Very Accurate Time-Frequency Representation of Induction Motors Harmonics for Fault Diagnosis Under Arbitrary Load Variations

Bonet-Jara, J.; Fernandez-Cavero, V.; Vedreno Santos, Francisco.; Pons-Llinares, J.

Abstract

Induction motors work under steady state in many applications. Nevertheless, in some cases (e.g., wood cutting machine or pulp mixer in paper industries), the load continuously varies. In these cases, time-frequency transforms are needed to detect the evolutions of faulty harmonics, quantify their amplitudes and determine the severity of the fault. To achieve reliable results under challenging situations, a very precise time-frequency transform, which enables to very accurately plot the faulty harmonics evolutions, must be developed. The Dragon Transform is here proposed to address the problem. It can be seen, through simulation and experimental results, how the transform enables to plot up to five faulty harmonics under rotor asymmetry. Precision is so high that even the oscillations caused by ripple effect can be observed for the first time in the technical literature, enhancing the reliability of the diagnosis performed, and opening the path for a true solution of the problem.

Citation

Bonet-Jara, J., Fernandez-Cavero, V., Vedreno Santos, F., & Pons-Llinares, J. (2022, September). Very Accurate Time-Frequency Representation of Induction Motors Harmonics for Fault Diagnosis Under Arbitrary Load Variations. Presented at 2022 International Conference on Electrical Machines (ICEM), Valencia, Spain

• Presentation Conference Type: Conference Paper (published)
• Conference Name: 2022 International Conference on Electrical Machines (ICEM)
• Start Date: Sep 5, 2022
• End Date: Sep 8, 2022
• Acceptance Date: May 31, 2022
• Online Publication Date: Oct 13, 2022
• Publication Date: 2022
• Deposit Date: Nov 7, 2022
• Publicly Available Date: Nov 7, 2022
• Publisher: Institute of Electrical and Electronics Engineers
• Series ISSN: 2381-4802
• Book Title: 2022 International Conference on Electrical Machines (ICEM)
• DOI: https://doi.org/10.1109/ICEM51905.2022.9910768
• Keywords: Induction Motors; Load Oscillations; Signal analysis; Time-frequency transforms; Fault diagnosis
• Public URL: http://researchrepository.napier.ac.uk/Output/2885270