摘要
多段主元分析(MPCA)是针对间歇进行故障诊断一种行之有效的方法.在MPCA中主元个数的确定是模型的关键,关系到主元模型的可靠性、准确性、完备性.传统的累积方差贡献率(CPV)方法确定主元个数主观性较大并且没有考虑故障因素.为了提高检测性能,有效的提取主元,文中提出一种信噪比(SNR)与MPCA相结合选取间歇过程主元个数的方法,SNR表明的是故障诊断的灵敏度和主元个数的影响关系,在确保主元信息充分描述数据的基础上,该方法考虑了故障的信息对主元个数的影响来选取主元.将此方法应用于青霉素间歇发酵过程故障诊断中,仿真结果表明T2统计量和SPE统计量的响应曲线对故障更加敏感,有效地提高了故障诊断的准确率.
Multi-way principal component analysis （MPCA） is an effective method for fault diagnosis in batch processes In MPCA, the determination of principal component numbers（PCs） is the key to the model, which concerns the reliability, accuracy, completeness of PCA model. The traditional method, using CPV to determine PCs, is too subjective and does not consider the failure factors. In order to improve the detection performance, and effectively extract principal component, this paper proposes a method that is combing SNR with MPCA to select PCs in batch process, SNR indicates the relationship between the sensitivity of fault diagnosis and PCs. On the basis that the principal information fully describes the data ,and considering the influence of fault information on CPs, then it selects principal component. Applying this method to fault diagnosis in penicillin batch fermentation process, the simulation results show that the response curves of T^2 statistics and SPE statistics are more sensitive to fault, which effectively improves the accuracy rate of fault diagnosis.
出处
《计算机系统应用》
2015年第2期200-205,共6页
Computer Systems & Applications
基金
国家自然科学基金(61273131)
江苏省产学研联合创新资金(BY2013015-39)
