Diagnosis Of Abnormal Energy Consumption Of Roller Kiln Based On Principal Component Analysis

Roller kiln is the core equipment for building ceramics production and the main energyconsuming equipment in the production process.Its operation status will directly affect product quality and energy efficiency.Once an abnormal situation occurs in the production process of the roller kiln and cannot be dealt with in time,it may lead to product defects,increased energy consumption directly,and causing serious safety accidents,even huge losses to the enterprise.With the development of the Internet of Things and industrial informatization,as well as the widespread application of intelligent instruments and industrial control technologies,the use of data-driven abnormal diagnosis technology has become a research hotspot.But at this stage ceramic manufacturers generally rely on manual inspection and fixed threshold alarm abnormal diagnosis methods.The detection cost is high,the accuracy is low,and the abnormal discovery lags significantly.On the other hand,there are few research results on abnormal diagnosis of energy consumption of roller kilns.Research on roller kilns at home and abroad mainly focuses on intelligent control and numerical simulation.Therefore,the thesis proposes a method for detecting and locating abnormal energy consumption in the production process of roller kiln based on principal component analysis.First,focusing on the firing section,the basic principles of the operation process of the roller kiln are comprehensively analyzed,and the common types of abnormal energy consumption are summarized.The improved method establishes the abnormal diagnosis model of energy consumption of roller kiln.Finally,the research content is integrated to realize the abnormal diagnosis module of energy consumption of roller kiln.The specific research contents of this article are as follows:(1)According to the production process of ceramic tiles,the structure and process principles of the roller kiln are comprehensively analyzed,the key attributes that affect energy consumption and product quality are clarified,and the common abnormal types and corresponding causes of the roller kiln production process are summarized.On this basis,the principal component analysis method is used to carry out preliminary verification of energy consumption abnormality diagnosis on the energy consumption data of the roller kiln to determine the applicability of the method.(2)In view of the dynamic characteristics of the roller kiln system,a method for detecting abnormal energy consumption of the roller kiln based on the improved dynamic principal component analysis is proposed.The autocorrelation function is introduced to optimize the construction process of the augmented matrix in the dynamic principal component analysis and effectively reduce the matrix Scale,save computing resources and improve algorithm performance.Through case analysis,the effectiveness of the improved dynamic principal component analysis method for abnormal diagnosis of energy consumption in roller kilns is verified.(3)Aiming at the time-varying characteristics of the roller kiln system,an abnormal diagnosis method of roller kiln energy consumption based on the adaptive step size moving window principal component analysis is proposed.,Optimized the algorithm flow of the recursive update of the moving window,and improved the accuracy and speed of the algorithm.Through case analysis,the effectiveness of the adaptive step size moving window principal component analysis method in the abnormal diagnosis of energy consumption of the roller kiln is verified.Based on the above research content and combining with the specific needs of the enterprise,the energy consumption abnormality diagnosis module was developed with Python,integrated into the enterprise's existing energy management system through service invocation,and was initially applied in enterprise production.