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http://hdl.handle.net/20.500.12390/2336


Título: Fault diagnosis via neural ordinary differential equations
Autor(es): Enciso-Salas L. 
Pérez-Zuñiga G. 
Sotomayor-Moriano J. 
Resumen: Implementation of model-based fault diagnosis systems can be a difficult task due to the complex dynamics of most systems, an appealing alternative to avoiding modeling is to use machine learning-based techniques for which the implementation is more affordable nowadays. However, the latter approach often requires extensive data processing. In this paper, a hybrid approach using recent developments in neural ordinary differential equations is proposed. This approach enables us to combine a natural deep learning technique with an estimated model of the system, making the training simpler and more efficient. For evaluation of this methodology, a nonlinear benchmark system is used by simulation of faults in actuators, sensors, and process. Simulation results show that the proposed methodology requires less processing for the training in comparison with conventional machine learning approaches since the data-set is directly taken from the measurements and inputs. Furthermore, since the model used in the essay is only a structural approximation of the plant; no advanced modeling is required. This approach can also alleviate some pitfalls of training data-series, such as complicated data augmentation methodologies and the necessity for big amounts of data. © 2021 by the authors. Licensee MDPI, Basel, Switzerland.
Tema: Deep learning;Fault diagnosis;Neural ordinary differential equations
Editorial: MDPI AG
Fecha de publicación: 2021
Publicado en: Applied Sciences (Switzerland) 
Financiamiento: 10-2018-FONDECYT-BM 
Tipo de publicación: info:eu-repo/semantics/article
Identificador Handle: http://hdl.handle.net/20.500.12390/2336
DOI: 10.3390/app11093776
Nivel de acceso: info:eu-repo/semantics/openAccess
Colección:2.1 Estudios de doctorado y postdoctorado

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