Condition Based Maintenance of Low Speed Rolling Element Bearings using Hidden Markov Model
Bibliographic record
Abstract
This paper presents an integrated hidden Markov model (HMM) approach to undertake fault diagnosis and maintenance planning for low-speed roller element bearings in a conveyor system. The components studied are relatively long-life components for which run-to-failure data is not available. Furthermore, the large number of these components in a conveyor system makes the individual monitoring of each bearing impractical. In this paper, HMM is employed to overcome both these challenges. For fault diagnosis, a number of bearings varying in age and usage were extracted from the system and tested to develop a baseline HMM model. This data was then used to calculate likelihood probabilities, which were subsequently used to determine the health state of an unknown bearing. For maintenance planning, experimentally determined thresholds from faulty bearings were used in conjunction with simulated degradation paths to parametrize a HMM. This HMM is then used to determine the state duration statistics and subsequently the calculation of residual useful life (RUL) based on bearing vibration data. The RUL distribution is then used for maintenance planning by optimizing the expected cost rate and the results so obtained are compared with the results obtained from a traditional age based replacement policy.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".