8. Multimode Process Monitoring: Part 1

Multimode process monitoring has recently received much attention. Conventional methods either assume the process data is Gaussian in each operation mode, or some process knowledge should be incorporated, which makes the methods supervised. In this chapter, a new unsupervised method is introduced for multimode process monitoring, which is based on Bayesian inference and two-step independent component analysis-principal component analysis (ICA-PCA) feature extraction strategy. ICA-PCA is first introduced for feature extraction and dimension reduction. By transferring the traditional monitoring statistic to fault probability in each operation mode, monitoring results in different operation modes can be easily combined through the Bayesian inference. After that, a fault identification method can be developed upon this monitoring framework. Through analyses of the posterior probability and the joint probability for the monitored data sample, the correct operation mode or fault scenario can both be identified.