Improved acoustic methods for leak detection in buried plastic water distribution pipes
Bibliographic record
Abstract
This thesis is concerned with a theoretical and experimental investigation into the enhancement of acoustic methods used to detect leaks in buried plastic water distribution pipes. Although acoustic methods have been successfully used for leak detection in metallic pipes, they are less effective in plastic pipes due, for example, to the high attenuation of leak noise signals. Two specific problems investigated in this thesis. They are the uncertainty involved in the calculation of the time delay in arrival times between two leak signals, and the variation of speed at which the leak noise propagates in the pipe. This is done in both the time and frequency domains. A new way to estimate the wave speed from measurements made in the presence of a leak is also proposed, together with a way of estimating the attenuation of leak noise as it propagates in the pipe. A bespoke test-rig was designed and built specifically for this project by South Staffs Water plc, so that leaks could be simulated and investigated in controlled conditions.<br/>Following the characterisation of the test rig, two specific correlators were investigated, one using the basic cross-correlation (BCC) function and other using the phase transform (PHAT). It is shown that the BCC is more suitable for leak detection, when the pipe exhibits resonance behaviour. It is further shown that the bandwidth over which the analysis is conducted is crucial to locate the leak accurately. To determine this bandwidth a procedure is developed to determine automatically low and high frequency cut-off frequencies of a band-pass filter. This method uses both the coherence and the modulus of the cross-spectral density (CSD) function between two leak noise signals.<br/>A new technique is also proposed to calculate the time delay estimate using both the modulus and phase of the CSD function, and this is validated using a wide range of<br/>data from the bespoke test-rig and also from a Canadian test-rig. Moreover, an expression for the variance of the time delay estimate is determined based on frequency domain data, and this is shown to give the same result as that previously determined in the time domain. Using the variance and statistical analysis a range of values that the time delay estimate can assume is calculated. This range of values is related to the 95% confidence interval of the time delay estimate calculated using the weighted least squares fit. The confidence interval can also be applied to the wave-speed estimate. The time delay and wavespeed estimates can be combined to determine a region where a leak is likely to be located in a pipe rather than giving an exact position as provided by commercial correlators. Measured data is used to validate the approach using the bespoke test-rig.
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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 | 0.001 | 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) | 0.002 | 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 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".