Adaptive System Identification and Severity Index-Based Fault Diagnosis in Motors
Bibliographic record
Abstract
In this paper, a model-based fault detection and isolation (FDI) method is presented using an adaptive system identification approach. The proposed FDI method consists of three essential steps: adaptive modeling and residual generation, fault detection using adaptive hybrid threshold, and fault identification using fault severity indices. The primary task is based on current signal modeling using an input-output identification method. The modeled signal is utilized for residual generation and a dynamic and hybrid thresholding method is used for residual analysis and fault detection. Moreover, the concept of fault severity indices is incorporated for the identification of fault type and severity level. In this study, the proposed method is experimentally investigated using an induction motor testbed. Fault detection and identification is performed for broken rotor bar as well as inner race and outer race bearing faults. Experimental results are included to demonstrate the feasibility of the proposed method for fault detection and isolation. The results demonstrate robust fault detection and accurate fault isolation. The proposed fault diagnosis method provides an efficient flexible solution for improving system reliability and safety.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".