Early Fault Detection Scheme and Optimized CBM Strategy for Gear Transmission System Operating under Varying Loads
Bibliographic record
Abstract
The development of fault detection schemes and optimized condition-based maintenance (CBM) strategies for gear transmission systems has received considerable attention in recent years. Most models considered the gear systems operating under constant load. Constant load assumptions imply that changes in condition monitoring data are caused only by deterioration of the gear systems. However, most real gear systems operate under varying loads and speeds which affect the condition monitoring data signature of the system. This typically makes it difficult to recognize the occurrence of an impending fault and to optimize a CBM strategy.\nThis thesis first presents a novel approach to detect and localize the gear failure occurrence for a gear system operating under varying load conditions. An autoregressive model with exogenous variables is fitted to the time-synchronously averaged (TSA) condition monitoring data when the gear transmission system operated under various load conditions in good state. The fault detection and localization indicator is calculated by applying F-test to the residual signals of the ARX model. Then, the gear deteriorating process is modeled as a three state hidden continuous Markov model with partial information, and the model parameters are estimated using ARX model residuals. The pseudo likelihood function is maximized and the EM algorithm is applied. Finally, a multivariate Bayesian process control is developed to optimize the decision variables of the CBM strategy: the time interval for next data collection and the control limit for the posterior probability calculated at next data collection epoch. The decision variables are calculated by minimizing the total expected cost from current stage to the end of the production run. Dynamic programming is applied to update the decision variables after new monitoring data are acquired, and the maintenance decision is made by taking all available information into account.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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 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".