Machine auscultation: enabling machine diagnostics using convolutional neural networks and large-scale machine audio data
Source 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 (BP-NN); 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.
Source: openalex_inverted_index · Quality: baseline
Research context
OpenAlex topics: Music and Audio Processing · Machine Fault Diagnosis Techniques · Phonocardiography and Auscultation Techniques
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
References (46)
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