Fault Diagnosis Using Triangular Episodes and HMMs

1 Fault Diagnosis Using Triangular Episodes and HMMs 

The first step of the analysis is the training where Hidden Markov Models (HMMs) representing various operating behaviors are trained using labeled historical data from the process. In this section, three broad operat- 

Once the relevant HMMs are trained, the trend analysis is carried out on the newly observed time series in real-time. The time series is windowed and smoothed before the signal in the window can be represented in the 

The method will be illustrated by two case studies next. 

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