Roller Fault Distance Estimation Model Based on Beamforming and CNN-LSTM
Idlers are the key components of belt-conveying operation as well as the weak links in conveying systems.The directionality of beamforming algorithm and the feature recognition ability of deep learning algorithm provide the possibility for estimating the fault distance of idlers.In view of the issues of long conveying distances and the difficulty of detection,a method based on beamforming and Convolutional Neural Network-Long Short term Memory(CNN-LSTM)is proposed to estimate the distance of the fault sound sources of the rollers.Firstly,the Microphone array is used to collect fault sound source data for different distances,and the data set is constructed through beamforming positioning processing.The spatial characteristics of the data sampling set are obtained through convolutional neural network(CNN).Then the sequence information composed of spatial data is input into the LSTM network with the help of the modeling function of LSTM network in the sequence,so as to obtain the spatial timing information.Finally,the spatial temporal information generated by the LSTM network is input into the Softmax classifier to achieve fault distance estimation.The experimental results show that the Beamforming CNN-LSTM(BCL)model can achieve high accuracy in estimating the fault distance of idlers in the environments with or without noise interference,and has better recognition ability than other models.
万方数据
维普
