Predictive Maintenance by the Unsupervised Clustering of Gradual Faults in a fleet of IoT-based Public Buses
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".