Multi‐scale feature fusion network‐based industrial process fault diagnosis method using space–time capsule and classifier optimization
Bibliographic record
Abstract
Abstract This paper introduces a multi‐scale feature fusion deep learning network method for industrial process fault diagnosis based on spatio‐temporal capsules and classifier optimization. In the feature extraction phase, a multi‐scale residual convolution network is initially employed to extract multi‐scale features. Subsequently, the identified fault features are forwarded to the spatio‐temporal capsule network to further extract information related to time and space. After the feature extraction is completed, we replace the traditional softmax classifier with eXtreme Gradient Boosting (XGBoost) to make the final diagnosis more efficient and faster, avoiding the long diagnosis time caused by complex models. The proposed network fully takes into account the nonlinearity, timing, and high‐dimensionality of the original data. The residual network structure can solve the problem of model degradation caused by the deepening of network layers. The LSTM and capsule network structures can minimize the loss of effective feature information for features extraction and the XGBoost algorithm achieves good classification. This ‘offline training, online diagnosis’ method can avoid lengthy training and effectively improve the fault diagnosis efficiency. Our experiments on chemical engineering processes, such as the Tennessee Eastman (TE) process and industrial coking furnace, show that the proposed method significantly improves fault diagnosis accuracy.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".