Induction Motor Bearing Fault Diagnosis Based on Singular Value Decomposition of the Stator Current

Yuriy Zhukovskiy (), Aleksandra Buldysko and Ilia Revin
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Yuriy Zhukovskiy: Educational Research Center for Digital Technologies, Saint Petersburg Mining University, 191106 St. Petersburg, Russia
Aleksandra Buldysko: Educational Research Center for Digital Technologies, Saint Petersburg Mining University, 191106 St. Petersburg, Russia
Ilia Revin: National Center for Cognitive Research, ITMO University, 197101 St. Petersburg, Russia

Energies, 2023, vol. 16, issue 8, 1-23

Abstract: Among the most widespread systems in industrial plants are automated drive systems, the key and most common element of which is the induction motor. In view of challenging operating conditions of equipment, the task of fault detection based on the analysis of electrical parameters is relevant. The authors propose the identification of patterns characterizing the occurrence and development of the bearing defect by the singular analysis method as applied to the stator current signature. As a result of the decomposition, the time series of the three-phase current are represented by singular triples ordered by decreasing contribution, which are reconstructed into the form of time series for subsequent analysis using a Hankelization of matrices. Experimental studies with bearing damage imitation made it possible to establish the relationship between the changes in the contribution of the reconstructed time series and the presence of different levels of bearing defects. By using the contribution level and tracking the movement of the specific time series, it became possible to observe both the appearance of new components in the current signal and the changes in the contribution of the components corresponding to the defect to the overall structure. The authors verified the clustering results based on a visual assessment of the component matrices’ structure similarity using scattergrams and hierarchical clustering. The reconstruction of the time series from the results of the component grouping allows the use of these components for the subsequent prediction of faults development in electric motors.

Keywords: digital technologies; induction motor; reliability; fault detection; time series analysis; singular spectrum analysis; SSA; singular decomposition; SVD; machine learning (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2023
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (7)

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