Classification and regression models of audio and vibration signals for machine state monitoring in precision machining systems

Han, Seulki; Mannan, Nasir; Stein, Daryl C.; Pattipati, Krishna R.; Bollas, George M.

doi:10.1016/j.jmsy.2021.08.004

Classification and regression models of audio and vibration signals for machine state monitoring in precision machining systems

Journal Article · Mon Aug 23 00:00:00 EDT 2021 · Journal of Manufacturing Systems

DOI:https://doi.org/10.1016/j.jmsy.2021.08.004· OSTI ID:1977353

Han, Seulki ^[1]; Mannan, Nasir ^[2]; Stein, Daryl C. ^[3]; ^[4]; Bollas, George M. ^[4]

University of Connecticut, Storrs, CT (United States); OSTI
Connecticut Center for Advanced Technology, Inc, East Hartford, CT (United States)
Gerber Technology, Inc., Tolland, CT (United States)
University of Connecticut, Storrs, CT (United States)

Here we present a data-driven method for monitoring machine status in manufacturing processes. Audio and vibration data from precision machining are used for inference in two operating scenarios: (a) variable machine health states (anomaly detection); and (b) settings of machine operation (state estimation). Audio and vibration signals are first processed through Fast Fourier Transform and Principal Component Analysis to extract transformed and informative features. These features are then used in the training of classification and regression models for machine state monitoring. Specifically, three classifiers (K-nearest neighbors, convolutional neural networks and support vector machines) and two regressors (support vector regression and neural network regression) were explored, in terms of their accuracy in machine state prediction. It is shown that the audio and vibration signals are sufficiently rich in information about the machine that 100% state classification accuracy could be accomplished. Data fusion was also explored, showing overall superior accuracy of data-driven regression models.

View Accepted Manuscript (DOE)

Research Organization:: University of California, Los Angeles, CA (United States); University of Connecticut, Storrs, CT (United States)

Sponsoring Organization:: National Aeronautics and Space Administration (NASA); Naval Research Laboratory (NRL); Office of Naval Research (ONR); USDOE Office of Energy Efficiency and Renewable Energy (EERE)

Grant/Contract Number:: EE0007613

OSTI ID:: 1977353

Alternate ID(s):: OSTI ID: 1862435

Journal Information:: Journal of Manufacturing Systems, Journal Name: Journal of Manufacturing Systems Journal Issue: C Vol. 61; ISSN 0278-6125

Publisher:: Elsevier - Society of Manufacturing EngineersCopyright Statement

Country of Publication:: United States

Language:: English

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