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It demonstrates that predictive maintenance enhances cost and time efficiency and improves user safety by enabling preemptive maintenance actions. While the predictive models implemented in this article focus on the cooling and engine torque systems, the methodology proposed is flexible and can be extended to other subsystems. To mitigate the problem of insufficient data, this work also generates synthetic datasets to simulate normal buses and buses with potential faults. Experiments on synthetic datasets simulating 78 buses deliver high-quality clusters with silhouette scores as high as 0.99 (cooling system) and 0.88 (engine system). Furthermore, the clusters identify the faulty components with an accuracy of 100%, that is, all the buses with potential faults were detected successfully. Predictive maintenance frameworks usually require large volumes of labeled data and suffer from imbalance issues; however, the proposed methodology in this article delivers highly accurate results even in the absence of large volumes of labeled data while being robust against imbalanced cases. Overall, this work contributes to predictive maintenance by presenting an efficient and practical solution that ensures the reliability and safety of transportation systems.<\/jats:p>","DOI":"10.1145\/3773280","type":"journal-article","created":{"date-parts":[[2025,10,25]],"date-time":"2025-10-25T10:11:32Z","timestamp":1761387092000},"page":"1-31","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":0,"title":["Predictive Maintenance by the Unsupervised Clustering of Gradual Faults in a fleet of IoT-based Public Buses"],"prefix":"10.1145","volume":"22","author":[{"ORCID":"https:\/\/orcid.org\/0009-0003-0708-4251","authenticated-orcid":false,"given":"Gautam","family":"Vira","sequence":"first","affiliation":[{"name":"School of Electrical Engineering and Computer Science, University of Ottawa","place":["Ottawa, Canada"]}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6039-6751","authenticated-orcid":false,"given":"Tet","family":"Yeap","sequence":"additional","affiliation":[{"name":"School of Electrical Engineering and Computer Science, University of Ottawa","place":["Ottawa, Canada"]}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9119-9451","authenticated-orcid":false,"given":"Iluju","family":"Kiringa","sequence":"additional","affiliation":[{"name":"School of Electrical Engineering and Computer Science, University of Ottawa","place":["Ottawa, Canada"]}]}],"member":"320","published-online":{"date-parts":[[2026,1,7]]},"reference":[{"key":"e_1_3_1_2_2","doi-asserted-by":"publisher","DOI":"10.1145\/3434581.3434619"},{"key":"e_1_3_1_3_2","doi-asserted-by":"publisher","DOI":"10.5555\/3000850.3000887"},{"key":"e_1_3_1_4_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.trc.2019.02.001"},{"key":"e_1_3_1_5_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.engappai.2011.03.002"},{"key":"e_1_3_1_6_2","doi-asserted-by":"publisher","DOI":"10.1002\/wics.1460"},{"key":"e_1_3_1_7_2","doi-asserted-by":"publisher","DOI":"10.1109\/EMBC.2018.8512873"},{"key":"e_1_3_1_8_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.procir.2019.03.077"},{"key":"e_1_3_1_9_2","doi-asserted-by":"publisher","DOI":"10.1109\/TR.2020.3007504"},{"key":"e_1_3_1_10_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.promfg.2020.04.032"},{"key":"e_1_3_1_11_2","doi-asserted-by":"publisher","DOI":"10.1145\/3090354.3090402"},{"key":"e_1_3_1_12_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.trc.2019.07.020"},{"key":"e_1_3_1_13_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.compind.2021.103554"},{"key":"e_1_3_1_14_2","unstructured":"Gretel.ai. 2024. 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