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• 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]

• The proposed unsupervised method merges anomaly detection and fault tracing. • Signal decomposition-based energy features are extracted to make the diagnosis more robust. • The model training process only requires normal data. • The diagnostic process can be performed without labeled fault samples. The absence of detailed fault data presents a significant challenge in diagnosing faults of rotating machinery. This paper focuses on addressing abnormal detection and fault localization challenges in drivetrain components under the assumption that only normal data is available. Specifically, this paper proposes an unsupervised hypersphere description (UHD) framework by seamlessly integrating abnormal detection and localization. By constructing a one-class description hypersphere, this UHD method achieves fault detection without special fault samples. Abnormal samples can be detected based on their distance from the center of hypersphere, enabling accurate localization of the underlying abnormality. Validation of experimental data demonstrates that the UHD approach effectively detects anomalies and accurately localizes faults within specific locations. Remarkably, the UHD approach surpasses several mainstream unsupervised methods in terms of fault tracing accuracy. This shows the potential of the UHD approach in abnormal detection and localization in a drivetrain, particularly when only normal data is available. [ABSTRACT FROM AUTHOR]