Inverter Open Switch Fault Detection Using BiLSTM Neural Network in Induction Motor Drive
Bibliographic record
Abstract
The inverter open switch fault is one of the faults that mostly occurs in electric motor drives. If this type of fault is not detected and tolerated in time, it can lead to performance degradation, high repair costs and even accidents. This paper presents a fault detection and diagnosis (FDD) method to capture single and double open switch/switches faults in a three-phase, two-level inverter fed V/F controlled induction motor. Three deep neural networks (DNNs) classifiers, namely bidirectional long short-term memory (BiLSTM) network, convolutional neural network (CNN), and fully connected neural network (FCN), were trained and tested using three-phase voltage and current rms and angle. The BiLSTM model performed the best with an overall accuracy of$\mathbf{9 9. 1 5 \%}$due to its ability to capture temporal dependencies over time. The proposed FDD method can detect faults and exact faulty switches in less than one fundamental period at different speed levels and during varying speed conditions. The results confirm the performance of the proposed FDD method, making it highly reliable for real-time applications.
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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".