Optimizing Performance of Equipment Fleets in Dynamic Environments: A Straightforward Approach to Detecting Shifts in Component Operational States
Bibliographic record
Abstract
In modern cyber-physical systems (CPS), sophisticated equipment is equipped with sensors that record high-resolution multivariate time series (MVTS) data. Alarm systems based on static rules and parameters are known to erroneously trigger alerts called “false positives” or, even more problematically, fail to detect actual faults, referred to as “false negatives”. Due to these limitations, alarm systems generally do not reflect the complex behaviors of the equipment and the overall characteristics of the system, including the interdependencies between different components. Several studies aim to establish a more intelligent alarm system by relying on filtering processes and dynamic logics including alarm rationalization, dynamic alarm management, risk-based prioritization (probabilistic approach), or the analysis of time series combined with machine learning models. Despite these efforts, the proposed solutions are difficult to generalize across different types of alarm systems. To address this, this article presents a new approach that efficiently uses sensor data to detect anomalies and label the operational states of all the equipment comprising the analyzed system.
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) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| 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".