Machine auscultation: enabling machine diagnostics using convolutional neural networks and large-scale machine audio data

查看全文 作　　者：Ruo-Yu [1]Yang;Rahul [1]Rai 机构地区：[1]Manufacturing and Design Lab (MAD Lab), University at Buffalo, Buffalo, NY, USA 出　　处：《Advances in Manufacturing》索引2019年第7卷第2期,共14页 摘　　要：Acoustic signals play an essential role in machine state monitoring. Efficient processing of real-time machine acoustic signals improves production quality. However, generating semantically useful information from sound signals is an ill-defined problem that exhibits a highly non-linear relationship between sound and subjective perceptions. This paper outlines two neural network models to analyze and classify acoustic signals emanating from machines:(i) a backpropagation neural network (BPNN);and (ii) a convolutional neural network (CNN). Microphones are used to collect acoustic data for training models from a computer numeric control (CNC) lathe. Numerical experiments demonstrate that CNN performs better than the BP-NN. 关 键 词：ACOUSTIC signal processing MACHINE performance Backpropagation NEURAL NETWORK (BP-NN) Convolutional NEURAL NETWORK (CNN)