Intelligent Choice of Machine Learning Methods for Predictive Maintenance of Intelligent Machines

Becherer, Marius; Zipperle, Michael; Karduck, Achim P.

doi:10.32604/csse.2020.35.081

Cited by 11 publications

(2 citation statements)

References 2 publications

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“…Machine learning (ML) techniques have been preferred as a powerful predictive tool for maintaining industrial environments in recent years [2]. It discovers hidden patterns within complex and multivariate data where the traditional methods (e.g., statistical inference methods) remain inadequate.…”

Section: Introductionmentioning

confidence: 99%

An Ordinal Multi-Dimensional Classification (OMDC) for Predictive Maintenance

Taşer¹

2023

Computer Systems Science and Engineering

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Predictive Maintenance is a type of condition-based maintenance that assesses the equipment's states and estimates its failure probability and when maintenance should be performed. Although machine learning techniques have been frequently implemented in this area, the existing studies disregard to the natural order between the target attribute values of the historical sensor data. Thus, these methods cause losing the inherent order of the data that positively affects the prediction performances. To deal with this problem, a novel approach, named Ordinal Multi-dimensional Classification (OMDC), is proposed for estimating the conditions of a hydraulic system's four components by taking into the natural order of class values. To demonstrate the prediction ability of the proposed approach, eleven different multi-dimensional classification algorithms (traditional Binary Relevance (BR), Classifier Chain (CC), Bayesian Classifier Chain (BCC), Monte Carlo Classifier Chain (MCC), Probabilistic Classifier Chain (PCC), Classifier Dependency Network (CDN), Classifier Trellis (CT), Classifier Dependency Trellis (CDT), Label Powerset (LP), Pruned Sets (PS), and Random k-Labelsets (RAKEL)) were implemented using the Ordinal Class Classifier (OCC) algorithm. Besides, seven different classification algorithms (Multilayer Perceptron (MLP), Support Vector Machine (SVM), k-Nearest Neighbour (kNN), Decision Tree (C4.5), Bagging, Random Forest (RF), and Adaptive Boosting (AdaBoost)) were chosen as base learners for the OCC algorithm. The experimental results present that the proposed OMDC approach using binary relevance multi-dimensional classification methods predicts the conditions of a hydraulic system's multiple components with high accuracy. Also, it is clearly seen from the results that the OMDC models that utilize ensemble-based classification algorithms give more reliable prediction performances with an average Hamming score of 0.853 than the others that use traditional algorithms as base learners.

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Section: Introductionmentioning

confidence: 99%

An Ordinal Multi-Dimensional Classification (OMDC) for Predictive Maintenance

Taşer¹

2023

Computer Systems Science and Engineering

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“…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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