1. A deep supervised learning approach for condition-based maintenance of naval propulsion systems
- Author
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Toufik Bentrcia, Leila Hayet Mouss, Mohamed Benbouzid, Tarek Berghout, Elhoussin Elbouchikhi, University of Batna Hadj Lakhder [Algeria], Laboratoire d'Automatique et de Productique de Batna (LAP), Université Hadj Lakhdar Batna 1, Energie et Systèmes Electromécaniques (LABISEN-ESE), Laboratoire ISEN (L@BISEN), Institut supérieur de l'électronique et du numérique (ISEN)-YNCREA OUEST (YO)-Institut supérieur de l'électronique et du numérique (ISEN)-YNCREA OUEST (YO), Institut de Recherche Dupuy de Lôme (IRDL), and Université de Bretagne Sud (UBS)-Université de Brest (UBO)-École Nationale Supérieure de Techniques Avancées Bretagne (ENSTA Bretagne)-Centre National de la Recherche Scientifique (CNRS)
- Subjects
Environmental Engineering ,business.industry ,Generalization ,Process (engineering) ,Computer science ,Condition-based maintenance ,Supervised learning ,Estimator ,020101 civil engineering ,Ocean Engineering ,02 engineering and technology ,Propulsion ,Machine learning ,computer.software_genre ,01 natural sciences ,Predictive maintenance ,010305 fluids & plasmas ,0201 civil engineering ,[SPI]Engineering Sciences [physics] ,0103 physical sciences ,Artificial intelligence ,business ,computer ,ComputingMilieux_MISCELLANEOUS ,Extreme learning machine - 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.
- Published
- 2021