Fault Diagnosis of a Sensor Network: A Distributed Filtering Approach
Why this work is in the frame
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Bibliographic record
Abstract
This paper proposes a model-based approach to develop a novel fault diagnosis scheme for a sensor network of a cascade, parallel and feedback combination of subsystems. The objective is to detect and isolate a fault in any of the subsystems and measurement sensors which are subject to disturbances and/or measurement noise. Our approach hinges on the use of a bank of Kalman filters (KF) to detect and isolate faults. Each KF is driven by either a pair (a) of consecutive sensor measurements or (b) of a reference input and a measurement. It is shown that the KF residual is a reliable indicator of a fault in subsystems and sensors located in the path between the pair of the KF's input. The simple and efficient procedure proposed here analyzes each of the associated paths and leads to both the detection and isolation of any fault that occurred in the paths analyzed. The scheme is successfully evaluated on several simulated examples and on a physical fluid system exemplified by a benchmarked laboratory-scale two-tank system to detect and isolate faults including sensor, actuator and leakage ones.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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