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Gearbox fault diagnosis is one of the core research areas in the field of rotating machinery condition monitoring. The signal processing-based bearing fault diagnosis in the gearbox is considered as challenging as the vibration signals collected from acceleration transducers are, in general, a mixture of signals originating from an unknown number of sources, i.e. an underdetermined blind source separation (UBSS) problem. In this study, an effective UBSS-based algorithm solution, that combines empirical mode decomposition (EMD) and kernel independent component analysis (KICA) method, is proposed to address the technical challenge. Firstly, the nonlinear mixture signals are decomposed into a set of intrinsic mode function components (IMFs) by the EMD method, which can be combined with the original observed signals to reconstruct new observed signals. Thus, the original problem can be effectively transformed into over-determined BSS problem.

Due to the high center of gravity of medium-duty vehicles, rollover accidents can easily occur during high-speed cornering and lane changes. In order to prevent the deformation of the body structure, which would restrict the survival space and cause compression injuries to occupants, it is necessary to investigate methods for mitigating these incidents. This paper establishes a numerical model of right-side rollover for a commercial medium-duty vehicle in accordance with ECE R66 regulations, and the accuracy of the model is verified by experiment. According to the results, the material and size parameters of the key components of the right side pillar are selected as design variables. The response result matrix was constructed using the orthogonal design method for total mass, energy absorption, maximum collision acceleration, and minimum distance from the survival space.