Predictive Maintenance by Detection of Gradual Faults in an IoT-Enabled Public Bus
Bibliographic record
Abstract
The widespread incorporation of Internet of Things (IoT) devices in various systems such as mobile phones, vehicles, and security systems is used to collect data. These large volumes of data can be used to train models to make predictions about various things. One such application that uses data from IoT devices is called predictive maintenance. 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. The work in this paper presents an algorithm to perform predictive maintenance by detecting potential faults, and the algorithm is tested for the cooling system of a bus. We also generate a synthetic time series dataset to simulate normal buses, and buses with underlying intermittent faults and gradual deterioration that would not be detected by the standard maintenance systems. The data is used to train clustering models that identify deterioration patterns and detect faults before the deterioration patterns result in failure or breakdowns.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 teacher head, 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".