基于改进麻雀搜索优化置信规则库的工业机器人故障诊断方法
Industrial Robot Fault Diagnosis Method Based on Refined Sparrow Search Algorithm Optimized Belief Rule Base
DOI: 10.12677/csa.2026.1611326, PDF,    科研立项经费支持
作者: 黄建青, 马 宁*：哈尔滨师范大学计算机科学与信息工程学院，黑龙江 哈尔滨
关键词: 工业机器人；故障诊断；置信规则库；证据推理；麻雀搜索算法；Industrial Robot； Fault Diagnosis； Belief Rule Base； Evidential Reasoning； Sparrow Search Algorithm
摘要: 针对工业机器人故障诊断问题，提出一种基于改进麻雀搜索优化置信规则库的工业机器人故障诊断方法(Refined Sparrow Search Algorithm Optimized Belief Rule Base, RSSA-BRB)。首先，结合先验知识建立了工业机器人故障诊断模型；其次，设计了改进麻雀搜索算法对模型参数进行优化，为提高优化精度，避免算法陷入局部最优，采用混沌映射初始化种群、自适应惯性权重位置更新、高斯变异最优个体策略对麻雀搜索算法进行了改进；最后，通过对工业机器人故障诊断案例进行研究，验证了本文方法在工业机器人故障诊断中的有效性。对比研究表明，本文方法在诊断精度和收敛速度方面具有一定的优势。
Abstract: Addressing the issue of industrial robot fault diagnosis, a method based on a refined Sparrow Search Algorithm optimized belief rule base (RSSA-BRB) is proposed. Firstly, an industrial robot fault diagnosis model is established by combining prior knowledge. Secondly, an improved Sparrow Search Algorithm is designed to optimize the model parameters. To enhance optimization accuracy and prevent the algorithm from falling into local optima, the Sparrow Search Algorithm is improved by using chaos mapping to initialize the population, adaptive inertia weight position updating, and Gaussian mutation for the optimal individual strategy. Finally, through research on industrial robot fault diagnosis cases, the effectiveness of the proposed method in industrial robot fault diagnosis is verified. Comparative studies show that the proposed method has certain advantages in terms of diagnosis accuracy and convergence speed.
文章引用：黄建青, 马宁. 基于改进麻雀搜索优化置信规则库的工业机器人故障诊断方法[J]. 计算机科学与应用, 2026, 16(11): 60-75. https://doi.org/10.12677/csa.2026.1611326

参考文献

[1] 赵杰. 我国工业机器人发展现状与面临的挑战[J]. 航空制造技术, 2012, 55(12): 26-29.
[2] 雷亚国, 贾峰, 孔德同, 等. 大数据下机械智能故障诊断的机遇与挑战[J]. 机械工程学报, 2018, 54(5): 94-104.
[3] 刘亚军, 訾斌, 王正雨, 等. 智能喷涂机器人关键技术研究现状及进展[J]. 机械工程学报, 2022, 58(7): 53-74.
[4] Sabry, A.H. and Ungku Amirulddin, U.A.B. (2024) A Review on Fault Detection and Diagnosis of Industrial Robots and Multi-Axis Machines. Results in Engineering, 23, Article 102397.
https://doi.org/10.1016/j.rineng.2024.102397
[5] Raouf, I., Kumar, P. and Soo Kim, H. (2024) Deep Learning-Based Fault Diagnosis of Servo Motor Bearing Using the Attention-Guided Feature Aggregation Network. Expert Systems with Applications, 258, Article 125137.
https://doi.org/10.1016/j.eswa.2024.125137
[6] Vu, M., Nguyen, V., Tran, T., Pham, V. and Lo, M. (2024) Few-Shot Bearing Fault Diagnosis via Ensembling Transformer-Based Model with Mahalanobis Distance Metric Learning from Multiscale Features. IEEE Transactions on Instrumentation and Measurement, 73, 1-18.
https://doi.org/10.1109/tim.2024.3381270
[7] Yang, B., Huang, Y., Jiao, J., Xu, W., Liu, L., Xie, K., et al. (2024) Multidomain Neural Process Model Based on Source Attention for Industrial Robot Anomaly Detection. Advanced Engineering Informatics, 62, Article 102910.
https://doi.org/10.1016/j.aei.2024.102910
[8] Chen, C., Wang, T., Liu, C., Liu, Y. and Cheng, L. (2023) Lightweight Convolutional Transformers Enhanced Meta-Learning for Compound Fault Diagnosis of Industrial Robot. IEEE Transactions on Instrumentation and Measurement, 72, 1-12.
https://doi.org/10.1109/tim.2023.3277956
[9] Chen, Y., Yang, J., Xu, D. and Yang, S. (2013) On the Inference and Approximation Properties of Belief Rule Based Systems. Information Sciences, 234, 121-135.
https://doi.org/10.1016/j.ins.2013.01.022
[10] Yang, J.B., Liu, J., Wang, J., et al. (2006) Belief Rule-Base Inference Methodology Using the Evidential Reasoning Approach-Rimer. IEEE Transactions on Systems, Man, and Cybernetics—Part A: Systems and Humans, 36, 266-285.
https://doi.org/10.1109/tsmca.2005.851270
[11] Li, J., Yang, F., He, W., et al. (2025) An Online Ultrasonic Flowmeter Fault Diagnosis Method Based on an Extended Belief Rule Base. KSII Transactions on Internet & Information Systems, 19, 123-148.
[12] Zhao, B., Kong, L., He, W., Zhou, G. and Zhu, H. (2025) A Fault Diagnosis Method for Manufacturing System Based on Adaptive BRB Considering Environmental Disturbance. International Journal of Fuzzy Systems, 27, 1038-1051.
https://doi.org/10.1007/s40815-024-01799-9
[13] Wan, H., Liu, M., Zhu, H., Ma, N. and He, W. (2024) An Interpretable Bearing Fault Diagnosis Method Based on Belief Rule Base with Interval Structured. IEEE Access, 12, 116939-116955.
https://doi.org/10.1109/access.2024.3435057
[14] Gao, Z., He, M., Zhang, X., Hu, G., He, W. and Chen, S. (2024) Fault Diagnosis of High-Speed Train Motors Based on a Multidimensional Belief Rule Base. IEEE Access, 12, 122544-122556.
https://doi.org/10.1109/access.2024.3452641
[15] Mai, J., Luo, Z., Ge, G., Zhang, G. and He, W. (2024) A Fault Diagnosis Method for Autonomous Underwater Vehicles Based on Online Extended Belief Rule Base with Dynamic Rule Reduction. IEEE Access, 12, 165407-165424.
https://doi.org/10.1109/access.2024.3487841
[16] Zhou, Z., Cao, Y., Hu, G., Zhang, Y., Tang, S. and Chen, Y. (2021) New Health-State Assessment Model Based on Belief Rule Base with Interpretability. Science China Information Sciences, 64, Article 172214.
https://doi.org/10.1007/s11432-020-3001-7
[17] Feng, Z., Zhou, Z., Hu, C., Chang, L., Hu, G. and Zhao, F. (2018) A New Belief Rule Base Model with Attribute Reliability. IEEE Transactions on Fuzzy Systems, 27, 903-916.
https://doi.org/10.1109/tfuzz.2018.2878196
[18] Si, X.S., Hu, C.H., Yang, J.B., et al. (2011) A New Prediction Model Based on Belief Rule Base for System’s Behavior Prediction. IEEE Transactions on Fuzzy Systems, 19, 636-651.
https://doi.org/10.1109/tfuzz.2011.2130527
[19] Jiao, L., Zhang, H., Geng, X. and Pan, Q. (2024) Belief Rule Learning and Reasoning for Classification Based on Fuzzy Belief Decision Tree. International Journal of Approximate Reasoning, 175, Article 109300.
https://doi.org/10.1016/j.ijar.2024.109300
[20] Xue, J. and Shen, B. (2020) A Novel Swarm Intelligence Optimization Approach: Sparrow Search Algorithm. Systems Science & Control Engineering, 8, 22-34.
https://doi.org/10.1080/21642583.2019.1708830
[21] Ren, J., Wei, H., Yuan, Y., Li, X., Luo, F. and Wu, Z. (2022) Boosting Sparrow Search Algorithm for Multi-Strategy-Assist Engineering Optimization Problems. AIP Advances, 12, Article 095201.
https://doi.org/10.1063/5.0108340
[22] Xu, K., Chen, Y., Zhang, X., Ge, Y., Zhang, X., Li, L., et al. (2024) Improved Sparrow Search Algorithm Based on Multistrategy Collaborative Optimization Performance and Path Planning Applications. Processes, 12, Article 2775.
https://doi.org/10.3390/pr12122775
[23] Wang, Z., Peng, Q., Rao, W. and Li, D. (2025) An Improved Sparrow Search Algorithm with Multi-Strategy Integration. Scientific Reports, 15, Article No. 3314.
https://doi.org/10.1038/s41598-025-86298-z