Predictive Maintenance Model Based on Multisensor Data Fusion of Hybrid Fuzzy Rough Set Theory Feature Selection and Stacked Ensemble for Fault Classification

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dc.contributor.authorBuabeng, Albert
dc.contributor.authorSimons, Anthony
dc.contributor.authorFrempong, Nana Kena
dc.contributor.authorZiggah, Yao Yevenyo
dc.contributor.orcid0000-0002-7138-3526
dc.date.accessioned2024-07-26T09:48:52Z
dc.date.available2024-07-26T09:48:52Z
dc.date.issued2022
dc.descriptionThis article is published by Hindawi,2022 and is also available at https://doi.org/10.1155/2022/4372567
dc.description.abstractWith the rising demand for integrated and autonomous systems in the eld of engineering, e cient frameworks for instant detection of performance anomalies are imperative for improved productivity and cost-e ectiveness. is study proposes a systematic predictive maintenance framework based on the hybrid multisensor fusion technique of fuzzy rough set feature selection and stacked ensemble for the e cient classi cation of fault conditions characterised by uncertainties. First, a feature vector of time-domain features was extracted from 17 multiple sensor signals. en, a comparative study of six di erent Fuzzy Rough Set Feature Selection (FRFS) methods was employed to select the various combinations of optimal feature subsets for various faults classi cation tasks. e determined optimal feature subsets then served as inputs for training the stacked ensemble (ESB(STK)). In the ESB(STK), Support Vector Machine (SVM), Multilayer Perceptron (MLP), k-Nearest Neighbour (k-NN), C4.5 Decision Tree (C4.5 DT), Logistic Regression (LR), and Linear Discriminant Analysis (LDA) served as the base classi ers while the LR was selected to be the metaclassi er. e proposed hybrid framework (FRFS-ESB(STK)) improved the classi cation accuracy with the selected combinations of optimal feature subset size whiles reducing the computational cost, over tting, training runtime, and uncertainty in modelling. Overall analyses showed that the FRFS-ESB(STK) proved to be generalisable and versatile in the classi cation of all conditions of four monitored hydraulic components (i.e., cooler, valve, accumulator, and internal pump leakage) when compared with the six base classi ers (standalone) and three existing ensemble classi ers (Stochastic Gradient Boosting (SGB), Ada Boost (ADB), and Bagging (BAG)). e proposed FRFS-ESB(STK) showed an average improvement of 11.28% and 0.88% test accuracies when classifying accumulator and pump conditions, respectively, whiles 100% classi cation rates were obtained for both cooler and valve.
dc.description.sponsorshipKNUST
dc.identifier.citationHindawi Mathematical Problems in Engineering Volume 2022, Article ID 4372567, 24 pages
dc.identifier.uri10.1155/2022/4372567
dc.identifier.urihttps://ir.knust.edu.gh/handle/123456789/15879
dc.language.isoen
dc.publisherHindawi
dc.titlePredictive Maintenance Model Based on Multisensor Data Fusion of Hybrid Fuzzy Rough Set Theory Feature Selection and Stacked Ensemble for Fault Classification
dc.typeArticle
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