Fault detection in sensors using single and multi-channel weighted convolutional neural networks
Bibliographic record
Abstract
The success of sensor based application depends on the availability of clean data and its fail safe operation. However, due to their inherent dynamical behavior, detecting faults in sensors can be challenging. To this end, we propose a signal processing and nonlinear dynamics based fault detection approach for learning the normal/abnormal states of sensors using machine learning. The characteristic traits in detecting faults are the textured images that are generated from the time series of sensor measurements. Our approach uses these textured images that capture the dynamical properties of the sensor systems along with spectrograms with weighted Convolutional Neural Networks (wCNNs). We first test our approach on a radar sensor system since identifying faults in such a system is difficult due to their inherent complex dynamics. An evaluation of the multi-channel wCNN model for radar fault detection shows its robustness to tolerate noise. The efficiency of our approach is validated on a real data set from a gearbox sensor system. Results demonstrate that the unique patterns from the image representation convey valuable information about the dynamical states of the sensor systems in improving 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 | 0.000 | 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 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".