RESUMO
Vibrations of defective gearboxes show great complexities. Therefore, dynamics and noise levels of vibrations of gearboxes vary with operation of gearboxes. As a result, nonlinearity and determinism of data can serve to describe running conditions of gearboxes. However, measuring of nonlinearity and determinism of data is challenging. This paper defines a two-dimensional measure for simultaneously quantifying nonlinearity and determinism of data by comparing generalized Hurst exponents of original, shuffle and surrogate data. Afterwards, this paper proposes a novel method for fault diagnosis of gearboxes using the two-dimensional measure. Robustness of the proposed method was validated numerically by analyzing simulative signals with different noise levels. Moreover, the performance of the proposed method was benchmarked against Approximate Entropy, Sample Entropy, Permutation Entropy and Delay Vector Variance by conducting two independent gearbox experiments. The results show that the proposed method achieves superiority over the others in fault diagnosis of gearboxes.
RESUMO
This study examined scaling properties of an increment series from rotating machinery. Moreover, two fluctuation parameters for the smallest and largest time scales of a scaling range served as a pair of fluctuation parameters to describe system conditions. Therefore, an interesting phenomenon is observed: the data points, each representing a pair of fluctuation parameters, for fault conditions almost form a straight line, while those for normal clearly depart from the straight line. To describe the phenomenon, a novel concept termed the diagnostic line was introduced. Subsequently, properties of the diagnostic line were carefully investigated theoretically and numerically. Consequently, a decisive role of noise in forming the diagnostic line was determined. Accordingly, this study develops a novel criterion for condition monitoring of rotating machinery.