基于多特征融合的工业气动调节阀快速自学习故障诊断方法
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中国矿业大学 信息与控制工程学院,江苏 徐州 221116

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E-mail: weidai@cumt.edu.cn.

中图分类号:

TP206.3

基金项目:

国家自然科学基金面上项目(61973306);江苏省自然科学基金项目(BK20200086);江苏省研究生科研与实践创新计划项目(SJCX22_1128);中国矿业大学研究生创新计划项目(2022WLJCRCZL097).


Fast self-learning fault diagnosis method for industrial pneumatic control valves based on multi-feature fusion
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School of Information and Control Engineering,China University of Mining and Technology,Xuzhou 221116,China

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    摘要:

    气动调节阀的复杂特性,使得通过建立精确数学模型来描述阀门故障较为困难,因而数据驱动技术在其故障诊断领域颇受关注.但现有商业化的调节阀其控制系统仅配置了相当有限的硬件设备,这对故障诊断模型和学习效率提出了更高的要求.为此,提出一种基于多特征融合的气动调节阀快速自学习故障诊断方法.首先,提出基于云模型(cloud model,CM)和动态内部主元分析(dynamic-inner principal component analysis,DiPCA)的特征信息融合方法,提高诊断模型的输入信息质量;其次,建立一种低差异随机配置网络,按照低差异序列以监督增量方式快速自主构造调节阀诊断模型,从而有效提高模型的学习效率和紧致性;最后,利用DAMADICS平台的实验数据验证所提出方法的快速性和准确性.

    Abstract:

    The complex characteristics of pneumatic control valves make it difficulty to describe valve faults by establishing a accurate mathematical model, data-driven technology thus attracts widespread attention in the filed of its fault diagnosis. The existing control systems of commercial regulating valves, however, are always equipped with limited hardware equipment, which puts forward higher requirements for the fault diagnosis model and learning efficiency. Therefore, this paper presents a fast self-learning fault diagnosis method for pneumatic control valves based on multi-feature fusion. Firstly, by integratding the cloud model(CM) and dynamic-inner principal component analysis(DiPCA), a fault feature fusion method of pneumatic control valves is proposed to improve the quality of input information for the diagnosis model. Then, a low discrepancy stochastic configuration network is established to construct the diagnosis model quickly and autonomously in a supervised incremental manner according to the low discrepancy sequence, effectively improving the learning efficiency and compactness of the model. Finally, experimental data from the DAMADICS platform are employed to verify the rapidity and accuracy of the proposed method.

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代伟,黄金昊,王聪,等.基于多特征融合的工业气动调节阀快速自学习故障诊断方法[J].控制与决策,2023,38(10):2934-2942

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  • 在线发布日期: 2023-09-19
  • 出版日期: 2023-10-20
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