Publication

A Novel Deep Learning Approach for Water Leakage Detection Using Acoustic Features

Our Proof of Concept (POC) is designed to demonstrate the capabilities and effectiveness of our deep learning-based water leakage detection system. Through this POC, we aim to showcase how our technology can seamlessly integrate with existing plumbing systems and provide accurate, real-time leak detection. The POC comprises the following key components:

• Data Collection : We collect a diverse range of audio samples from different plumbing systems, simulating both leak and non-leak scenarios. These samples serve as the foundation for training our deep learning model.


• Feature Extraction : Extracting meaningful features from the audio data is crucial for the model's accuracy. We employ advanced techniques to transform raw audio signals into spectrograms and Mel-frequency cepstral coefficients (MFCCs) that capture the unique sound patterns associated with leaks.


• Model Training : Our data scientists train a deep learning model, leveraging Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs), on the extracted features. The model learns to distinguish between normal water flow and leak-induced acoustic patterns.


• Testing and Validation : Once trained, the model is rigorously tested using a separate set of audio samples to ensure its accuracy and generalization capabilities. Validation tests assess the model's ability to differentiate between various leak sizes and types.


• Real-time Detection : The trained model is integrated into our water leakage detection system, which continuously monitors the sounds of water flow in real time. When a leak is detected, the system triggers alerts to notify relevant parties, enabling timely intervention.