Optimizing Performance of Equipment Fleets in Dynamic Environments: A Straightforward Approach to Detecting Shifts in Component Operational States
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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 it