Adaptive System Identification and Severity Index-Based Fault Diagnosis in Motors
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| 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) | 0.000 | 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 it