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

doi:10.1007/s40436-019-00254-5

Abstract

Abstract: 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.

The full text can be downloaded at https://link.springer.com/article/10.1007/s40436-019-00254-5

Key words: Acoustic signal processing, Machine performance, Backpropagation neural network (BP-NN), Convolutional neural network (CNN)

Ruo-Yu Yang, Rahul Rai. Machine auscultation: enabling machine diagnostics using convolutional neural networks and large-scale machine audio data[J]. Advances in Manufacturing, 2019, 7(2): 174-187.

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