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• Gearbox fault diagnosis using BFNCC is proposed for nonstationary condition. • The method can remove the effect of the structure transfer paths. • The proposed method is robust to the location of measuring points. • Method shows potential for a machinery with complicated structure transfer paths. The main challenges in gearbox condition monitoring and fault diagnosis are the heavy noises caused by harsh operating environments and the complicated nonstationary condition during the runtime. It is extremely difficult and even impossible to obtain sensitive fault characteristics through direct measurements. Thus, it is of great significance of fault characteristic inversion based on the structure vibration transfer paths for complicated mechanical systems fault diagnosis. This paper proposes a gearbox fault diagnosis method under nonstationary condition through using nonlinear chirp components extracted from bearing force (BFNCC). In the proposed method, the bearing dynamic forces are first identified based on the vibration transfer function matrix, and the intrinsic signal components are then decomposed through a nonlinear chirp mode decomposition method. The proposed method improved the quality of vibration signals by removing the effect of the structure vibration transfer paths. In comparison with order tracking, the BFNCC is capable of clearly reflecting the magnitude increase phenomenon under gear fault condition. Numerical simulation and experimental studies show that the proposed method is effective for gear fault diagnosis under nonstationary condition. The proposed method is demonstrated to be superior to the existing approaches and robust to the location of measuring points, and thus shows potential in machinery fault diagnosis under the interference of complicated structure transfer paths. [ABSTRACT FROM AUTHOR]

• A gearbox fault diagnosis method based on bearing force identification is proposed. • Identified bearing dynamic forces outperform vibration signals for fault diagnosis. • The proposed method avoids the dependence on the layout of measuring points. • The method can theoretically be combined with the feature extraction algorithms. In the area of gearbox fault diagnosis, to obtain sufficient and effective vibration information, traditional studies are usually conducted from the perspective of optimizing the layout of measuring points. This paper focuses on a gearbox fault diagnosis method based on bearing dynamic force identification. Gearbox bearing dynamic forces under the operating condition are modeled as excitation sources, and vibration signals measured at any position on the housing are modeled as receivers. Then mutual coupling effect of different structure transfer paths of housing on the excitation signals can be quantitatively modeled. The bearing dynamic forces are finally constructed with the transfer function matrix and vibration signals by solving the inverse problem. The identified bearing dynamic forces is capable of clearly reflecting the gear fault characteristics, which outperforms the original vibration signals measured on the gearbox housing due to poor signal quality and the effect of structure transfer paths. Numerical simulation and experimental studies show the effectiveness of the estimated bearing dynamic force signals for gear fault diagnosis. The proposed method is demonstrated to be insensitive to the location of measuring points, and shows a good potential in complicated mechanical system fault diagnosis. [ABSTRACT FROM AUTHOR]

• A mediator algorithm between simulation and experiment is proposed to identify the error excitation. • The proposed identification algorithm provides an indirect approach to estimate the error excitation of gears. • The pre-decomposition technique is proposed to accelerate the solution of the simulation dynamic model. • The proposed identification algorithm is robust to noise, damping and phase differences. • The performance of the proposed identification algorithm is verified by both simulation and experiment. There are three primary sources of internal excitation in gear systems: time-varying mesh stiffness, meshing damping and tooth error. Compared with the first two excitations, it is more difficult to evaluate or measure the error excitation. The error excitation is usually assumed empirically or even neglected in the majority of dynamic models. To address this issue, we desired to propose a mediator algorithm between the simulation and the experiment to identify the error excitation of gear systems. Based on the second-order cyclostationarity of gear signals, a signal processing procedure is proposed to acquire the mediator signal with a high signal-to-noise ratio. The mediator signal is served as the "media" between the simulation and experiment. The error parameter is updated repeatedly in the iterative optimization until the optimal matching is found between the simulation and the experiment. Simultaneously, the pre-decomposition technique is proposed to accelerate the solution of the simulation dynamic model. The performance of the proposed identification algorithm is verified both numerically and experimentally. Moreover, noise immunity analysis shows that the proposed identification algorithm is robust to noise, damping and phase differences between the experiment and simulation. The proposed identification algorithm provides an indirect approach to estimating the error excitation when the direct measurement is unavailable. [ABSTRACT FROM AUTHOR]

• An in-situ transfer path analysis method is proposed to avoid physical decoupling ;. • A virtual decoupling method is applied to estimate the decoupled frequency response function;. • The effectiveness of the proposed method is verified both numerically and experimentally ;. • The proposed method is capable of tracing the fault location by determining the dominant transfer path;. • A physically-interpretable signal decomposition method is proposed to enhance the fault feature. A transfer path analysis method based on the in-situ measurement is proposed for the fault tracing of parallel axis gear systems. A virtual decoupling technique is used to achieve the in-situ measurement of the decoupled frequency response function (FRF) between bearing positions and measuring points on housing, avoiding the tedious test procedure and non-representative assembly conditions caused by the physical decoupling. Based on the decoupled FRF and the responses of the gearbox housing, the bearing force is identified using the Tikhonov regularization theory. Then the frequency-dependent path contribution, which will be used to perform the transfer path analysis, is obtained by multiplying the decoupled FRF and the bearing force. Finally, the fault-specific path rank order is determined using the path contribution within the resonant frequency band. This band is located by the spectral kurtosis algorithm. Based on the proposed in-situ path analysis, fault tracing is conducted by determining the dominant transfer path. The effectiveness of the proposed path analysis method is verified both numerically and experimentally. A two-stage parallel axis gear test rig with localized defects is utilized to demonstrate the efficacy of the proposed fault tracing strategy. Moreover, based on the path analysis, a physically-interpretable signal decomposition method is proposed to enhance the fault feature. Through this decomposition method, the fault indicator of the weakest fault type (chipped tooth) can be magnified by 204%. The proposed transfer path analysis method provides a feasible strategy for fault tracing, feature enhancement and incipient fault diagnosis of parallel axis gear systems. [Display omitted] [ABSTRACT FROM AUTHOR]