A4) [37], [44], [68], [87], [109], [116] Rule-based (V-A5) [65], [70], [71], [73] Class Activation Mapping (CAM) and Gradient-weighted CAM (V-B1) [37], [44],…”

Section: Methodsmentioning

confidence: 99%

Explainable Predictive Maintenance: A Survey of Current Methods, Challenges and Opportunities

Cummins,

Sommers,

Ramezani

et al. 2024

IEEE Access

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Predictive maintenance is a well studied collection of techniques that aims to prolong the life of a mechanical system by using artificial intelligence and machine learning to predict the optimal time to perform maintenance. The methods allow maintainers of systems and hardware to reduce financial and time costs of upkeep. As these methods are adopted for more serious and potentially life-threatening applications, the human operators need trust the predictive system. This attracts the field of Explainable AI (XAI) to introduce explainability and interpretability into the predictive system. XAI brings methods to the field of predictive maintenance that can amplify trust in the users while maintaining well-performing systems. This survey on explainable predictive maintenance (XPM) discusses and presents the current methods of XAI as applied to predictive maintenance while following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) 2020 guidelines. We categorize the different XPM methods into groups that follow the XAI literature. Additionally, we include current challenges and a discussion on future research directions in XPM.

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Section: Methodsmentioning

confidence: 99%

Explainable Predictive Maintenance: A Survey of Current Methods, Challenges and Opportunities

Cummins,

Sommers,

Ramezani

et al. 2024

IEEE Access

View full text Add to dashboard Cite

show abstract

“…Du et al proposed a delay-time model for repair schedules [18]. Paralikar optimized the repair interventionbased machine learning methods [19][20][21]. According to the characteristics of problems, other methods, such as fuzzy logic, stochastic programming, and greedy heuristic local search algorithm, were also used to determine repair intervals [22,23].…”

Section: Introductionmentioning

confidence: 99%

Optimization of Fleet’s Level Repair Plan for Diverse Missions

Jiang

2023

Mathematical Problems in Engineering

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Various missions significantly impact the fleet’s level repair plan. The mission requirement model is established based on the “regular and dual control” repair mode. Considering the fleet’s overall operation, capability decay characteristics, and planner’s expectations and preferences, the fleet’s capability evaluation model is constructed. Furthermore, aiming at the maximum capability value and the minimum number of the mission ships and comprehensively considering constraints such as sailing rate, mission period, repair period, repair cost, capability restoration, and mission capability, the optimization model of the fleet’s level repair plan for diverse missions is established. The particle swarm optimization algorithm based on the hierarchical sequence method is used to solve the model, and the fleet’s level repair plan and mission configuration plan are obtained, which solves the coordination problem of the two types of plans. The results show that, compared with the traditional planning method, this method can fully consider the actual requirements of diverse missions and has better coordination of the relationship between the ship’s use and repair. This method can provide strong technical support for the scientific preparation of level repair plans and the effective completion of combat readiness training missions.

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Time series data mining for railway wheel and track monitoring: a survey

Lourenço,

Ribeiro,

Fernandes

et al. 2024

Neural Comput & Applic

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The railway sector has witnessed a significant surge in condition-based maintenance, thanks to the proliferation of sensing technologies and data-driven methodologies, such as machine learning. However, despite the plethora of algorithms designed to detect and classify track irregularities and wheel out-of-roundness, they often fall short when put to the test in real-world scenarios. These shortcomings typically stem from their inability to meet all four critical requirements for constructing an effective maintenance plan: (R1) suitability of the condition-based maintenance strategy, (R2) availability of relevant data, (R3) proper problem formulation, and (R4) accurate evaluation of data mining methods. In response to the absence of a unified framework and standardized guidelines, this survey delves into the realm of time series sensor data and wheel-track interface components for railway structural health monitoring. This survey aims to bridge this gap by offering an extensive categorization, pinpointing existing challenges, and outlining potential directions for future research. Through these efforts, this survey provides a more thorough and targeted exploration of the subject matter, contributing to the advancement of this field.

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Using an Explainable Machine Learning Approach to Minimize Opportunistic Maintenance Interventions

Cited by 3 publications

References 29 publications

Explainable Predictive Maintenance: A Survey of Current Methods, Challenges and Opportunities

Explainable Predictive Maintenance: A Survey of Current Methods, Challenges and Opportunities

Optimization of Fleet’s Level Repair Plan for Diverse Missions

Time series data mining for railway wheel and track monitoring: a survey

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