Anomaly Detection with Convolutional Autoencoder for Predictive Maintenance
Bibliographic record
Abstract
Predictive maintenance is set to prevent downtime and failures of equipment and processes to meet the quality and availability requirements of several industrial, commercial, and even residential activities. This paper proposes CAE-AD, a novel convolutional autoencoder anomaly detection method that relies only on normal operation data for training the intelligent classi-fier. The method also accommodates a sliding window algorithm for generating the input from sensor readings, which accounts for the dynamic characteristics of the data. The anomaly detection is accomplished by comparing the convolutional autoencoder reconstruction error to a threshold value to segregate between normal and anomalous predictions. The threshold value is found by minimizing the False Positive Rates and False Negative Rates. Using a benchmark water pump sensor time-series data, the model successfully classified all water pump breakdowns and correctly identified 98.8% of anomalous data and 94.8 % of normal data using a chosen best window length of the past 37 sensor readings.
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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 | 0.001 | 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".