Automatic feature extraction of time-series applied to fault severity assessment of helical gearbox in stationary and non-stationary speed operation
| Autor: | Diego Cabrera, Chuan Li, José Valente de Oliveira, Fannia Pacheco, Mariela Cerrada, René-Vinicio Sánchez, Fernando Sancho |
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| Přispěvatelé: | Universidad de Sevilla. Departamento de Ciencias de la Computación e Inteligencia Artificial, Ministerio de Economía y Competitividad (MINECO). España, Universidad Politécnica Salesiana (Ecuador) |
| Jazyk: | angličtina |
| Rok vydání: | 2017 |
| Předmět: |
0209 industrial biotechnology
Computer science Feature extraction Transform Feature selection 02 engineering and technology Auto-encoder Machine learning computer.software_genre Wavelet packets Convolutional neural network Wavelet packet decomposition Vibration signal 020901 industrial engineering & automation Machinery Robustness (computer science) Diagnosis 0202 electrical engineering electronic engineering information engineering Helical gearbox Signal processing Genetic Algorithm business.industry Deep learning Pattern recognition Autoencoder Convolution 020201 artificial intelligence & image processing Artificial intelligence business computer Failure mode and effects analysis Software |
| Zdroj: | Repositório Científico de Acesso Aberto de Portugal Repositório Científico de Acesso Aberto de Portugal (RCAAP) instacron:RCAAP idUS. Depósito de Investigación de la Universidad de Sevilla instname idUS: Depósito de Investigación de la Universidad de Sevilla Universidad de Sevilla (US) |
| Popis: | Signals captured in rotating machines to obtain the status of their components can be considered as a source of massive information. In current methods based on artificial intelligence to fault severity assessment, features are first generated by advanced signal processing techniques. Then feature selection takes place, often requiring human expertise. This approach, besides time-consuming, is highly dependent on the machinery configuration as in general the results obtained for a mechanical system cannot be reused by other systems. Moreover, the information about time events is often lost along the process, preventing the discovery of faulty state patterns in machines operating under time-varying conditions. In this paper a novel method for automatic feature extraction and estimation of fault severity is proposed to overcome the drawbacks of classical techniques. The proposed method employs a Deep Convolutional Neural Network pre-trained by a Stacked Convolutional Autoencoder. The robustness and accuracy of this new method are validated using a dataset with different severity conditions on failure mode in a helical gearbox, working in both constant and variable speed of operation. The results show that the proposed unsupervised feature extraction method is effective for the estimation of fault severity in helical gearbox, and it has a consistently better performance in comparison with other reported feature extraction methods. (C) 2017 Elsevier B.V. All rights reserved. R&D projects Ministeriode Economia yCompet-itividad of Cobierno de Espana [TH12012-37434, T1N2013-41086-P] European FEDER funds GIDTEC [002-002-2016-03-03] Universidad Politecnica Salesians sede Cuenca Secretariat for Higher Education, Science.Technology and Innovation (SENESCVT) of the Republic of Ecuador info:eu-repo/semantics/publishedVersion |
| Databáze: | OpenAIRE |
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