On-line fault diagnosis for optimal rice α-amylase production process of a temperature-sensitive mutant of Saccharomyces cerevisiae by an autoassociative neural network

Abstract

A nonlinear statistical approach to data analysis, an autoassociative neural network (AANN), was applied to fault diagnosis in the optimal production process of a recombinant yeast with a temperature controllable expression system. High frequency noise in the data could be eliminated by a wavelet transform 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 same faults were not detected by linear principal component analysis (PCA). By implementing corrective action after fault detection, the final production amount was increased to twice the amount it would have been without diagnosis.