The underground mining environment of coal mine is complex. Once the water supply or gas supply pipes leak, the location and time of leakage are unpredictable. If the leakage can not be found in time, it will seriously threaten the life safety of underground workers. Therefore, it is very necessary to automatically detect the pipeline leakage. In order to solve the problem of automatic detection of coal mine pipeline leakage, this paper proposes an image processing method based on gray level co-occurrence matrix to extract the features of time-frequency image of vibration signal. Firstly, the data in a relatively short time is divided into one frame to reduce the amount of calculation. Secondly, each frame of signal is processed by SPWVD to get the time-frequency image. Then, the time-frequency image after time-frequency analysis is transformed into gray-scale image, and the gray-scale co-occurrence matrix (GLCM) of the image is extracted. Finally, the energy, contrast and correlation of the co-occurrence matrix are extracted and input to SVM for learning and training. Through the design and build of the experimental pipeline to test the algorithm proposed in this paper, the detection result of SVM is 96.67%, which shows that this method can effectively detect the pipeline leakage.
|Number of pages||8|
|Journal||IOP Conference Series: Earth and Environmental Science|
|Early online date||20 Mar 2020|
|Publication status||Published - 20 Mar 2020|
|Event||5th International Conference on Advances in Energy Resources and Environment Engineering - Chongqing, China|
Duration: 6 Dec 2019 → 8 Dec 2019
Conference number: 5