| Summary: | The popularity of asynchronous machines, particularly squirrel cage machines, stems from their inexpensive production costs, resilience, and low maintenance requirements. Unfortunately, potential flaws in these devices might have a negative impact on the facility's profitability and service quality. As a result, diagnostic tools for detecting flaws in these types of devices must be developed. Asynchronous machine problems can be diagnosed using a variety of methods. Signal processing techniques based on extracting information from characteristic quantities of electrical machine operation can provide highly useful information about flaws.
The purpose of this research is to develop efficient algorithms based on numerous signal processing approaches for correctly detecting asynchronous cage machine rotor defects (rotor bar ruptures)
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