基于模态分析和PCA-WOA-RF的磨煤机下架体壳振预测

doi:10.6040/j.issn.1672-3961.0.2024.228

摘要/Abstract

摘要： 为探究磨煤机下架体壳振与其他运行参数之间的复杂非线性映射关系,并提高磨煤机下架体壳振预测的准确性,提出一种基于PCA-WOA-RF模型的磨煤机下架体壳振预测方法。对磨煤机下架体进行模态分析,验证下架体壳振标准值,使用Spearman相关系数法和主成分分析法(principal component analysis, PCA)对磨煤机工作数据进行相关性分析并提取主成分;以随机森林(random forest, RF)为预测模型结构基础,使用鲸鱼优化算法(whale optimization algorithm, WOA)对模型的超参数进行优化;以国能长源武汉青山热电有限公司磨煤机工作数据进行实例验证,并与PCA-BP、PCA-SVM和PCA-RF模型进行精度对比。结果表明:一次风流量、拉杆应变、磨煤机电机轴振动、中架体壳振、煤量和一次风出入口差压与磨煤机下架体壳振有显著相关性,经过主成分分析法提取的2个主成分方差贡献率达94.569%,所提出的PCA-WOA-RF模型平均预测误差最小,预测精度达到97.80%。该模型进一步提升了磨煤机下架体壳振预测精度。

关键词: 磨煤机, 下架体壳振, 主成分分析, 随机森林, 鲸鱼优化算法

Abstract: In order to investigate the complex nonlinear mapping relationship between the shell vibration of the lower frame body of the coal mill and other operating parameters, and to improve the accuracy of the prediction of the shell vibration of the lower frame body of the coal mill, a prediction method of the shell vibration of the lower frame body of the coal mill based on the PCA-WOA-RF model was proposed. Modal analysis was carried out on the lower frame body of the coal mill to verify the standard value of shell vibration of the lower frame body, correlation analysis was conducted on the working data of the coal mill using Spearman correlation coefficient method and principal component analysis(PCA)method and principal components were extracted. Random forests(RF)were used as the basis of the structure of the prediction model, and the hyperparameters of the model were optimised using whale optimisation algorithm(WOA). The coal mill working data of Guoneng Changyuan Wuhan Qingshan Thermal power Co., Ltd. was used as an example for validation, and the accuracy was compared with PCA-BP, PCA-SVM and PCA-RF models. The results showed that the primary air flow, tie rod strain, coal mill motor shaft vibration, mid-frame body shell vibration, coal volume and primary air inlet and outlet differential pressure were significantly correlated with the lower frame body shell vibration of the coal mill, the variance contribution of the two principal components extracted by principal component analysis was 94.569%, and the proposed PCA-WOA-RF model had the smallest average prediction error, and the prediction accuracy reached 97.80%. The model further improved the prediction accuracy of the shell vibration of the lower frame body of the coal mill.

Key words: coal mill, lower frame shell vibration, principal component analysis, Random Forest, whale optimization algorithm

中图分类号:

TM621

赵小惠,刘磊,蒲军平,成小乐,高畅,胡胜. 基于模态分析和PCA-WOA-RF的磨煤机下架体壳振预测[J]. 山东大学学报 (工学版), 2026, 56(1): 149-157.

ZHAO Xiaohui, LIU Lei, PU Junping, CHENG Xiaole, GAO Chang, HU Sheng. Prediction of shell vibration of coal mill lower frame body based on modal analysis and PCA-WOA-RF[J]. Journal of Shandong University(Engineering Science), 2026, 56(1): 149-157.

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