Abstract
Rotating machinery is one of the key components of marine equipment. Due to the complex and harsh offshore environment, the health status of rotating machinery is more likely to be affected. Therefore, fault diagnosis is of great significance to normal operation and maintenance of rotating machinery in marine equipment. Traditional data-driven fault diagnosis tasks require massive label data for training, and it takes time and manpower to obtain enough label samples. At the same time, it is considered that the noise can interfere with the performance of the fault diagnosis framework. To overcome the above two defects, this paper proposes a fault diagnosis framework based on semi-supervised learning, where the contractive stacked autoencoder (CSA) and the classifier multilayer perceptron (MLP) extract features from unlabeled data and realize fault classification respectively. Compared with the Stacked Autoencoder (SAE)-MLP and Stacked Denoising Autoencoder (SDAE)-MLP frameworks, the proposed learning framework has better fault diagnosis accuracy and robustness.
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
More From: Proceedings of the Institution of Mechanical Engineers, Part M: Journal of Engineering for the Maritime Environment
Disclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.