Abstract
The vibration signal of heavy gearbox has the nonlinear and nonstationary characteristic, which makes the gear fault diagnosis difficult. Moreover, the useful fault information is mainly focused on the high-frequency components of the raw signal, which also affects the fault feature extraction from vibration signal. For this reason, a novel signal processing method based on variational mode decomposition (VMD) and detrended fluctuation analysis (DFA) is proposed to diagnose the gear faults of heavy gearbox. Since high-frequency component contains more fault information, the raw vibration signal is decomposed several mode components by VMD, which can remove the low-frequency component to retain the high-frequency component. Moreover, the most sensitive mode component is selected in these high-frequency components by a maximal indicator, which is composed of kurtosis and correlation coefficient. The most sensitive mode component is calculated by DFA to obtain bi-logarithmic map, and the sliding windowing algorithm is employed to capture turning point of the bi-logarithmic map, thus extracting the fault feature of small time scale to identify gear faults. The effectiveness of the proposed method for fault diagnosis is validated by experimental data analysis, and the comparison results demonstrate that the recognition rate of gear faults condition have marked improvement by proposed method than the DFA of small time scale (STS-DFA) and EMD-DFA.
Highlights
Heavy gearboxes are widely used in manufacture, metallurgic, and marine fields for their advantages of strong load-bearing capacity, compact structure, and large transmission ratio. e high-speed gearbox usually works in the harsh operating conditions, such as inevitable impact and complex alternating loads which cause the gear to be difficult to avoid crack, pitting, scratch, and spalling [1, 2]
Motivated by the previous work, since the feature vector of local fluctuation corresponding to highfrequency components show better performance for gear fault classification, a novel method of variational mode decomposition (VMD) incorporation with detrended fluctuation analysis (DFA) is applied in the gear fault diagnosis
The raw vibration signal from gearbox is decomposed by VMD, and it is used to extract high-frequency mode components corresponding to local fluctuation, which eliminates the influence of fluctuation corresponding to the large time scale. e index methods of kurtosis and cross-correlation in Equations (14) and (15) are used to select the most sensitive mode component from decomposed modes
Summary
Heavy gearboxes are widely used in manufacture, metallurgic, and marine fields for their advantages of strong load-bearing capacity, compact structure, and large transmission ratio. e high-speed gearbox usually works in the harsh operating conditions, such as inevitable impact and complex alternating loads which cause the gear to be difficult to avoid crack, pitting, scratch, and spalling [1, 2]. Wang et al [32] proposed a method which combined the scale exponent with intercept in DFA double logarithmic map which used the small time scale to classify the fault pattern of gears. STS-DFA shows better performance of fault pattern recognization because of the fractal features of small time scale, which represents the local fluctuation as well as high-frequency component [32]. Motivated by the previous work, since the feature vector of local fluctuation corresponding to highfrequency components show better performance for gear fault classification, a novel method of VMD incorporation with DFA is applied in the gear fault diagnosis. DFA is employed to extract the fractal feature vector of high-frequency mode components, the feature vectors of the small time scale are used to the fault diagnosis of the gear.
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