Scalable and Unsupervised Feature Engineering Using Vibration-Imaging and Deep Learning for Rotor System Diagnosis

This paper proposes a scalable and unsupervised feature engineering method that uses vibration imaging and deep learning. For scalability, a vibration imaging approach is devised that incorporates data from systems with various scales, such as small testbeds and real field-deployed systems. Moreover, a deep learning approach is proposed for unsupervised feature engineering. The overall procedure includes three key steps: 1) vibration image generation; 2) unsupervised feature extraction; and 3) fault classifier design. To demonstrate the validity of the proposed approach, three case studies are conducted using an RK4 rotor kit and a power plant journal bearing system. By incorporating smaller-system data as well as real-system data, the proposed approach can substantially increase the applicability of the fault diagnosis method while maintaining good accuracy. Moreover, the time and effort needed to develop a diagnostic approach for other rotor systems can be reduced considerably.

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