In recent years,with the development of big data and artificial intelligence technology,log data oriented intelligent fault prediction gradually attracts research attention.Conventional fault prediction methods based on log data only focus on a single requirement attribute,making it difficult to adapt to the multi-dimensional requirement prediction.To address this problem,this paper proposes Multi-Det,an intelligent fault prediction model with multi-channel fusion,which designs specific scheduling method that incorporates machine learning and deep learning.Through feature extraction and data fusion,the system model is well adapted to different user requirement attributes to optimize the accuracy and reliability of fault prediction in specific scenarios.To verify the effectiveness of Multi-Det,experimental comparisons of requirement parameters in multiple scenarios are carried out on public datasets.The results indicate that the proposed method can effectively adapt to different fault prediction requirements,intelligently adjust the prediction strategy in specific scenarios,and provide strong support for the maintenance and management of device in specialized fields.
NETL
NSTL
维普
万方数据
