Fault Diagnosis Using HMMs

Hidden Markov models (HMMs) provide a powerful framework for recognizing patterns in data and diagnosing process faults as shown in the previous sections. Here, another procedure is introduced that is based on the state estimation problem (see Section 6.4.2). The procedure determines first 

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Fault Diagnosis Using Triangular Episodes and HMMs... 

Fault Diagnosis Using Wavelet-Domain HMMs... 

Contribution plots presented in Section 7.4 provide an indirect approach to fault diagnosis by first determining process variables that have inflated the detection statistics. These variables are then related to equipment and disturbances. A direct approach would associate the trends in process data to faults explicitly. HMMs discussed in the first three sections of this chapter is one way of implementing this approach. Use of statistical discriminant analysis and classification techniques discussed in this section and in Section 7.6 provides alternative methods for implementing direct fault diagnosis. 

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