Fault Detection on a Class of Robotic Manipulators using Time-variant Transmissibilities
Why this work is in the frame
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Bibliographic record
Abstract
This paper investigates detecting faults in robotic manipulators with bounded nonlinearities and time-variant parameters using transmissibility operators. Transmissibility operators are mathematical relations between a set of system responses to another set of responses within the same system. Both parameter variation and system nonlinearities are considered to be unknown. Transmissibility operators are shown in the literature to be independent of the system inputs. The bounded nonlinearities are considered as independent excitation on the system, which renders transmissibilities independent of these nonlinearities. To overcome the unknown variant parameters, we propose identifying transmissibilities using recursive least-squares in the structure of noncausal FIR models. While parameter variation can be treated as system nonlinearities, the recursive least squares algorithm is used to optimize what time-variant dynamics to include in the transmissibility operator and what dynamics to push to the system nonlinearities over time. The identified transmissibilities are then used for the purpose of fault detection in an experimental robotic arm with variant picked mass. The experimental results show the proposed approach to be used effectively to detect faults in robotic manipulators.
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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 | 0.000 | 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) | 0.001 | 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 it