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A Data-Driven Approach for the Diagnosis of Mechanical Systems Using Trained Subtracted Signal Spectrograms

1
School of Mechanical Engineering, Chung-Ang University, Seoul 06974, Korea
2
The George W. Woodruff School of Mechanical Engineering, Georgia Institute of Technology, Atlanta, GA 30332, USA
*
Authors to whom correspondence should be addressed.
Sensors 2019, 19(5), 1055; https://doi.org/10.3390/s19051055
Received: 30 January 2019 / Revised: 20 February 2019 / Accepted: 25 February 2019 / Published: 1 March 2019
(This article belongs to the Special Issue Sensors for Fault Diagnosis and Fault Tolerance)
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Abstract

Toward the prognostic and health management of mechanical systems, we propose and validate a novel effective, data-driven fault diagnosis method. In this method, we develop a trained subtracted spectrogram, the so called critical information map (CIM), identifying the difference between the signal spectrograms of normal and abnormal status. We believe this diagnosis process may be implemented in an autonomous manner so that an engineer employs it without expert knowledge in signal processing or mechanical analyses. Firstly, the CIM method applies sequential and autonomous procedures of time-synchronization, time frequency conversion, and spectral subtraction on raw signal. Secondly, the subtracted spectrogram is then trained to be a CIM for a specific mechanical system failure by finding out the optimal parameters and abstracted information of the spectrogram. Finally, the status of a system health can be monitored accurately by comparing the CIM with an acquired signal map in an automated and timely manner. The effectiveness of the proposed method is successfully validated by employing a diagnosis problem of six-degree-of-freedom industrial robot, which is the diagnosis of a non-stationary system with a small amount of training datasets. View Full-Text
Keywords: prognostics and health management (PHM); industrial robot; critical information map (CIM); data-driven; non-stationary signal; smart factory; wavelet package decomposition (WPD) prognostics and health management (PHM); industrial robot; critical information map (CIM); data-driven; non-stationary signal; smart factory; wavelet package decomposition (WPD)
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).
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Huh, J.; Pham Van, H.; Han, S.; Choi, H.-J.; Choi, S.-K. A Data-Driven Approach for the Diagnosis of Mechanical Systems Using Trained Subtracted Signal Spectrograms. Sensors 2019, 19, 1055.

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