Fault diagnosis for multi-state equipment with multiple failure modes
Bibliographic record
Abstract
Multi-state systems have received considerable attention recently with regards to reliability and maintenance. Since most mechanical equipment operates under some sort of stress or load, it tends to deteriorate or degrade over time, thus possibly resulting in discrete degradation states (damage degrees), ranging from perfect functioning to complete failure. This multi-state deterioration is the motivation for using condition monitoring tools for the purpose of modeling, diagnosis, prognosis, and condition-based maintenance. Most mechanical equipment is subject to multiple independent failure modes and degradation processes. Rather than independently diagnosing and prognosing the health condition of the equipment for a single failure mode, it is important to investigate how multi-dimensional condition monitoring information can be used for recognition purposes of the state of the health of equipment with multiple independent failure modes. This paper focuses on a non-repairable piece of equipment with multiple independent failure modes, in which the state of the equipment for each single failure mode is not directly observable and only incomplete information is available through condition monitoring. The main objective of this paper is to develop a method to obtain an observation probability matrix which can be used as the main tool for damage degree classification of each failure mode. An observation probability matrix represents the statistical relationship between the actual health state (damage degree and failure mode) of the equipment and the condition monitoring information. This observation probability matrix is an input for such methods, as hidden Markov models, hidden Semi-Markov models, and Naïve Bayes classifiers. We modify the Naïve Bayes classifier to use this observation probability matrix for classification. The result of this paper is applied for damage degree classification of a planetary gearbox, which is subject to multiple failure modes.
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 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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".