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In this study, the aim is to detect an induction motor's winding faults by using independent component analysis (ICA). Many laboratory experiments have been done on an induction motor to check the performance of the proposed method. The phase currents of the induction motor are used in the fault detection algorithm. The algorithm is implemente d by using MATLAB. The proposed method is compared to...
Traditionally discrete Fourier transform, wavelet techniques etc. are used for numerical transformer protection. In this study two different techniques are suggested for this purpose. The techniques so called dynamical principal component analysis (PCA) and independent component analysis (ICA) are investigated and compared to detect transformer internal faults. A three phase custom-built transformer...
A new fault detection and identification (FDI) scheme for transformer faults are suggested in this paper. The new method is based on fuzzy discrete event, from now FDES, composed from between a transformer's measured outputs and its faults. In the FDES, events and state membership functions take values between zero and one. All events occur at the same time with different membership degrees. The main...
Early detection and diagnosis of incipient faults is desirable for on-line condition assessment, product quality assurance and improved efficiency of induction motors running off power supply mains. In the applications of three-phase induction motors in industry, the inner faults may occur in their rotor and stator windings. These kinds of faults will make serious health problems on the motor. This...
In this paper; a method is proposed to detect parameter faults in nonlinear systems. The proposed method is called a dynamic principal component analysis approach. In this approach, the detection is based on the manipulation of input and output data without assuming any model for the system. The approach is based on the principal component analysis of the system input-output correlation data on a...
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