Pipeline Leak Audio Detection System based on Machine Learning

It is of great practical significance to study intelligent robots for crack detection of urban underground pipes instead of manual detection, which not only can save time and cost, but also can improve the accuracy rate of identification. The system consists of two parts: mobile platform and system detection platform. The mobile platform is obstacle-avoiding by ultrasonic module, segmenting the pipeline color by camera and issuing platform steering command. The system detection platform adopts Raspberry Pi as the core control system, and collects the pipeline leakage audio through the pickups, and the collected audio is passed through Gaussian mixture model-Hidden Markov model (GMM -HMM) and Deep neural networks- Hidden Markov model (DNN -HMM) respectively in Raspberry Pi model (DNN -HMM) for the purpose of detection. The accuracy and running speed of the two methods were tested separately through simulation experiments. GMM -HMM achieves 74.1% accuracy and 14 (frame/s) running speed, while DNN -HMM achieves 80.6% accuracy and 9 (frame/s) running speed, DNN -HMM has higher accuracy and running speed to meet the real-time requirements for pipeline leakage audio detection.