Location of abnormal energy consumption and optimization of energy efficiency of hydraulic press considering uncertainty

Abstract

As a high energy consumption machine, there is plenty of abnormal energy consumption in the operation of a hydraulic press, which leads to energy loss and reduces energy efficiency. The time series of energy consumption data are characterized by intense uncertainty, nonlinearity, and non–Gaussianity. The traditional method is used to locate it, which has the problems of a large error and uncertain locating result. Therefore, an abnormal energy consumption locating technique considering uncertainty is developed. To quantitatively evaluate the correlation between abnormal machine components and energy consumption, an uncertainty evaluation method combining entropy weight and fuzzy theory is proposed, which aims to provide a decision basis for the location of abnormal energy consumption. To accurately locate abnormal energy consumption, a novel localization technique is proposed, which is called genetic algorithm–based wavelet neural network. Firstly, the energy consumption data is processed by wavelet packet decomposition and reconstruction. The energy value is calculated, and then the dimension reduction is performed by principal component analysis. Finally, energy value is input into the genetic algorithm–based backpropagation neural network model for training and anomaly location. We integrate the proposed method into the energy management system and perform energy efficiency optimization analysis. The results show that the proposed method can improve the energy efficiency of a hydraulic press, reduce energy cost, and reduce environmental pollution. This provides a solution for solving the cleaner production problem of high energy consumption manufacturing industry and realizing sustainable intelligent manufacturing.