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Development of an on-line fault diagnosis and operation system for an optimal rice-alphaamylase production process of temperature-sensitive mutant of Saccharomyces cerevisiae by autoassociative neural network

利用自联想神经网络开发酿酒酵母温度敏感突变体最佳水稻α淀粉酶生产过程的在线故障诊断和操作系统

基本信息

批准号：
08455381
负责人：
SHIOYA Suteaki
金额：
$ 4.99万
依托单位：
Osaka University
依托单位国家：
日本
项目类别：
Grant-in-Aid for Scientific Research (B)
财政年份：
1996
资助国家：
日本
起止时间：
1996 至 1997
项目状态：
已结题

项目摘要

A nonlinear multivariate analysis, artificial autoassociative neural network (AANN), was applied to bioprocess fault detection . In an optimal production process of a recombinant yeast with a temperature controllable expression system, faults in test cases of faulty temperature sensor and plasmid instability of recombinant cells could be detected by the AANN.Since the raw data of measured variables included high frequency noise, a wavelet filter bank (WFB) was applied noise elimination before training of the AANN.The filtering performance of the WFB was compared with those of some classical first order digital filters. The filtered signals at several resolution scales by the WFB were employed as the training data of the AANN.The computing time and summation of square of errors (SSE) in training were compared and appropriate degree of the noise filtering and the density of the training data of the AANN were discussed. High frequency noise in the data could be eliminated by the WFB before the fault diagnosis was performed. The diagnosis system could accurately and immediately detect the faults on-line in the test cases of a faulty temperature sensor and plasmid instability of the recombinant cells. The performance of the feature capturing by the AANN was compared with that by a linear multivariate analysis, principal component analysis (PCA). AJ index defined in this study, using inputs and outputs of the AANN was used for fault detection successfully. The same faults were not detected by linear principal component analysis (PCA). The output of the first unit of the trained AANN functioned effectively for the discrimination of the data in the abnormal cases from the data in the normal cases. By implementing corrective action after fault detection, the final production amount was increased to twice the amount it would have been without diagnosis.

将一种非线性多变量分析--人工自联想神经网络（AANN）应用于生物过程故障检测.在一个具有温度可控表达系统的重组酵母的优化生产过程中，AANN可以检测到故障温度传感器和重组细胞质粒不稳定性测试用例中的故障，由于测量变量的原始数据包含高频噪声，小波滤波器组（WFB）在训练神经网络前先进行消噪处理，并将WFB滤波器的滤波性能与经典的一阶数字滤波器进行了比较。将WFB滤波后的信号在不同分辨率下作为AANN的训练数据，比较了AANN训练的计算时间和误差平方和（SSE），讨论了AANN的噪声滤除程度和训练数据的密度。在进行故障诊断之前，WFB可以消除数据中的高频噪声。在温度传感器故障和重组细胞质粒不稳定的测试用例中，该诊断系统能够准确、及时地在线检测出故障。通过与线性多变量分析、主成分分析（PCA）的比较，分析了AANN的特征提取性能。本文定义的AJ指标，利用AANN的输入和输出，成功地用于故障检测。线性主成分分析（PCA）没有检测到相同的故障。训练后的AANN的第一单元的输出有效地区分了异常情况下的数据和正常情况下的数据。通过在故障检测后实施纠正措施，最终生产量增加到没有诊断时的两倍。