Complex equipment maintenance decision-making method based on fault prediction

Abstract

The invention provides a complex equipment maintenance decision-making method based on fault prediction. The method comprises the following steps: A, determining a feature factor related to an equipment fault, setting a fault threshold of the feature factor, and collecting the historical data of the feature factor; B, predicting the numerical value of the feature factor through a gray model and aBP neural network model respectively; C, determining weights of the gray model and the BP neural network model; and D, carrying out numerical prediction on the equipment feature factor based on the combination model determined by the weight, taking the moment when a fault threshold value is reached as a prediction fault moment, and determining the optimal maintenance opportunity. The method has the following advantages: the advantages that the gray model has low requirements for sample data volume and the BP neural network has strong autonomous learning capability are combined, high-precisionprediction of the feature factor is effectively realized, the maintenance time is determined in time according to comparison with a fault threshold, preventive maintenance can be carried out in time,and normal operation of equipment is ensured.

Description

technical field [0001] The invention relates to the technical field of equipment predictive maintenance, in particular to a complex equipment maintenance decision-making method based on failure prediction. Background technique [0002] With the continuous advancement of science and technology, complex engineering equipment such as wind turbines, CNC machine tools, and construction machinery are gradually developing towards flexibility, precision, and intelligence. This type of equipment system can significantly increase production efficiency, improve the production environment, improve product quality, and reduce business operating costs. However, correspondingly, once such a complex equipment system fails or its health status deteriorates, it will often cause huge economic losses to the enterprise, and even cause catastrophic consequences. However, traditional post-event maintenance and preventive maintenance have been difficult to meet the intelligent support and maintena...

AI Technical Summary

Problems solved by technology

[0006] Complex equipment has the characteristics of complex structure, complex life distribution, and complex working status, resulting in fewer effective samples of equipment and inaccurate prediction of status trends, which increases the difficulty of equipment maintenance and waste of resources

The current research on predictive maintenance of complex equipment includes but is not limited to the following three difficulties: (1) Some characteristic factors are difficult to implement effective monitoring, resulting in few effective samples for fault state prediction; (2) Equipment fault prediction accuracy is not high, and maintenance timing Inaccurate grasp; (3) The influencing factors of equipment maintenance are characterized by ambiguity and uncertainty

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