Automated Condition-Based Monitoring of Automated People Movers
Bibliographic record
Abstract
Cable-driven automated people movers (APMs) are complex, dynamic systems that are comprised of many critical rotating components such as bearings, gears, shafts, and line sheaves. Consequently, these systems are susceptible to a number of faults such as bearing failures, shaft misalignments, or gear tooth deterioration. Due to the continual operative nature of many APM systems, downtimes due to faults or superfluous maintenance can result in significant economic losses. Recently, preventative maintenance strategies have become an attractive alternative to traditional run-to-failure or scheduled maintenance strategies. Condition-based maintenance is a preventative maintenance strategy that works by detecting incipient faults within the critical components of a system through the identification of anomalies within condition indicators extracted from their vibration signatures. A fault is detected when a condition indicator exceeds a prescribed failure threshold. Traditionally, these failure thresholds are based on historical precedents or failure data obtained from similar specimens. However, due to their longevity and bespoke construction, many APMs lack the historical failure data required to define these failure thresholds in the traditional way. In this paper, an automated CBM framework developed specifically for a cable-driven APM gearbox is presented. The framework utilizes a state-based Gaussian mixture modeling approach to account for variations in operating conditions resulting from frequent start and stop phases, variations in passenger load, and fluctuations in cable tension, among other variables. Failure threshold setting is accomplished using a hierarchical Bayesian approach, which utilizes past and present data to set thresholds that adapt to changing health state of the machine over its lifespan. In addition to real-time monitoring, the proposed framework utilizes the collected data to model the degradation of the system in order to generate an estimate of its remaining useful life. The combination of short-term preventative maintenance (real-time fault detection) and long-term predictive maintenance coupled with automation, makes the presented approach a highly efficient tool for maintenance planning.
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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 | 0.000 | 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".