Bibliographic record
Abstract
A stochastic model was developed determining the optimal policy for monitoring and planned preventive maintenance in a manufacturing process. Specifically, this model integrates the multivariate Exponentially Weighted Moving Average (EWMA) chart and preventative maintenance to minimize the total costs associated with monitoring and maintenance by jointly optimizing the inspection and maintenance policies. The objective is to determine the interval between samples, the control limit, and the multivariate EWMA exponential weight minimizing the expected average cost per unit time. This model can be applied to the other situation when there is a typical warning state. This study focuses on the cross study of multivariate control chart and condition-based maintenance, which have been extensively studied in isolation but limitedly in an integrated way. Specifically, the multivariate statistical process control charts method is applied to condition based maintenance. My research efforts are divided between analyzing vibration datasets for failure diagnosis and developing a new stochastic model for optimization of maintenance policies. The main tasks of the failure diagnosis in the rotating machinery are to detect the incipient failure and identify the failure mode or pattern. A novel failure diagnosis scheme for gearboxes was proposed. I used a combination of multivariate time series modeling, dynamic principal component analysis method, and multivariate control chart to implement failure diagnosis. The research results are very appealing in three aspects: First, it provides the whole picture of teeth health condition in one single analysis. Second, it not only reduces the probability of false alarms but also improves the reliability by distinguishing the real alarm pattern from the false alarm pattern. Third, the failure mode of adjacent teeth fracture can be identified by visual inspection from the graph.
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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".