Abstract
This paper proposes a diagnosis method, combining signal analysis and classification models, to the rotor defect problems of motors. Two manufacture technologies, nonmagnetic high-temperature resistant ceramic adhesive and electrical discharge machining (EDM), are applied to make testing samples, including blowhole and perforation defects of rotor bars in this study. The typical multiresolution analysis (MRA) model is used to analyze acquired source current signals of motors. The features are extracted from the signals of each column of MRA-matrix, including maximum, mean, standard deviation, root-mean-square, and summation. The typical back-propagation neural network (BPNN) model is used to diagnose the rotor bar defects of motors, and then the various signal-to-noise ratio (SNR) of white Gaussian noise (WGN), 30, 25, and 20 dB, are added to the signals to verify the robustness of the proposed method. The results show the availability of the proposed method to diagnose the rotor bar defects of motors.
Highlights
Three-phase squirrel-cage induction motors (SCIM) are used widely for industrial applications.they are the important equipment for the whole production line
This paper proposed a diagnosis method, combining signal analysis and Neural network (NN) classification algorithms under different loading rate conditions, to rotor defective problems of IM motors
fast Fourier transform (FFT), multiresolution analysis (MRA), and Hilbert-Huang transform (HHT) were, respectively, used to analyze the motor current signals where six signals features were extracted from the signals, including maximum, minimum, mean, standard deviation, root-mean-square, and summation values of the raw matrix
Summary
Three-phase squirrel-cage induction motors (SCIM) are used widely for industrial applications. They are the important equipment for the whole production line. Must be built in the process of the motor manufacture. Faults in an induction motor are contributed by bearing, stator, rotor, and others, with percentages of 41%, 37%, 10%, and 12%, respectively [1]. The best characteristics of the infinite impulse response (IIR) filter are exploited to observe the broken rotor bar (BRB) frequencies with good legibility in current and voltage the spectrum of the grid and inverter-fed motor, respectively. Blowholes of rotor bars are always detected in the die casting process of aluminum alloy when parameters of die casting machines are improper, as shown Figure 1
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