Fault Diagnosis of Rotary Machines Using Machine Learning

Iago M. Brandao; Cesar da Costa

doi:10.18618/REP.2022.3.0013

Authors

Iago M. Brandao Department of Electrical Engineering, IFSP Federal Institute of São Paulo, São Paulo, Brazil
Cesar da Costa Department of Electrical Engineering, IFSP Federal Institute of São Paulo, São Paulo, Brazil

DOI:

https://doi.org/10.18618/REP.2022.3.0013

Keywords:

Fault classification, Fault diagnosis, Machine learning, Vibration analysis

Abstract

Classification of faults in rotary machines using machine learning is gaining attention in the field of science and engineering. In rotating machinery, misalignment is a common fault. This type of fault has been extensively studied in the literature using the vibration signals produced by rotary machines. This study proposes an approach based on machine learning techniques to diagnose misalignments in rotary machines under various conditions. A personalized diagnostic fault approach is proposed to detect misalignment faults. The approach includes three steps. First, the data acquisition model is developed to obtain signals (fault samples). The rotor vibration signals in stationary rotation conditions were obtained by two inductive proximity sensors with analog output, and the data were collected by a data acquisition device. Then, to generate the faulty training samples, each data acquisition signal is transformed to the frequency domain using Fast Fourier Transform (FFT). Finally, using the samples obtained through the feature selection techniques, machine learning algorithms Random Forest, Naïve Bayes and SVM were evaluated, resulting in classifications with different efficiencies. The results show that the SVM algorithm outperforms the Naïve Bayes and Random Forest algorithms when the same number of features is used. The proposed personalized diagnostic approach was applied to detect faults of rotating electrical machines misalignment with success.

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Author Biographies

Iago M. Brandao, Department of Electrical Engineering, IFSP Federal Institute of São Paulo, São Paulo, Brazil

was born in São Paulo, SP, Brazil. He received the B.Sc. degree in Automation and Control Engineering from the IFSP –Federal Institute of São Paulo, SP, Brazilin 2018. He is currently Machine Learning Engineer. His research interest includes Artificial Neural Network, machine learning, induction motors, sensors, diagnostic, electrical machines, and FPGA.

Cesar da Costa , Department of Electrical Engineering, IFSP Federal Institute of São Paulo, São Paulo, Brazil

was born in Rio de Janeiro, RJ, Brazil. He received the B.Sc. degree in electronic and electrical engineering from the CEFET-RJ, Federal Center of Technological Education Celso Suckow da Fonseca and Nuno Lisboa University in 1975 and 1980 respectively. He received the M.Sc. degree in mechanical engineering from Taubate University, Taubate, SP, Brazil, and the Ph.D. degree in mechanical engineering from UNESP-Universidad Estadual Paulista Julio de Mesquita Filho, Guaratingueta, SP, Brazil in 2005 e 2011, respectively. He did sandwich doctoral stage, PDEE-CAPES, in the IST-Institute Superior Tecnico, Lisbon, Portugal in 2009. He is currently postdoctoral and professor of automation and control engineering in the IFSP –Federal Institute of Education, SP, Brazil. His research interests include Fuzzy controller, Artificial Neural Network, machine learning, electrical machines, FPGA, and Industry 4.0.

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