Modified Grammian Angular Field for spatially informed induction motor fault diagnosis using feature fusion for fault tolerant control applications
Bibliographic record
Abstract
Early detection of bearing faults is crucial for ensuring the operational safety and fault-tolerant control (FTC) of motors. Traditional 2D representations of 1D time series data often fail to preserve temporal variations within a spatial context, limiting their effectiveness in fault diagnosis and control adaptation. To address this limitation, Grammian Angular Field (GAF) provides a structured encoding of time series data. This paper introduces a Modified Grammian Angular Field (M. GAF), a refined feature representation that enhances fault detection and enables adaptive control strategies. Our approach employs a weighted overlapping technique that fuses the Grammian Angular Difference Field (GADF) and logarithmic Grammian Angular Summation Field (Log. GASF), effectively capturing intricate temporal patterns and spectral features. The enhanced M. GAF representation serves as the foundation for our proposed Involution Convolution Feature Concatenation (I. C. FC) framework, which extracts both channel agnostic and spatial-specific, as well as spatial agnostic and channel-specific features, facilitating robust fault diagnosis and real-time control adaptation. Experimental results demonstrate the robustness of our approach, achieving 99.74% accuracy on the University of Ottawa dataset and 100% accuracy on the Case Western Reserve University dataset. Furthermore, our methodology contributes to the advancement of FTC strategies by enabling intelligent fault detection.
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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 | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".