A new approach for fault detection of broken rotor bars in induction motor based on support vector machine

Armaki, Mahdi Gordi; Roshanfekr, Reza

doi:10.1109/iraniancee.2010.5506976

Cited by 8 publications

(1 citation statement)

References 41 publications

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“…In [234], Case Western Reserve University data present the results of a comparative examination of different methods for identifying bearing faults. The frequency spectrum of torque, speed, and stator currents, as well as the Parks transformation and continuous wavelet transform (CWT), can be used to train a support vector machine (SVM) for fault identification and diagnostics in IM [235][236][237][238]. The Least Squares Support Vector Machine (LSSVM) has produced reliable predictions when working with a limited data set.…”

Section: Support Vector Machinesmentioning

confidence: 99%

State-of-the-Art Techniques for Fault Diagnosis in Electrical Machines: Advancements and Future Directions

Akbar,

Vaimann,

Asad

et al. 2023

Energies

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Electrical machines are prone to various faults and require constant monitoring to ensure safe and dependable functioning. A potential fault in electrical machinery results in unscheduled downtime, necessitating the prompt assessment of any abnormal circumstances in rotating electrical machines. This paper provides an in-depth analysis as well as the most recent trends in the application of condition monitoring and fault detection techniques in the disciplines of electrical machinery. It first investigates the evolution of traditional monitoring techniques, followed by signal-based techniques such as spectrum, vibration, and temperature analysis, and the most recent trends in its signal processing techniques for assessing faults. Then, it investigates and details the implementation and evolution of modern approaches that employ intelligence-based techniques such as neural networks and support vector machines. All these applicable and state-of-art techniques in condition monitoring and fault diagnosis aid in predictive maintenance and identification and have the highly reliable operation of a motor drive system. Furthermore, this paper focuses on the possible transformational impact of electrical machine condition monitoring by thoroughly analyzing each of the monitoring techniques, their corresponding pros and cons, their approaches, and their applicability. It offers strong and useful insights into proactive maintenance measures, improved operating efficiency, and specific recommendations for future applications in the field of diagnostics.

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