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• W148660314 abstract "The motor is the workhorse of industries. The issues of preventive and condition-based maintenance, online monitoring, system fault detection, diagnosis, and prognosis are of increasing importance. This paper introduces fault detection for induction motors. Stator currents are measured by current meters and stored by time domain. The time domain is not suitable for representing current signals, so the frequency domain is applied to display signals. The Fourier Transform is employed to convert signals. After signal conversion, signal features must be extracted by signal processing such as wavelet and spectrum analysis. Features are entered in a pattern classification model such as a neural network model, a polynomial neural network, or a fuzzy inference model. This paper describes fault detection results that use Fourier and wavelet analysis. This combined approach is very useful and powerful for detecting signal features." @default.
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• W148660314 date "2004-07-20" @default.
• W148660314 modified "2023-10-06" @default.
• W148660314 title "Fault Detection of Induction Motors Using Fourier and Wavelet Analysis" @default.
• W148660314 doi "https://doi.org/10.20965/jaciii.2004.p0431" @default.
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