Abstract
Abstract Wheel hub bearing not only bears axial load but also radial load, and then its running status directly affects the performance and safety of the automobile. In particular, complicated and variable driving conditions of the automobile are not only liable to aggravate the occurrence of mechanical fault such as localized wear, but also often cause powerful intermittent interference noise. It is difficult to extract the vibration characteristics of the transient impact and harmonic components of the wheel bearings in the event of a local fault. An extraction method of fault feature based on ration of smooth and kurtosis (RSK) index and resonance-based signal sparse de-composition (RSSD) is proposed for automobile wheel hub bearings, which aims at the minimum value of RSK index, sequentially optimizes the Q-factor of the resonance sparse decomposition, and obtains its optimal value adaptively to achieve the decomposition of low resonance components with transient impact components and high resonance components with harmonic components. The effect of the method has been verified by powerful intermittent interference simulation signals and experiment signals from local fault of wheel hub bearing with periodic dynamic radial loading.
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