Fault Diagnosis of Three-Phase Induction Motors Using Convolutional Neural Networks
Bibliographic record
Abstract
The challenges associated with diagnosing faults in three-phase induction motors necessitate the development of innovative, non-invasive methods that can increase efficiency and reduce costs.This study presents a novel approach to fault detection in these motors, leveraging advanced machine learning technology.The primary focus is the identification of faults related to the stator, including single-phase and three-phase faults, current interruptions, and sudden torque changes.Convolutional Neural Networks (CNN), inspired by the human visual nervous system, form the backbone of the proposed fault detection methodology.This technique utilizes external measurements for processing, circumventing the need for intrusive measures such as opening the motor or installing internal sensors.The non-intrusive nature of this method not only simplifies the process but also significantly reduces associated costs.The CNN-based approach offers superior accuracy in diagnosing faults, facilitating timely prevention measures and potentially saving human lives.It also reduces the time and effort required to identify fault types, thus minimizing motor downtime and associated costs.Simulations were conducted using MATLAB software, and individual fault scenarios were applied and analyzed.The results obtained demonstrate the efficacy of the CNN-based fault diagnosis method, thereby highlighting its potential for implementation in real-world scenarios.This study contributes to the field by providing a detailed exploration of a non-invasive, cost-effective, and highly accurate method for fault detection in three-phase induction motors.It opens avenues for further research into the application of machine learning techniques for fault diagnosis in other types of motors.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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 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".