A deep supervised learning approach for condition-based maintenance of naval propulsion systems. (1st February 2021)

• Record Type: Journal Article
• Authors: Berghout, Tarek; Mouss, Leïla-Hayet; Bentrcia, Toufik; Elbouchikhi, Elhoussin; Benbouzid, Mohamed
• Is Part Of: Ocean engineering. Volume 221(2021)
• Publication Date: 2021-02-01
• Subjects: Predictive maintenance -- Decay detection -- Extreme learning machine -- Deep learning -- Prognostic and health management -- Naval propulsion systems; Ocean engineering -- Periodicals; Ocean engineering; Periodicals; 620.4162
• DOI: 10.1016/j.oceaneng.2020.108525
• Languages: English
• ISSNs: 0029-8018

Abstract

Abstract: In the last years, predictive maintenance has gained a central position in condition-based maintenance tasks planning. Machine learning approaches have been very successful in simplifying the construction of prognostic models for health assessment based on available historical labeled data issued from similar systems or specific physical models. However, if the collected samples suffer from lack of labels (small labeled dataset or not enough samples), the process of generalization of the learning model on the dataset as well as on the newly arrived samples (application) can be very difficult. In an attempt to overcome such drawbacks, a new deep supervised learning approach is introduced in this paper. The proposed approach aims at extracting and learning important patterns even from a small amount of data in order to produce more general health estimator. The algorithm is trained online based on local receptive field theories of extreme learning machines using data issued from a propulsion system simulator. Compared to extreme learning machine variants, the new algorithm shows a higher level of accuracy in terms of approximation and generalization under several training paradigms. Highlights: ELM-based DBN training for both unsupervised learning and supervised fine tuning stages. Integration of locally connected combinatorial neural sub-networks into hidden nodes based on ELM-LRF. Introduction of a regularized online OS-ELM-based learning to address adaptive training to prevent from structural risks. Convolutional mapping and deep features reconstruction with autoencoders stack in multilayer neural network single framework.