基于音频特征融合Res-CNN-LSTM网络的带式输送机运行状态预测
Running State Prediction of Belt Conveyor Based on Audio Feature Fusion Res-CNN-LSTM Network
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摘要: 摘 要:为了解决使用接触式传感器、机器视觉对矿用带式输送机的运行状态监测存在安装不便、稳定性差,且缺少对带式输送机运行状态预测的问题,提出基于音频特征融合Res-CNN-LSTM网络的带式输送机运行状态预测的方法。首先对音频信号进行滤波去噪,然后采用梅尔倒频谱法(MFCC)提取信号的一维梅尔倒频谱系数MFCC0,作为网络模型的输入;考虑到网络模型加深会导致过拟合和性能退化,引入残差块对网络模型进行优化。以实验室带式输送机平台进行试验,实验过程中不断优化网络模型参数,结果表明,音频信号可以获取带式输送机更多的运行状态信息;与其他模型相比,所设计的网络模型预测准确率最高,且训练时间短;同时在不同的工况下验证了该模型具有较高的鲁棒性。
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关键词:
- 带式输送机 /
- 特征提取 /
- MFCC /
- Res-CNN-LSTM /
- 运行状态预测
Abstract: Abstract: In order to solve the problems of inconvenient installation, poor stability, and lack of prediction of the operating status of the belt conveyor using contact sensors and machine vision to monitor the operating status of the mining belt conveyor, Res-CNN-LSTM based on audio feature fusion is proposed. Network-based belt conveyor operation state prediction method. Firstly, the audio signal is filtered and denoised, and then the one-dimensional Mel cepstrum coefficient MFCC0 of the signal is extracted by the Mel cepstrum method (MFCC) as the input of the network model; considering that the deepening of the network model will lead to over-fitting and performance degradation,The residual block is introduced to optimize the network model. Experiments are carried out on the laboratory belt conveyor platform. During the experiment, the network model parameters are continuously optimized. The results show that the audio signal can obtain more information about the operation status of the belt conveyor; compared with other models, the designed network model predicts the accuracy is the highest, and the training time is short; at the same time, it is verified that the model has high robustness under different working conditions.-
Key words:
- belt conveyor /
- feature Extraction /
- MFCC /
- Res-CNN-LSTM /
- operational state prediction
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