A Novel Identification Scheme for Physical Systems with Applications to System Health Monitoring
Bibliographic record
Abstract
A novel identification scheme is proposed here for a wide class of nonlinear and highly complex physical systems, including manufacturing ones, which can be modelled using the linear parameter-varying (LPV) approach. The targeted applications include status monitoring, condition-based maintenance and fault diagnosis. In this approach, several operating points, selected over the entire system's operating regime, are used to model the system at each such point, by a linear model such that the set of all such local linear models forms the best approximation of the original nonlinear system. To ensure reliability and accuracy of both the proposed identification scheme and its applications, emulators are included at the output measurements to mimic likely operating scenarios resulting from variations in the subsystems such as the controller, sensor, actuators and plant. At each operating point, data collected from various emulator parameter-perturbed experiments is used to identify both the system and the influence vectors. The influence vector, a novel concept used here, is used to map the variations of the emulator parameters (and hence those of the subsystems) to the feature vector (transfer function coefficients). At the heart of the system's health monitoring setup used is a Kalman filter which uses its residual to detect a fault and the influence vector to isolate the faulty subsystem. The novel use of emulators and influence vectors is shown here to spawn various contributions, namely a generalization of the traditional LPV approach, a new multi-model LPV-based identification scheme, and hence a new health monitoring system based on it, that are both reliable, robust and accurate. The superiority of the performance of the proposed novel identification scheme over that of conventional schemes is shown both analytically and through a successful evaluation on both simulated and physical systems.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".