Anomaly Detection in Cold Storage Systems: A Machine Learning Approach for Fault Diagnosis
Bibliographic record
Abstract
Cold storage systems play a crucial role in preserving temperature-sensitive goods. However, they are susceptible to various faults that can compromise operational efficiency and product safety. Traditional rule-based fault detection methods are limited by their rigidity and lack of adaptability. In contrast, this study introduces a machine learning (ML)-based framework for anomaly detection and fault diagnosis in cold storage environments. The proposed framework combines autoencoders for unsupervised anomaly detection with gradient boosting classifiers for supervised fault categorization. It addresses key challenges such as data imbalance, temporal drift, and sensor noise. Experimental results on an industrial cold storage dataset show that the framework achieves high fault detection accuracy, reduced false alarm rates, and strong generalization to unseen anomalies. These findings demonstrate the effectiveness of ML approaches in enabling proactive and scalable fault diagnosis in cold storage systems.
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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 | 0.001 | 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".