带式输送机系统故障诊断方法综述

杨春雨; 曹博仕; 张鑫; 姬明君

doi:10.13272/j.issn.1671-251x.2023030099

带式输送机系统故障诊断方法综述

doi: 10.13272/j.issn.1671-251x.2023030099

中国矿业大学信息与控制工程学院, 江苏徐州　221008

基金项目: 国家自然科学基金面上项目 (61873272)。

详细信息

作者简介:
杨春雨（1979—），男，辽宁凌源人，教授，博士，研究方向为多时间尺度系统自适应与自学习优化控制方法、多电动机系统最优协调控制、智能机器人导航与控制、基于机器学习的智能装备健康状态评价，E-mail：chunyuyang@cumt.edu.cn

中图分类号: TD634
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出版历程
- 收稿日期: 2023-03-31
- 修回日期: 2023-06-08
- 网络出版日期: 2023-06-19

Summary of fault diagnosis methods for belt conveyor systems

School of Information and Control Engineering, China University of Mining and Technology, Xuzhou 221008, China

摘要

摘要: 输送带和驱动装置是带式输送机的主要组成部分且为故障高发部位，以输送带故障和驱动装置故障为切入点，分析了输送带跑偏、打滑、损伤、堆料撒料等故障及驱动装置滚筒、托辊、减速器等故障的机理，重点阐述了知识驱动和数据驱动的带式输送机故障诊断方法研究进展。知识驱动法以知识处理技术为基础，实现符号处理和数值处理的统一、推理过程和算法过程的统一，主要包括专家系统、故障树分析法。数据驱动法采用机器学习和数据挖掘等技术对历史数据进行分析处理，建立诊断模型，达到故障诊断目的，主要包括支持向量机（SVM）、比差法、基于声音和视觉的诊断方法。分析了带式输送机故障诊断方法目前存在的挑战和未来发展趋势：① 结合历史故障数据和实时数据推断设备健康状况，预测早期微小故障，提醒工作人员进行预测性维护。② 揭示带式输送机耦合故障的关联关系，利用人工智能等新兴技术研究耦合故障联合诊断方法。③ 利用多模态机器学习技术研究带式输送机多模态信息融合利用机制，开发带式输送机多模态信息融合故障诊断方法。④ 将故障知识图谱和带式输送机领域知识相结合，实现带式输送机设备故障追踪、故障超前预警，通过知识查询、知识推理和辅助决策功能，提高故障处理、精准挖掘设备潜在故障风险的能力。
- 带式输送机 /
- 故障诊断 /
- 输送带故障 /
- 驱动装置故障 /
- 知识驱动 /
- 数据驱动
Abstract: The conveyor belt and driving device are the main components of the belt conveyor and are the high-risk areas for faults. Taking conveyor belt faults and driving device faults as the starting point, this paper analyzes the mechanisms of conveyor belt deviation, slipping, damage, stacking and scattering, as well as the faults of the driving device roller, idler and reducer. It focuses on the research progress of knowledge-driven and data-driven fault diagnosis methods for belt conveyors. Based on the knowledge processing technology, the knowledge-driven method realizes the unification of symbol processing and numerical processing, the unification of reasoning process and algorithm process. It mainly includes expert system and fault tree analysis. The data-driven method uses machine learning and data mining techniques to analyze and process historical data. It establishes diagnostic models, and achieves fault diagnosis purposes. It mainly includes support vector machines (SVM), comparison method, and diagnosis methods based on sound and vision. This paper analyzes the current challenges and future development trend of belt conveyor fault diagnosis methods. ① The historical fault data and real-time data should be combined to infer equipment health. The early minor faults should be predicted so as to remind the staff to carry out predictive maintenance. ② The correlation between coupling faults of belt conveyors should be revealed. The emerging technologies such as artificial intelligence should be used to study joint diagnosis methods for coupling faults. ③ The multimodal machine learning technology should be utilized to study the mechanism of multimodal information fusion and utilization of belt conveyors. Fault diagnosis methods for multimodal information fusion of belt conveyors needs to develop. ④ The fault knowledge graph and the belt conveyor domain knowledge should be combined to realize the belt conveyor equipment fault tracking and fault early warning. Through the knowledge query, knowledge reasoning and auxiliary decision-making functions, the capability of fault handling and precise mining of potential fault risks of equipment can be improved.
- belt conveyor /
- fault diagnosis /
- conveyor belt fault /
- driving device fault /
- knowledge-driven /
- data-driven

HTML全文

图 1 带式输送机结构

Figure 1. Structural of belt conveyor

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图 2 输送带跑偏故障

Figure 2. Deviation fault of conveyor belt

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图 3 输送带损伤故障

Figure 3. Damage fault of conveyor belt

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图 4 滚筒装置

Figure 4. Rollers

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图 5 托辊装置

Figure 5. Idlers

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图 6 减速器装置

Figure 6. Reducer

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图 7 带式输送机故障诊断流程

Figure 7. Fault diagnosis process of belt conveyor

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图 8 故障诊断专家系统

Figure 8. Expert system for fault diagnosis

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图 9 基于PCA和SVM的跑偏故障诊断流程

Figure 9. Diagnosis process of deviation fault based on principal component analysis and support vector machine

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图 10 基于声音的撕裂故障诊断流程

Figure 10. Diagnosis process of tear fault based on sound

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图 11 基于视觉的故障诊断流程

Figure 11. Visual based fault diagnosis process

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图 12 故障树分析法与专家系统相结合的故障诊断方法

Figure 12. Fault diagnosis method combining fault tree analysis with expert system

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图 13 基于音频的托辊故障诊断流程

Figure 13. Diagnosis process of idler fault based on audio

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表 1 各类故障诊断方法对比

Table 1. Comparison of fault diagnosis methods

类型	诊断方法	故障类型	优缺点
知识驱动	专家系统^[50-51]	跑偏、打滑、损伤、堆料撒料、滚筒故障	不需要数学模型，但知识库建立较难
知识驱动	故障树^[23,53,64]	跑偏、打滑、损伤、堆料撒料、滚筒故障	因果关系清晰明了，但复杂系统故障树异常复杂
数据驱动	时频域分析法^[5,66]	减速器故障、滚筒故障、托辊故障	计算简单快速，不需要滤波处理，且精度较高，但不能分析随时间变化的信号
	最小熵理论^[8,35]	滚筒故障	数据波动情况下精度较高，但易受噪声影响
	BP、卷积神经网络^[65,68]	减速器故障、托辊故障	准确度较高，但样本量直接决定模型精度
	小波包分解法^[67,69]	托辊故障、滚筒故障、减速器故障	可观察信号的局部特性，但冗余度较大
	SVM^[55,68]	托辊故障、损伤故障	鲁棒性好，但对于大容量样本，难以实现，运算量大
	比差法^[56]	打滑故障	简单直接，但是应用场景较少，且误差较大
	音频特征分析法^{[57-58,66-68]}	托辊故障、损伤故障	计算量较小，但易受外界噪声影响
	视觉信息分析法^[14-15]	跑偏故障、损伤故障、托辊故障	具有无损检测的优势，但计算量较大，易受外界因素影响

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