基于智能巡检机器人的煤机故障音频识别方法
Audio recognition method of coal machine fault based on intelligent inspection robot
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摘要: 随着机器人技术飞速发展以及煤矿安全高效生产更高要求的提出，井下机电设备已经由传统的人工巡检转向具备“监测、检测、预警”功能的机器人化巡检。将声音处理和深度学习引入矿业系统智能处理中，对数据进行智能化分析；研究了声音预处理、语谱图生成和特征提取与分类等各个环节关键技术，解决了现阶段面向煤矿井下环境的关于语音特征无法时域和频域同时描述、缺乏动态序列信息的有效利用等问题。实验数据表明：采用以CNN+LSTM模型为核心，利用caffe c++深度学习框架建立声音识别的CNN+LSTM+Softmax网络，可以有效提高煤矿井下设备异常声音识别准确性和鲁棒性，减小算法复杂度以适应算法在嵌入式设备运行，实现机器人化煤机故障音频辨识及诊断。
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关键词:
- 智能巡检机器人 /
- 煤机故障音频识别 /
- 声音处理 /
- caffe c++深度学习 /
- CNN+LSTM模型
Abstract: With the rapid development of robot technology and the higher requirements of safe and efficient production in coal mine, underground mechanical and electrical equipment has changed from the traditional manual inspection to the robot inspection with the function of “monitoring, detection and early warning”. In this paper, sound processing and deep learning are introduced into the intelligent processing of mining system to analyze the data intelligently. The key technologies of voice preprocessing, spectrogram generation, feature extraction and classification are studied, which solve the problems that the speech features can not be described simultaneously in time domain and frequency domain, and lack of effective use of dynamic sequence information. The experimental data show that the CNN + LSTM + Softmax network based on CNN + LSTM model and caffe C + + deep learning framework can effectively improve the accuracy and robustness of abnormal sound recognition of coal mine equipment, reduce the complexity of the algorithm to adapt to the operation of the algorithm in embedded equipment, and realize the fault audio identification and diagnosis of robotic coal machine. -
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