Energy consumption monitoring method for the whole process of spinning based on feature self-matching transfer learning

Abstract

The invention relates to a method for monitoring energy consumption in the whole process of spinning based on feature self-matching migration learning. The method learns knowledge rules from the historical data set of the old factory through a deep convolutional neural network, and migrates to the abnormal trend of spinning energy efficiency in the new factory. Identification is in progress to make up for the lack of abnormal samples in new plants. At the same time, in order to solve the negative migration problem caused by the mismatch of data features between the source domain and the target domain during the migration process, a cluster-based feature self-matching layer network is designed to minimize the distance of similar features through the feature matching matrix and eliminate outliers. Features, promote the positive transfer of effective knowledge, and inhibit the negative transfer of invalid interference knowledge. Compared with the existing methods, the proposed model has higher detection accuracy and lower false negative rate of spinning energy efficiency anomaly.

Description

technical field [0001] The invention relates to a method for monitoring energy consumption in the whole process of spinning, in particular to a method for monitoring energy consumption in the whole process of spinning based on feature self-matching transfer learning, and belongs to the technical field of production energy consumption monitoring. Background technique [0002] Spinning is a typical energy-intensive industry for people's livelihood. In 2018, China's spinning power consumption reached about 70 billion kWh, but the effective utilization rate was less than 75%, and a large amount of energy consumption was wasted in the abnormal discovery of lagging behind. In the yarn production process, due to production events or changes in the production environment, the power consumption per ton of yarn often deviates from the normal value, and the lagging discovery leads to a lot of energy waste. Energy efficiency monitoring in the production process is one of the most effect...

AI Technical Summary

Problems solved by technology

[0005] The purpose of the present invention is to realize the early monitoring of energy consumption in the whole process of spinning, transfer the historical energy consumption data of the old factory to the energy consumption monitoring model of the new factory through the transfer learning method, improve the detection accuracy of energy consumption symptoms, and solve the problem of new factories Energy consumption monitoring cold start problem

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