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. This 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
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
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) | 0.000 | 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) | 0.000 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".