大型振动筛DZK2466侧帮裂纹故障诊断系统研究
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摘要
本文系统研究了大型振动筛侧帮裂纹故障诊断问题。以平煤集团田庄选煤厂大型直线振动筛DZK2466为研究对象,根据现场实际生产情况和设备维护需求,对其侧帮裂纹进行了等级划分,通过现场跟踪和检测,得到了不同裂纹等级状态下的侧帮振动数据。提出了基于AR模型、主元分析和支持向量机的振动筛侧帮裂纹小样本故障诊断方法。研究了小波和小波包分析理论,对检测信号进行了小波降噪处理和小波包能量故障特征提取；研究了神经网络和遗传算法理论,建立了小波遗传神经网络,实现了对振动筛侧帮裂纹快速、准确的诊断。最后,提出了一种基于数据库的振动筛故障在线智能诊断方法,利用数据库、VB和Matlab软件对检测信息进行在线处理、调用、存储和分析,界面友好,操作方便,成功实现了对DZK2466侧帮裂纹的在线诊断和预警,尤其是对早期疲劳裂纹的准确预警对振动筛安全运行有着非常积极的意义。
This dissertation systematically studies the side panel crack fault diagnosis of large scale vibrating screen. It takes the large scale linear vibrating screen DZK2466of Tianzhuang Coal Preparation Plant of Pingdingshan Coal Mining Group Company as research object. The side panel cracks are divided into four levels by requirements of field actual production and equipment maintenance. The side panel vibrating data at different crack level state has been obtained by field tracking and detection. The side panel crack fault diagnosis method with small samples of vibrating screen based on AR model, PCA and SVM theory is proposed. The author studied wavelet and wavelet packet analysis theory and implemented wavelet de-noising and wavelet packet energy feature extraction. Neural network and genetic algrithoms are studied and the wavelet genetic neural network is established which realizes quick and accurate diagnosis of fault. Finally, the dissertation proposes an online intelligent fault diagnosis method of vibrating screen based on database. This kind of system processes, calls, stores and analyzes the online information using database, VB and Matlab software with friendly interfaces and easy operation. It implements the online diagnosis and accurate early warning to side panel of DZK2466, especially the accurate early warning of fatigue crack has very positive significance to safety running of vibrating screen equipment.
This dissertation systematically studies the side panel crack fault diagnosis of large scale vibrating screen. It takes the large scale linear vibrating screen DZK2466of Tianzhuang Coal Preparation Plant of Pingdingshan Coal Mining Group Company as research object. The side panel cracks are divided into four levels by requirements of field actual production and equipment maintenance. The side panel vibrating data at different crack level state has been obtained by field tracking and detection. The side panel crack fault diagnosis method with small samples of vibrating screen based on AR model, PCA and SVM theory is proposed. The author studied wavelet and wavelet packet analysis theory and implemented wavelet de-noising and wavelet packet energy feature extraction. Neural network and genetic algrithoms are studied and the wavelet genetic neural network is established which realizes quick and accurate diagnosis of fault. Finally, the dissertation proposes an online intelligent fault diagnosis method of vibrating screen based on database. This kind of system processes, calls, stores and analyzes the online information using database, VB and Matlab software with friendly interfaces and easy operation. It implements the online diagnosis and accurate early warning to side panel of DZK2466, especially the accurate early warning of fatigue crack has very positive significance to safety running of vibrating screen equipment.
引文
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14.李鹤,杨周,张义民,等．基于径向基神经网络预测的混沌时间序列嵌入维数估计方法[J].物理学报,2011,60(7)：1-6.
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17.赵玉成,蔡国平,许庆余,等.SXG1500×3700型振动筛故障诊断[J].矿山机械,1998(10)：54-56.
18.刘建文．直线振动筛筛框模态分析和故障诊断研究[J].矿山机械,2004(7):41-44.
19.刘建文．直线振动筛状态监测和技术改造[J].煤炭工程,2004(2):12-15.
20. Steyn, Jacques. Fatigue failure of deck support beams on a vibrating screen[J]. International Journal of Pressure Vessels and Piping,1995,61 (2):315-327.
21.虞和济,陈长征,张省等．基于神经网络的智能诊断[M].北京：冶金工业出版社,2000:104-132．
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