结合改进Fisher判别分析和显著故障变量提取的卷烟制叶丝段故障诊断方法

Fault diagnosis method for tobacco strip processing by integrating variant Fisher discriminant analysis and significant fault variable extraction

针对卷烟制叶丝段因过程变量间相关性强而导致故障误诊断率高等问题,提出一种结合改进Fisher判别分析和显著故障变量提取的制叶丝段故障诊断方法。设计了一种具有正交判别成分的改进Fisher判别分析方法,通过两步特征提取避免了类内散布矩阵奇异性问题,并通过数据紧缩保证了判别成分间的垂直性。利用改进Fisher判别分析方法提取故障方向,沿故障方向通过贡献度分析度量不同变量对故障影响,获得对故障有重要影响的故障变量和没有影响的一般变量,分别建立故障变量和一般变量故障诊断模型,实现设备在线故障诊断。基于制叶丝段设备的实际运行数据进行实验验证,结果表明:与典型贡献图故障诊断方法相比,该方法有助于对故障过程和特性的深入理解,通过对显著故障变量分析与提取,克服了非关键故障诊断信息的影响,能够及时准确分离出引发故障的变量,有效提高卷烟设备故障诊断的可靠性。该方法为制丝生产设备异常状态的精确诊断提供了理论支撑。

In view of high misdiagnosis rate caused by the strong correlation between process variables in tobacco strip processing, a fault diagnosis method was proposed, which combined variant Fisher discriminant analysis （VFDA） with significant fault variable extraction. The VFDA contains orthogonal discriminant components, it prevents the singularity of intra-category scatter matrix by a two-step feature extraction and protects the verticality between discriminant components via data deflation. VFDA is used for extracting fault direction. The contribution of each variable to the faults was measured along the fault directions, those variables which influenced the faults heavily were referred to as fault variables, and those variables which did not affected the faults were referred to as general variables. Fault diagnosis models for fault variables and general variables were established separately for online equipment fault diagnosis. Off-line validation was conducted based on actual running data of tobacco strip p^oeessing equipments, the results showed that comparing with typical contribution plot fault diagnosis method, the proposed method was helpful to intensive understanding of the process and characteristics of faults and eliminating the influences of minor information via significant fault variable analysis and extraction. The variables causing faults were isolated timely and accurately, and the reliability of fault diagnosis of cigarette manufacturing equipments was effectively promoted. The proposed method provides theoretical support for the precise diagnosis of the equipment while it is not properly functioning.