A fault diagnosis method for coal mills based on Bayesian network under imbalanced data conditions

张涛; 邵毅; 刘乐源; 郝欣; 胡绍宇

doi:10.19666/j.rlfd.202503025

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A fault diagnosis method for coal mills based on Bayesian network under imbalanced data conditions

更新时间：2025-11-21

- A fault diagnosis method for coal mills based on Bayesian network under imbalanced data conditions
- Thermal Power Generation Issue 11, Pages: 117-125(2025)
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- DOI：10.19666/j.rlfd.202503025
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- Published：2025
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张涛, 邵毅, 刘乐源, et al. A fault diagnosis method for coal mills based on Bayesian network under imbalanced data conditions[J]. Thermal Power Generation, 2025, (11): 117-125.
DOI：

张涛, 邵毅, 刘乐源, et al. A fault diagnosis method for coal mills based on Bayesian network under imbalanced data conditions[J]. Thermal Power Generation, 2025, (11): 117-125. DOI： 10.19666/j.rlfd.202503025.

摘要

针对燃煤电厂磨煤机制粉系统故障诊断中因数据分布不均衡导致的少数类故障诊断精度低、可解释性差的问题，提出一种融合SMOTE数据增强、Dirichlet先验平滑与贝叶斯网络的故障诊断方法。通过SMOTE技术对少数类故障样本进行特征空间扩展，缓解数据稀缺性；结合Dirichlet先验平滑优化贝叶斯网络条件概率估计，解决样本缺乏导致的零概率问题；构建分层贝叶斯网络架构，融合领域知识与数据驱动结构学习，实现故障节点快速推断与属性节点间接推断的双模式诊断策略。实验基于真实工业数据，在非均衡数据场景下，所提方法具有较高的诊断精度与可解释性，为磨煤机故障诊断提供兼具实时性、准确性及透明性的解决方案。

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樊帅,肖军.锅炉制粉系统故障诊断方法[J].热力发电, 2015, 44(2):13-17.FAN Shuai, XIAO Jun. Fault diagnosis method of pulverizing system in utility boilers[J]. Thermal Power Generation, 2015, 44(2):13-17.

钱虹,张现涛.基于PCA-FINCH的磨煤机故障诊断方法[J].热力发电, 2023, 52(9):147-154.QIAN Hong, ZHANG Xiantao. Fault diagnosis method of coal mill based on PCA-FINCH[J]. Thermal Power Generation, 2023, 52(9):147-154.

姚天杨,茅大钧,韩万里.基于DPC-MND和多元状态估计的磨煤机故障预警研究[J].控制工程, 2024,31(5):928-937.YAO Tianyang, MAO Dajun, HAN Wanli. Research on fault warning of coal mills based on DPC-MND and multivariate state estimation technique[J]. Control Engineering of China, 2024, 31(5):928-937.

袁存波,田亮.基于改进证据理论的中速磨煤机故障诊断[J].华北电力大学学报(自然科学版), 2022, 49(6):84-91.YUAN Cunbo, TIAN Liang. Fault diagnosis of medium speed coal mill based on improved evidence theory[J].Journal of North China Electric Power University(Natural Science), 2022, 49(6):84-91.

JIANG X, ZHENG J, ZHUANG X, et al. Ensemble data augmentation for imbalanced fault diagnosis[J]. IEEE Transactions on Instrumentation and Measurement, 2023,72:1-12.

赵小强,李森.不平衡数据下的轻量化轴承故障诊断方法[J].电子测量与仪器学报, 2024, 38(10):244-254.ZHAO Xiaoqiang, LI Sen. Fault diagnosis method for lightweight bearings under unbalanced data[J]. Journal of Electronic Measurement and Instrumentation, 2024,38(10):244-254.

毕凤荣,郭明智,毕晓阳,等.数据不平衡情况下的柴油机故障诊断方法[J].天津大学学报(自然科学与工程技术版), 2024, 57(8):810-820.BI Fengrong, GUO Mingzhi, BI Xiaoyang, et al. Diesel engine fault diagnosis method for imbalanced data[J].Journal of Tianjin University(Science and Technology),2024, 57(8):810-820.

YUE Y, HAMID R K, LEN G, et al. MSIFT:a novel endto-end mechanical fault diagnosis framework under limited&imbalanced data using multi-source information fusion[J]. Expert Systems with Applications, 2025, 274:126947.

李川,伍依凡,杨帅.不平衡分布的数据驱动故障诊断的研究进展[J].仪器仪表学报, 2023, 44(8):181-197.LI Chuan, WU Yifan, YANG Shuai. Research progress of fault diagnostics driven by imbalanced data distribution[J]. Chinese Journal of Scientific Instrument,2023, 44(8):181-197.

WANG C J, XU Z L. An intelligent fault diagnosis model based on deep neural network for few-shot fault diagnosis[J]. Neurocomputing, 2021, 456:550-562.

ZHU J, JIANG Q, SHEN Y, et al. Application of recurrent neural network to mechanical fault diagnosis:a review[J].Journal of Mechanical Science and Technology, 2022,36(2):527-542.

SABERI A N, BELAHCEN A, SOBRA J, et al.LightGBM-based fault diagnosis of rotating machinery under changing working conditions using modified recursive feature elimination[J]. IEEE Access, 2022, 10:81910-81925.

LIANG Z, ZHANG L, WANG X. A novel intelligent method for fault diagnosis of steam turbines based on TSNE and XGBoost[J]. Algorithms, 2023, 16(2):98.

王晓霞,李雷孝,林浩. SMOTE类算法研究综述[J].计算机科学与探索, 2024, 18(5):1135-1159.WANG Xiaoxia, LI Leixiao, LIN Hao. Survey of research on SMOTE type algorithms[J]. Journal of Frontiers of Computer Science and Technology, 2024, 18(5):1135-1159.

DEY I, PRATAP V. A comparative study of SMOTE,borderline-SMOTE, and ADASYN oversampling techniques using different classifiers[C]//2023 3rd international conference on smart data intelligence(ICSMDI), IEEE, 2023:294-302.

周萱,吴伟丽.基于改进SMOTE不均衡样本处理和IHPO-DBN的变压器故障诊断方法研究[J].电力系统保护与控制, 2024, 52(11):21-30.ZHOU Xuan, WU Weili. Transformer fault diagnosis method based on improved SMOTE unbalanced sample processing and IHPO-DBN[J]. Power System Protection and Control, 2024, 52(11):21-30.

刘云鹏,和家慧,许自强,等.基于SVM SMOTE的电力变压器故障样本均衡化方法[J].高电压技术, 2020,46(7):2522-2529.LIU Yunpeng, HE Jiahui, XU Ziqiang, et al. Equalization method of power transformer fault sample based on SVM SMOTE[J]. High Voltage Engineering, 2020, 46(7):2522-2529.

HOU Y, YANG A, GUO W, et al. Bearing fault diagnosis under small data set condition:a Bayesian network method with transfer learning for parameter estimation[J].IEEE Access, 2022, 10:35768-35783.

郑伟,姚远,刘乐源,等.基于贝叶斯网络的制粉系统安全控制方法[J].控制工程, 2023, 30(3):560-569.ZHENG Wei, YAO Yuan, LIU Leyuan, et al. Safety operation control for the coal pulverizing system based on Bayesian network[J]. Control Engineering of China, 2023,30(3):560-569.

MINJIE F, HAIDONG S, MINGHUI S, et al. Utilizing Bayesian generalization network for reliable fault diagnosis of machinery with limited data[J]. KnowledgeBased Systems, 2024, 305:112628.

DOOYOUL L, INU L, YOUNGCHAN K, et al. Sensor set optimization by functional model and Bayesian network for fault diagnosis of turbine generator lubrication system[J]. Engineering Applications of Artificial Intelligence, 2024, 138(B):109416.

张栋良,汪刘峰,洪勤勤,等.基于模糊贝叶斯网络的汽轮机组故障诊断研究[J].计算机仿真, 2022, 39(7):476-481.ZHANG Dongliang, WANG Liufeng, HONG Qinqin,et al. Research on fault diagnosis of turbo-generator set based on fuzzy Bayesian network[J]. Computer Simulation, 2022, 39(7):476-481.

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