Fault Classification in Reciprocating Compressors: A Comparison of Machine Learning and Deep Learning Approaches

Author:

Sánchez René-Vinicio,Macancela Jean-Carlo,Cabrera Diego,Cerrada Mariela

Publisher

Elsevier BV

Reference16 articles.

1. Failure analysis of refinery hydrogen reciprocating compressors;Białek;Diagnostyka,2018

2. Fault diagnosis in reciprocating compressor bearings: an approach using lamda applied on current signals;Cerrada;IFAC-PapersOnLine,2022

3. de Paula Monteiro, R., Lozada, M.C., Mendieta, D.R.C., Loja, R.V.S., and Filho, C.J.A.B. (2022). A hybrid prototype selection-based deep learning approach for anomaly detection in industrial machines. Expert Systems with Applications, 204, 117528. doi: 10.1016/j.eswa.2022.117528.

4. Goodfellow, I., Bengio, Y., and Courville, A. (2016). Deep Learning. MIT Press. http://www.deeplearningbook.org.

5. Grinsztajn, L., Oyallon, E., and Varoquaux, G. (2022). Why do tree-based models still outperform deep learning on tabular data? doi:10.48550/ARXIV.2207.08815.

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