矿井带式输送机运行状态预测方法

李敬兆; 孙杰臣; 叶桐舟

doi:10.13272/j.issn.1671-251x.2021080074

矿井带式输送机运行状态预测方法

doi: 10.13272/j.issn.1671-251x.2021080074

1.
1. 安徽理工大学电气与信息工程学院, 安徽淮南 232001;
2.
2. 安徽理工大学机械工程学院, 安徽淮南 232001

基金项目:

国家自然科学基金项目（51874010）；北京理工大学高精尖机器人开放性研究项目（2018IRS16）；物联网关键技术研究创新团队项目（201950ZX003）。

详细信息

作者简介:
李敬兆(1964-),男,安徽淮南人,教授,博士,博士研究生导师,主要研究方向为嵌入式系统、人工智能技术,E-mail:jzhli@aust.edu.cn。

通讯作者:
孙杰臣(1995-),男,安徽阜阳人,硕士研究生,主要研究方向为信号处理、设备状态预测,E-mail:sjc95721@163.com。

中图分类号: TD634
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- 文章访问数: 218
- HTML全文浏览量: 10
- PDF下载量: 28
- 被引次数: 0
出版历程
- 收稿日期: 2021-08-27
- 修回日期: 2022-02-18
- 网络出版日期: 2022-03-01

Prediction method of operation state of mine belt conveyor

1.
School of Electrical and Information Engineering, Anhui University of Science and Technology, Huainan 232001, China;
2.
School of Mechanical Engineering, Anhui University of Science and Technology, Huainan 232001, China

摘要

摘要: 传感器监测数据结合神经网络预测模型是矿井带式输送机运行状态预测的主流方法，但利用接触式传感器对带式输送机运行状态进行监测存在安装不便、数据误差大等问题，导致带式输送机运行状态预测精度不高。针对该问题，提出了一种基于音频信号的矿井带式输送机运行状态预测方法。首先，采用高通滤波器和Boll谱减法对带式输送机运行时的原始音频信号进行滤波降噪处理。然后，通过预加重、分帧加窗、傅里叶变换、梅尔滤波器能量计算、离散余弦变换等提取音频信号的梅尔频率倒谱系数（MFCC）第1维分量（MFCC₀），并输入至残差块优化的卷积神经网络结合长短期记忆网络（Res-CNN-LSTM）预测模型，以减少预测模型的输入数据量。最后，通过添加残差块的CNN自适应提取带式输送机音频信号的MFCC₀空间特征并对数据进行降维，基于LSTM提取降维数据的时间特征，从而提高带式输送机运行状态预测精度。实验结果表明，MFCC₀可有效表征带式输送机不同运行状态时的音频信号特征；与CNN，LSTM，CNN-LSTM模型相比，Res-CNN-LSTM模型对带式输送机运行状态的预测更准确。
- 带式输送机 /
- 运行状态预测 /
- 音频信号 /
- MFCC /
- 残差块 /
- CNN /
- LSTM
Abstract: The sensor monitoring data combined with neural network prediction model is the mainstream method of mine belt conveyor operation state prediction. However, using contact sensor to monitor the belt conveyor running state has some problems, such as inconvenient installation and large data error, resulting in low prediction precision of belt conveyor operation state. In order to solve this problem, a prediction method of mine belt conveyor operation state based on audio signal is proposed. Firstly, the high-pass filter and Boll spectral subtraction are used to filter and reduce the noise of the original audio signal during belt conveyor operation. Secondly, the first dimension component (MFCC₀) of Mel-frequency cepstral coefficients (MFCC) of audio signal is extracted by pre-emphasis, framing and windowing, Fourier transform, Mel filter energy calculation, discrete cosine transform, and input to the residual block optimized convolutional neural network combined with long and short term memory network (Res-CNN-LSTM) prediction model to reduce the amount of input data of the prediction model. Finally, the MFCC₀ spatial characteristics of the belt conveyor audio signal are extracted adaptively by CNN with residual blocks, and the dimension of the data is reduced. Moreover, the temporal characteristics of the dimension-reduced data are extracted based on LSTM, so as to improve the prediction precision of the belt conveyor operation state. The experimental results show that MFCC₀ can effectively characterize the audio signal characteristics of belt conveyor in different operation states. Compared with CNN, LSTM, and CNN-LSTM models, the Res-CNN-LSTM model is more accurate in predicting the operation state of the belt conveyor.
- belt conveyor /
- operation state prediction /
- audio signal /
- MFCC /
- residual block /
- CNN /
- LSTM

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