Development of an Explainable Fault Diagnosis Framework Based on Sensor Data Imagification: A Case Study of the Robotic Spot-Welding Process
Bibliographic record
Abstract
In recent years, various advanced fault diagnostic models applying deep learning techniques have been proposed, but the confidence in model prediction in the industrial field is still low. Therefore, a method is required to establish a reliable fault diagnostic model that can provide an understandable rationale for the prediction result. This article develops an explainable fault diagnosis framework that infers the causal relationship of failure by combining domain knowledge. A novel data imagification methodology that generates fuzzy-based energy pattern image (FEPI) data using sensor signal is applied to the framework, and the physical interpretability of the FEPI data plays a key role in inferring the causality of the fault. Furthermore, a case study of the robotic spot-welding process is conducted to validate the proposed framework. Convolutional neural network (CNN)-based fault diagnostic model is trained by the FEPI data, and the result of gradient-weighted class activation mapping that traces the critical region for fault classification is interpreted by the domain knowledge to infer the failure causes. Finally, the accuracy of fault diagnosis and the performance of causal inference for the explainable fault diagnosis framework are verified together.
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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) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| 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".