Oil liquid sloshing is a common phenomenon in automobile fuel tank under variable working conditions. Installing baffles in automobile fuel tank is the most effective way to suppress adverse influence caused by oil liquid sloshing. Different types of three-dimensional finite element models filling oil liquid are created, meshed, and simulated. The reliability of simulation results is verified by test. The concept of time–area value is proposed in this work. In order to explore the influence of different baffle factors on oil liquid sloshing, six factors are studied. Six kinds of influencing factors are height, structure, shape, spacing, number, and placement of baffles. The sloshing pressure and time–area value are the core parameters for evaluating the influence degree. Some results could be obtained by comparing the parameters of oil liquid sloshing under the same condition. High baffles and baffles with small spacing have obvious attenuation influence on the pressure of oil liquid sloshing. Low baffles, double baffles, parallel baffles, and the combined action of inertia force and gravity are more beneficial to the reduction of time–area value. Time–area value is the largest and the smallest in fuel tank with intersection baffles and low baffles, respectively.
Because fault characteristics are often difficult to extract from a strong noise background, it is essential for mechanical fault diagnosis to extract a weak characteristic signal with a very small signal-to-noise ratio from a noisy interference. Therefore, this paper proposes a new method for diagnosing weak faults in asymmetric potential stochastic resonance. Compared with the existing methods, the asymmetric potential stochastic resonance method not only has characteristics common to the symmetric potential stochastic resonance, but can also change the inclination of the barrier and slope of the wall to obtain a better model structure. The proposed method solves the local adjustment problem of the existing method from the perspective of potential structure and optimizes the asymmetric system shape to better target frequency detection during much interference from noise. After simulation, a bearing failure test, and rolling mill gearbox bearing failure experiments, we concluded that the asymmetric potential stochastic resonance detection technology can effectively identify faults. Compared with the symmetric potential stochastic resonance method, the proposed method has better recognition.
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