Robust fault diagnosis of an electro-hydrostatic actuator using the Novel dynamic second-order SVSF and IMM strategy
Bibliographic record
Abstract
This paper introduces a new robust fault detection and identification (FDI) structure applied to an electro-hydrostatic actuator (EHA) experimental setup. This FDI structure consists of the dynamic second-order smooth variable structure filter (Dynamic second-SVSF) and the interacting multiple model (IMM) strategy. The dynamic second-order smooth variable structure filter (SVSF) is a new robust-state estimation method that benefits from the robustness and chattering suppression properties of second-order sliding mode systems. It produces robust-state estimation by preserving the first and secondorder sliding conditions such that the measurement error and its first difference are pushed towards zero. Moreover, the EHA prototype works under two different operational regimes that are the normal EHA mode and the faulty EHA mode. The faulty EHA setup contains two types of faults, namely friction and internal leakage. The FDI structure contains a bank of dynamic second-order SVSFs estimating state variables based on these models. The IMM strategy combines these filters in parallel and determines the particular operating regime based on the system models and the input-output data. Experimental results demonstrate superior performance in terms of accuracy, robustness, and smoothness of state estimates.
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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 | 0.001 | 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".