Highly reliable state monitoring system for induction motors using dominant features in a two-dimension vibration signal
In this paper, we propose a highly reliable state monitoring system for induction motors. The proposed system utilizes vibration signals to analyze characteristics of the induction motor and extract features for classifying abnormal states from normal ones. To extract the features of faulty and healthy signals, we first convert one-dimension vibration signals into two-dimension gray images to utilize the relationship between each element and its neighboring elements, and we calculate the number of significant pixels in these converted images. We then use multiclass support vector machines to distinguish between abnormal data and normal data. The experimental results indicate that the proposed state monitoring system achieves 100% classification accuracy. In addition, we explore the effects of the noise components inherent in the vibration signals by adding white Gaussian noise to the vibration signals to obtain signal-to-noise ratios (SNRs) of 10 dB, 15 dB, 20 dB, 30 dB, and 40 dB, respectively. The experimental results show that the proposed approach continues to achieve 100% classification accuracy in noisy environments with SNRs of at least 15 dB. Furthermore, the experimental results show that the proposed approach outperforms a conventional state-of-the-art algorithm in both noisy and noiseless environments.
state monitoring system induction motor multiclass support vector machines feature extraction fault classification