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The status monitoring and failure detection for equipment operation have always been important means to protect
equipment for its safe and reliable operation. Therefore, establishing of a self-adaptive selection and decision optimizing
model based on trend prediction method can self-adaptively select trend prediction method according to actual operating
status so as to improve failure prediction accuracy and expand application range of failure prediction. The failure
prediction experimental device was established to verify the practical application of optimal objective function in the
fault prediction. The self-adaptive selection and decision optimizing method, which realizes the failure prediction for
large size rotating equipments base on vibration signal, not only can adapt failure predictions of different rotating
equipments, but also can realize the real-time online prediction for rotating equipment status; moreover, it has self-adaptive
judgment method for multiple vibrating trend prediction models so that the optimal prediction results has high
judgment success rate. Meanwhile, it provides trend prediction method adopting multiple prediction models and provides
prediction results conducted by multiple prediction models. Compared with historical actual value, it has higher
judgment value of failure early warning.
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