Multi-Fault Diagnosis in Three-Phase Induction Motors Using Data Optimization and Machine Learning Techniques

Electronics

Alessandro Goedtel¹  Gustavo Henrique Bazan²  Daniel Morinigo-Sotelo³  Oscar Duque-Perez³ 

[1] Department of Electrical Engineering, Federal University of Technology—Paraná, Av. Alberto Carazzai, 1640, Cornélio Procópio 86300-000, PR, Brazil;Department of Electromechanics, Federal Institute of Paraná, Av. Doutor Tito, s/n, Jacarezinho 86400-000, PR, Brazil;GIR ADIRE—ITAP—Department of Electrical Engineering, University of Valladolid, Paseo del Cauce, 59, 47011 Valladolid, Spain;

关键词: multi-fault diagnosis;    shifted mutual information;    principal component analysis;    pattern recognition;   

DOI  :  10.3390/electronics10121462

来源: DOAJ

【 摘 要 】

Induction motors are very robust, with low operating and maintenance costs, and are therefore widely used in industry. They are, however, not fault-free, with bearings and rotor bars accounting for about 50% of the total failures. This work presents a two-stage approach for three-phase induction motors diagnosis based on mutual information measures of the current signals, principal component analysis, and intelligent systems. In a first stage, the fault is identified, and, in a second stage, the severity of the defect is diagnosed. A case study is presented where different severities of bearing wear and bar breakage are analyzed. To test the robustness of the proposed method, voltage imbalances and load torque variations are considered. The results reveal the promising performance of the proposal with overall accuracies above 90% in all cases, and in many scenarios 100% of the cases are correctly classified. This work also evaluates different strategies for extracting the signals, showing the possibility of reducing the amount of information needed. Results show a satisfactory relation between efficiency and computational cost, with decreases in accuracy of less than 4% but reducing the amount of data by more than 90%, facilitating the efficient use of this method in embedded systems.

【 授权许可】

Unknown