Balanced K-Star: An Explainable Machine Learning Method for Internet-of-Things-Enabled Predictive Maintenance in Manufacturing
Predictive maintenance (PdM) combines the Internet of Things (IoT) technologies with machine learning (ML) to predict probable failures, which leads to the necessity of maintenance for manufacturing equipment, providing the opportunity to solve the related problems and thus make adaptive decisions i...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-02-01
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| Series: | Machines |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2075-1702/11/3/322 |
| _version_ | 1797610579405307904 |
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| author | Bita Ghasemkhani Ozlem Aktas Derya Birant |
| author_facet | Bita Ghasemkhani Ozlem Aktas Derya Birant |
| author_sort | Bita Ghasemkhani |
| collection | DOAJ |
| description | Predictive maintenance (PdM) combines the Internet of Things (IoT) technologies with machine learning (ML) to predict probable failures, which leads to the necessity of maintenance for manufacturing equipment, providing the opportunity to solve the related problems and thus make adaptive decisions in a timely manner. However, a standard ML algorithm cannot be directly applied to a PdM dataset, which is highly imbalanced since, in most cases, signals correspond to normal rather than critical conditions. To deal with data imbalance, in this paper, a novel explainable ML method entitled “Balanced K-Star” based on the K-Star classification algorithm is proposed for PdM in an IoT-based manufacturing environment. Experiments conducted on a PdM dataset showed that the proposed Balanced K-Star method outperformed the standard K-Star method in terms of classification accuracy. The results also showed that the proposed method (98.75%) achieved higher accuracy than the state-of-the-art methods (91.74%) on the same data. |
| first_indexed | 2024-03-11T06:16:11Z |
| format | Article |
| id | doaj.art-5721e67f049c48669ca98eb68241f765 |
| institution | Directory Open Access Journal |
| issn | 2075-1702 |
| language | English |
| last_indexed | 2024-03-11T06:16:11Z |
| publishDate | 2023-02-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Machines |
| spelling | doaj.art-5721e67f049c48669ca98eb68241f7652023-11-17T12:14:53ZengMDPI AGMachines2075-17022023-02-0111332210.3390/machines11030322Balanced K-Star: An Explainable Machine Learning Method for Internet-of-Things-Enabled Predictive Maintenance in ManufacturingBita Ghasemkhani0Ozlem Aktas1Derya Birant2Graduate School of Natural and Applied Sciences, Dokuz Eylul University, 35390 Izmir, TurkeyDepartment of Computer Engineering, Dokuz Eylul University, 35390 Izmir, TurkeyDepartment of Computer Engineering, Dokuz Eylul University, 35390 Izmir, TurkeyPredictive maintenance (PdM) combines the Internet of Things (IoT) technologies with machine learning (ML) to predict probable failures, which leads to the necessity of maintenance for manufacturing equipment, providing the opportunity to solve the related problems and thus make adaptive decisions in a timely manner. However, a standard ML algorithm cannot be directly applied to a PdM dataset, which is highly imbalanced since, in most cases, signals correspond to normal rather than critical conditions. To deal with data imbalance, in this paper, a novel explainable ML method entitled “Balanced K-Star” based on the K-Star classification algorithm is proposed for PdM in an IoT-based manufacturing environment. Experiments conducted on a PdM dataset showed that the proposed Balanced K-Star method outperformed the standard K-Star method in terms of classification accuracy. The results also showed that the proposed method (98.75%) achieved higher accuracy than the state-of-the-art methods (91.74%) on the same data.https://www.mdpi.com/2075-1702/11/3/322machine learningpredictive maintenanceInternet of Thingsexplainable artificial intelligenceclassificationmanufacturing |
| spellingShingle | Bita Ghasemkhani Ozlem Aktas Derya Birant Balanced K-Star: An Explainable Machine Learning Method for Internet-of-Things-Enabled Predictive Maintenance in Manufacturing Machines machine learning predictive maintenance Internet of Things explainable artificial intelligence classification manufacturing |
| title | Balanced K-Star: An Explainable Machine Learning Method for Internet-of-Things-Enabled Predictive Maintenance in Manufacturing |
| title_full | Balanced K-Star: An Explainable Machine Learning Method for Internet-of-Things-Enabled Predictive Maintenance in Manufacturing |
| title_fullStr | Balanced K-Star: An Explainable Machine Learning Method for Internet-of-Things-Enabled Predictive Maintenance in Manufacturing |
| title_full_unstemmed | Balanced K-Star: An Explainable Machine Learning Method for Internet-of-Things-Enabled Predictive Maintenance in Manufacturing |
| title_short | Balanced K-Star: An Explainable Machine Learning Method for Internet-of-Things-Enabled Predictive Maintenance in Manufacturing |
| title_sort | balanced k star an explainable machine learning method for internet of things enabled predictive maintenance in manufacturing |
| topic | machine learning predictive maintenance Internet of Things explainable artificial intelligence classification manufacturing |
| url | https://www.mdpi.com/2075-1702/11/3/322 |
| work_keys_str_mv | AT bitaghasemkhani balancedkstaranexplainablemachinelearningmethodforinternetofthingsenabledpredictivemaintenanceinmanufacturing AT ozlemaktas balancedkstaranexplainablemachinelearningmethodforinternetofthingsenabledpredictivemaintenanceinmanufacturing AT deryabirant balancedkstaranexplainablemachinelearningmethodforinternetofthingsenabledpredictivemaintenanceinmanufacturing |