Non-Intrusive Water Pipeline Flowmeter Based on Acoustic Signal Using AI Approach
Bibliographic record
Abstract
Deep learning approaches spread widely in many applications because of their accurate results.Water flow rate measurement is one of these applications.This work proposes a new approach for measuring water flow rate in pipes using acoustic signals generated.The approach consists of multiple stages: real-time acoustic data collection, feature extraction, and classifier implementation.A medical stethoscope and microphone are attached to the pipe under test with a proper soundproof mounted enclosure to collect acoustic data.The obtained data represented ten levels of water flow rate in the pipe.Then, all these data are processed and used to generate features using the Mel-Filterbank energies spectrogram, which enhances flow-related acoustic information.The generated spectrogram features of the 10 classes are fed to the deep learning classifier, which is based on the TinyML framework.Classifier high-performance metrics show the implemented approach's success in accurately measuring flow.The 98.36% accuracy of the classifier illustrates the success of the proposed approach with an F1 Score of 0.99, which outperforms the competitive research results.Moreover, the trained and tested classifier with the feature generation stage is deployed efficiently on the limited resources microcontroller (Arduino Nano 33BLE sense), consuming only a tiny share of the microcontroller's resources.Additionally, the resulting latency of the classifier inference time is less than 0.2 seconds, making the classifier suitable for real-time applications.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".