Detection and Localization of a Failure in a Pipeline Using a Kalman Filter: An Intelligent Integrated Approach Powered by Bayesian Classification
Bibliographic record
Abstract
An integrated approach, based on the fusion of Model-Based Approach (MBA) and Model-Free Approaches (MFA) and powered by Bayesian classification, is proposed to ensure high probability of correct estimation of leakage detection and localization with low false alarm probability to prevent disastrous consequences to the economy and environment. To ensure mathematical tractability, the nonlinear model is better approximated using linear parameter-varying (LPV) model at various operating points indicated by scheduling variables. Flows at various pipeline sections are measured and transmitted wirelessly to a monitoring station. If there is a difference in the flows across a section, it indicates a leakage, and a drone is then sent to determine the exact location of the leakage. The pipeline trajectory is accurately estimated by a human operator. Using the input and the trajectory output, termed signal, an Autonomous Kalman filter (AKF) is designed to ensure accurate tracking of the desired trajectory. The emulator-generated data are used to identify the system, complement historical data to MFA, and develop the classifier fusion. The leakage is sequentially diagnosed by judiciously selecting the most appropriate approach (MFA or MBA) to ensure a fast and accurate diagnosis. The proposed scheme was evaluated on a physical 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 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".