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20 April 2022 Time-series imaging method for rotating machinery fault diagnosis using unsupervised sparse dictionary learning
Wonho Jung, Dae-Guen Lim, Jaewoong Bae, Yong-Hwa Park
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Proceedings Volume 12043, Active and Passive Smart Structures and Integrated Systems XVI; 120431B (2022) https://doi.org/10.1117/12.2612532
Event: SPIE Smart Structures + Nondestructive Evaluation, 2022, Long Beach, California, United States
Abstract
Time-series signal collected from rotating machinery is subjected to different environmental and operational conditions. The vibration signal is sensitively affected by external noises and load conditions. To solve these problems, this paper presents a diagnostic method for rotating machinery using the proposed robust time-series imaging method. The overall procedure includes the following three key steps: (1) transformation of a one-dimensional current signal to a twodimensional image in time-domain, (2) extracting features using convolutional neural networks, and (3) calculating a health indicator using Mahalanobis distance. Transformation of the time-series signal is based on recurrence plots (RP). The original RP method provides a binary image that makes it insensitive to detecting faulty signal. The proposed RP method develops from sparse dictionary learning that provides the dominant fault feature representations in a robust way. The proposed RP method can detect the weak difference between normal and fault signal, while enhancing robustness to external noise. The dataset acquired from KAIST rotor testbed is used to examine the proposed method’s capability to monitor the condition of rotating machinery. The results show that the proposed method outperforms vibration signalbased condition monitoring methods.
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Wonho Jung, Dae-Guen Lim, Jaewoong Bae, and Yong-Hwa Park "Time-series imaging method for rotating machinery fault diagnosis using unsupervised sparse dictionary learning", Proc. SPIE 12043, Active and Passive Smart Structures and Integrated Systems XVI, 120431B (20 April 2022); https://doi.org/10.1117/12.2612532
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KEYWORDS
Feature extraction
Mahalanobis distance
Convolutional neural networks
Diagnostics
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