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Abstract
The goal of this work is to improve the generalization of remaining useful life (RUL) prognostics for wheel hub bearings. The traditional life prognostics methods assume that the data used in RUL prognostics is composed of one specific fatigue damage type, the data used in RUL prognostics is accurate, and the RUL prognostics are conducted in the short term. Due to which, a generalizing RUL prognostics method is designed based on fault signal data. Firstly, the fault signal model is designed with the signal in a complex and mutative environment. Then, the generalizing RUL prognostics method is designed based on the fault signal model. Lastly, the simplified solution of the generalizing RUL prognostics method is deduced. The experimental results show that the proposed method gained good accuracies for RUL prognostics for all the amplitude, energy, and kurtosis features with fatigue damage types. The proposed method can process inaccurate fault signals with different kinds of noise in the actual working environment, and it can be conducted in the long term. Therefore, the RUL prognostics method has a good generalization.
Highlights
Wheel hub bearings are one of the key parts of automobiles
The long term remaining useful life (RUL) of wheel hub bearings can be dynamically prognosticated in real time in a complex and changing working environment
The experimental results show that this method gained good accuracies of RUL prognostics for all the amplitude, energy, and kurtosis features with fatigue damage types
Summary
Wheel hub bearings are one of the key parts of automobiles. Their main function is to bear the load of the automobile and provide precise guidance for the wheel hub rotation, which means that the wheel hub bearings bear the axial load and bear the radial load. More often, long term RUL prognostics are required to ensure maximum safety and reliability, especially in the early running stage of wheel hub bearings To solve these problems, it is necessary to design a generalizing RUL prognostics method. It is necessary to design a generalizing RUL prognostics method With this new method, the long term RUL of wheel hub bearings can be dynamically prognosticated in real time in a complex and changing working environment. The experimental results show that this method gained good accuracies of RUL prognostics for all the amplitude, energy, and kurtosis features with fatigue damage types This method can process inaccurate fault signals with different kinds of noises in the actual working environment. A fault-signal-based generalizing RUL prognostics method is proposed for wheel hub bearings. The proposed RUL prognostics method is validated in the fourth part, and we draw a conclusion in the last part
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