近期，我院教师孙慧、陈宁教授等的“HHT-based feature extraction of pump operation instability under cavitation conditions through motor current signal analysis”研究成果在《Mechanical Systems and Signal Processing》上发表。
Effective cavitation detection is significant to ensure reliability and efficiency of pump operation and prolong the life cycle. This research work adopts motor current signal analysis (MCSA) technology and improves accuracy and reliability for feature extraction by using Hilbert-Huang Transform (HHT). Experimental investigation was conducted to acquire current signals during cavitation process. A closed test rig was utilized, thoroughly monitored by transducers of high accuracy in order to fully characterize both normal and cavitation status of pump operation. Based on HHT method, current signals are decomposed into Intrinsic Mode Functions (IMFs) by Empirical Mode Decomposition (EMD). Marginal spectra are further obtained by Hilbert transform. Feature extraction is conducted based on cavitation characteristics. According to the current signal processing, occurrence and developing stages of cavitation could be characterized by the indicators with high sensitivity.