Degradation Feature Extraction from Quay Crane Gearboxes Based on StaticDivided Symbol Sequence Entropy

Abstract

Gearboxes are important transmission equipment for quay crane hoisting mechanisms. To accurately extract degradation features from vibration monitoring signals, a degradation feature extraction technique based on static divided symbol sequence entropy is proposed. Considering the uniformity of the symbolization standard, the technique takes the root mean square of the health condition signal as the basis and incorporates the scale coefficient to establish a uniform basic scale. Simultaneously, the symbol set is expanded to enhance the information content and the ability of the approach to characterize the complexity levels of signals in large-value regions. The logistic chaotic sequence and the lifetime signal of the hoisting mechanism gearbox are separately used for analysis. The results show that the proposed technique is able to characterize the complexity of the nonlinear time series and sensitively describe the performance degradation exhibited by the hoisting mechanism gearbox. The calculation speed is fast, which will lay a foundation for methods to further evaluate the health conditions of large-scale quay cranes in ports.