Hybrid Entropy in the Time-Frequency Domain for Grading Electrode Sediment Identification

Abstract

The traditional manual periodic screening method of grading electrode sediments is prone to cause the equipment failure of high voltage direct current converter valves. Therefore, we propose to use ultrasonic time-domain reflection method to detect the sediments. However, the ultrasonic echo signals are characterized by nonlinearity and nonsmoothness, which makes it very difficult to extract effective features for sediment detection. To address this issue, we propose an intelligent detection method based on multiscale hybrid entropy characteristics in the time-frequency domain. First, a multiscale decomposition of the signal is performed. Second, the weighted form factor index is proposed to select the noise modes. Moreover, we propose to calculate the hybrid entropy in the time-frequency domain of each mode as the characteristic input bidirectional long and short-term memory network model, and verified that feature enhancement can be achieved by noise modes noise reduction. Finally, the experimental validation shows that the proposed method can achieve nondestructive testing and intelligent identification of graded electrode sediment with a correct identification rate of 94.25%.

Keywords

• Electrodes
• electrical fault detection
• entropy
• feature extraction
• neural networks
• nondestructive testing