A novel deep multi‐task learning model for spatial–temporal fault detection and diagnosis in industrial systems
Bibliographic record
Abstract
Abstract The rapid advancements in big data and machine learning have significantly enhanced fault diagnosis in complex industrial systems. However, traditional data‐driven approaches often neglect the underlying physical relationships within these systems, resulting in models that identify correlations without providing interpretable explanations for their diagnostic conclusions. To address this limitation, we propose a novel method, the deep multi‐task learning graph convolution network and gated recurrent network (DMTL‐GCNGRU). This approach integrates graph convolution networks (GCN) with gated recurrent units (GRU) for effective feature extraction, enabling the simultaneous capture of both spatial and temporal patterns critical for fault detection and diagnosis. Further strengthening the model, we incorporate multi‐head attention mechanisms and residual connections, which together ensure robust feature propagation, improve the model focus on relevant sequence information, and mitigate the vanishing gradient problem that is commonly encountered in deep learning architectures. The DMTL‐GCNGRU model represents a significant advancement in fault diagnosis by utilizing a shared decoder framework for multi‐task learning, which enables the model to learn common representations across multiple diagnostic tasks, enhancing its efficiency, robustness, and generalizability. These innovations empower the model to reliably detect and diagnose faults in complex industrial environments, even under challenging and dynamic conditions. The model is evaluated on two real‐world datasets: the Tennessee Eastman process (TEP) and the hot strip mill process (HSMP). The results underscore the model's ability to handle intricate fault scenarios with robustness and precision, establishing it as a highly effective tool for real‐time industrial fault detection and diagnosis. deep learning, deep multi‐task learning, multi‐head attention mechanisms, residual connections, industrial fault detection and diagnosis.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".