煤田地质与勘探2025，Vol.53Issue(4)：141-152,12.DOI:10.12363/issn.1001-1986.24.12.0801
基于NRBO-XGBoost的煤体破坏声发射特征及裂纹扩展状态智能识别
Intelligent identification of the acoustic emission characteristics and crack propagation states during of coal failure based on the NRBO-XGBoost model
摘要
Abstract
[Objective]The dynamic hazards of coals are intimately associated with the complexity and uncertainty of coal failure,while accurately identifying the propagation states of coal cracks serves as a critical approach to investigat-ing the instability failure of coals.However,the current identification methods based on the physical quantities of acous-tic emission suffer from slow speeds and low efficiency,failing to meet the demand for safe,efficient,and intelligent mining of coal mines.[Methods]To achieve the intelligent identification of the crack propagation states of coals,this study constructed an optimization model based on the Newton-Raphson-based optimizer(NRBO)and the eXtreme Gradient Boosting(XGBoost)-a distributed gradient boosting library(also referred to as the NRBO-XGBoost model).Then,this study determined the corresponding relationships of multivariate parameters(i.e.,values b representing the re-lationship between acoustic emission magnitude and frequency,S representing the activity,and RA and AF representing the fracture types)with the crack propagation states of coals.Using experiments on acoustic emission monitoring of coal failure,this study acquired the multivariate signal data of acoustic emission,which were subsequently used as training samples.Employing the NRBO-XGBoost model,this study performed intelligent identification of the stable and un-stable crack propagation stages(stages Ⅲ and Ⅳ,respectively)of coals under varying loading rates,achieving adaptive optimization of XGBoost parameters.Furthermore,this study evaluated the performance of various models using four metrics:accuracy,precision,recall,and F1 score.[Results and Conclusions]The results indicate that in stage Ⅲ,coals exhibited slightly increased values b and S and a decreased proportion of shear cracks.After entering stage Ⅳ,coals manifested opposite variation trends in value b and the proportion of shear cracks,along with significantly increased value S.With an increase in the loading rate,value b and its fluctuation amplitude decreased,value S showed a reduced increment,and the proportion of shear cracks exhibited a rising increment.The NRBO-XGBoost model enabled the in-telligent identification of the crack propagation states of coals based on multivariate acoustic emission parameters.Com-pared to the XGBoost and PSO-XGBoost models,the NRBO-XGBoost model yielded higher accuracy,precision,recall,and F1 scores,which were 90.69%,88.79%,99.00%,and 93.62%,respectively under low loading rates and 82.76%,86.76%,88.50%,and 87.62%,respectively under high loading rates.The results of this study provide a novel philo-sophy for the identification and intelligent monitoring of the crack propagation states of coals using the acoustic emis-sion technique.Establishing prediction models based on the measured acoustic emission data allows for the monitoring and early warning of dynamic hazards of coals.关键词
煤体破坏/NRBO-XGBoost/裂纹扩展/智能识别/声发射多元参量/加载速率Key words
coal failure/NRBO-XGBoost/crack propagation/intelligent identification/multivariate acoustic emission parameters/loading rate分类
矿业与冶金引用本文复制引用
张天军,曹新爽,宋爽,贺绥男,薛仪伦,刘国瑛,陈俊涛..基于NRBO-XGBoost的煤体破坏声发射特征及裂纹扩展状态智能识别[J].煤田地质与勘探,2025,53(4):141-152,12.基金项目
国家自然科学基金项目(52374228) （52374228）
煤炭资源与安全开采国家重点实验室开放基金项目(SKLCRSM23KF003) （SKLCRSM23KF003）
陕西省教育厅重点实验室项目(24JS030) （24JS030）
