Simultaneously anomaly detection and forecasting for predictive maintenance using a zero-cost differentiable architecture search-based network
Bibliographic record
Abstract
To prevent costly failures and unplanned downtime, predictive maintenance for industrial machinery requires accurate forecasting and early anomaly recognition. This paper introduces a novel zero-cost Differentiable Neural Architecture Search framework for Vibration Analysis (DNAS-VA) that simultaneously optimizes forecasting and anomaly detection in vibration signals. The proposed approach automatically discovers the most appropriate neural network architectures by exploring a search space combining time and frequency-domain operations, including Fourier and wavelet transforms, attention mechanisms, and temporal modeling components. A Forecasting-Integrated Variational Autoencoder (FI-VAE) enhances anomaly detection by combining reconstruction error, latent space analysis, and temporal pattern assessment. The methodology employs a hierarchical training protocol to optimize both architecture search and anomaly detection performance. Experiments in real triaxial vibration data from an industrial motor demonstrate the framework’s effectiveness. The discovered architecture achieves superior forecasting performance, with mean absolute errors of 0.118–0.156 across vibration axes, and robust anomaly detection, outperforming baseline methods like Isolation Forest. Main innovations include a multi-fidelity evaluation strategy using zero-cost metrics, such as Fisher Information, correlation equal 0.90, to efficiently identify high-performing architectures without full training cycles. Latent space analysis reveals interpretable clusters corresponding to operational states, with anomalies detected at cluster boundaries. The results show that the integrated framework significantly improves predictive maintenance by enabling accurate forecasting and reliable early fault detection while reducing computational costs. The proposed method achieves state-of-the-art performance in both tasks, offering a scalable solution for industrial condition monitoring.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".