Predictive Maintenance by the Unsupervised Clustering of Gradual Faults in a fleet of IoT-based Public Buses

Predictive maintenance involves collecting data from machines and using algorithms to analyze the machine’s condition or determine if the machine requires maintenance or repairs. This work presents a clustering-based algorithm for predictive maintenance that detects potential faults and gradual deterioration for IoT-based buses. 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.