Application of a Remote Health Monitoring System for Pipeline Bolted Joints
Bibliographic record
Abstract
Early detection of bolt loosening is a major concern in the oil and gas industry. In this study, a vibration-based health monitoring strategy has been developed for detecting loosened bolts in pipeline. Both numerical and experimental studies are conducted to verify the integrity of the proposed method. Several damage scenarios for a bolted joint connecting two steel pipes (ASTM A53/A53M–07) are considered by simulating the loosening of the bolts through varying the applied torque on each bolt. An electric impact hammer is used to excite the pipe’s vibration in a consistent manner. The induced vibration signal is collected remotely via piezoceramic sensors bonded onto the pipe as well as the flange. The gathered vibration signals are transferred remotely to an in-house developed MATLAB code by a wireless data acquisition (DAQ) module. The data is processed with the embedded signal processing code, which incorporates normalization, filtering of data and the empirical mode decomposition (EMD) to establish an effective energy-based damage index. The assessment of the damage indices obtained for the damage scenarios verifies the integrity of the proposed methodology in identifying the damage and its progression in bolted joints.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| 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".