Equipment fault self-adaptive upper and lower early warning boundary generation method based on convolutional neural network

Abstract

The invention provides an equipment fault self-adaptive upper and lower early warning boundary generation method based on a convolutional neural network, which can be used for fault diagnosis of a chemical fiber winding machine in a spinning process. The method comprises a convolutional neural network model for interval prediction and an upper and lower boundary model for interval self-adaptive generation and classification. According to the invention, fault diagnosis is carried out on vibration signals collected by a chemical fiber winding machine during the spinning process, the defects thatan existing fault diagnosis technology is not high in accuracy and is easily influenced by human factors are overcome, a cost-sensitive learning module is introduced to optimize a loss function in the convolutional neural network updating iteration process, and therefore a machine learning fault detection method with the misclassification cost as the optimization target is obtained. The method provided by the invention has good practicability under the condition of unbalanced samples.

Description

technical field [0001] The invention relates to a method for diagnosing a chemical fiber winding machine fault that combines cost-sensitive learning and a classification algorithm of a convolutional neural network, and belongs to the field of fault diagnosis and machine learning. Background technique [0002] The normal operation of chemical fiber equipment plays a vital role in the quality of chemical fiber products, the safety of chemical fiber production and the normal use of chemical fiber equipment. Because the chemical fiber production process is more complicated than traditional mechanical manufacturing, it is mainly manifested in the formation of silk yarns from solid raw materials through high temperature and high pressure, cooling and air drying, involving melting, extrusion, spinning, clustering, winding, texturing and other processes. The winding of chemical fiber yarn is a very complicated process. The structure of the winding machine is complex and there are ma...

AI Technical Summary

Problems solved by technology

[0005] The technical problem to be solved by the present invention is: the accuracy of existing fault diagnosis technology is not high, and it is easily affected by human factors

Images