Non-Intrusive Water Pipeline Flowmeter Based on Acoustic Signal Using AI Approach

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.