Reconstruction-based fault prognosis for flue gas turbines with independent component analysis

Online detection and prognosis are very important for the safe operation of flue gas turbines. Compared with univariate monitoring of the process, multivariate process monitoring is more effective and can capture abnormal situation in the early stage. This paper proposes a new multivariate fault prognosis framework for the flue gas turbine with a hidden fault process based on independent component analysis (ICA). ICA is a statistical method for identifying underlying independent factors or components in multivariate data. First of all, the non-Gaussian measurements are modeled by the ICA model, and three indices are used for fault detection. Once several faulty samples are collected, fault directions can be extracted for the reconstruction. Then, a reconstruction-based method is proposed to estimate the fault magnitude with the most sensitive index. At last, the fault magnitude is predicted by the support vector machine model. Test results for K-103 catalytic flue gas turbine clearly showed that the proposed fault prognosis method is more efficient than traditional methods. The results also demonstrate support vector machine method has advantages over auto regression approach. © 2013 Curtin University of Technology and John Wiley & Sons, Ltd.