Degradation Feature Extraction of Quay Crane Gearbox Based on Static Divided Symbol Sequence Entropy

Abstract

Gearbox is an important transmission equipment of quay crane hoisting mechanism. In order to accurately extract the degradation features from the vibration monitoring signal, a degradation feature extraction technique based on the static divided symbol sequence entropy is proposed. Considering the uniformity of the symbolization standard, the technique takes the root mean square of the signal in health condition as the basis, and combines the scale coefficient to establish a uniform basic scale. At the same time, the symbol set is expanded to enhance the information content and the ability of characterizing complexity of signal in large-value region. The Logistic chaotic sequence and the lifetime signal of hoisting mechanism gearbox are used for analysis respectively. The results show that the proposed technique is able to characterize the complexity of the nonlinear time series, and sensitively describe the performance degradation of the hoisting mechanism gearbox. The calculation speed is fast, which will lay a method foundation for further evaluating the health condition of large-scale quay crane in the port.