Incremental equipment fault diagnosis method based on knowledge distillation and hidden layer sharing

Abstract

The invention discloses an incremental equipment fault diagnosis method based on knowledge distillation and hidden layer sharing, and relates to the field of bearing equipment fault diagnosis. According to the method, a knowledge distillation and hidden layer sharing technology is utilized, so that a shallow layer equipment fault diagnosis model is guaranteed to have relatively good data characteristic extraction capability, and the fault classification performance of the shallow layer equipment fault diagnosis model is improved. For continuous increase of industrial data and update of an edgeequipment fault diagnosis model, methods of effective sample identification, data set reconstruction, pre-training model fine adjustment and the like are used for realizing incremental learning of the model. The requirements on the network bandwidth and the network delay in a massive real-time industrial equipment data transmission process are met; the accuracy of a shallow layer equipment faultdiagnosis method is improved; and the incremental learning is supported. Through a simulation experiment of bearing operation state data, under the condition that calculation resources are limited, the edge cloud collaborative data transmission efficiency is improved and the fault prediction classification accuracy is realized; and the incremental data learning and processing are supported.

Description

Technical field [0001] The invention relates to the technical field of fault diagnosis of bearing equipment, in particular to an incremental equipment fault diagnosis method based on knowledge distillation and hidden layer sharing. Background technique [0002] With the development of the Industrial Internet of Things, cloud computing, and big data, large-scale equipment in the industrial field will continue to generate massive operating status data during the production process. How to analyze the operating status of the equipment based on these data and use big data and machine learning technology To predict equipment failures and reduce production termination or personnel accidents caused by unexpected equipment failures has become a hot spot in the field of intelligent manufacturing. At present, with the deep integration of the Internet of Things and the industrial field, data acquisition in the equipment production process has become easier, and equipment fault diagnosis met...

AI Technical Summary

Problems solved by technology

In addition, due to the complexity and time-varying nature of industrial equipment, data is continuously generated. Therefore, the deep neural network model needs to save the original knowledge and learn the knowledge in the new data through incremental learning. At present, the incremental extreme learning machine, incremental Both the incremental learning neural network model and the incremental support vector machine have achieved good results, but how to combine incremental learning with the edge model to ensure that the edge device model can be continuously updated has become an urgent problem to be solved.

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