Fault detection and diagnosis in the Tennessee Eastman Process using interpretable knowledge discovery

This paper proposes an interpretable knowledge discovery approach to detect and diagnose faults in chemical processes. The approach is demonstrated using simulated data from the Tennessee Eastman Process (TEP), as a challenging benchmark problem. The TEP is a plant-wide industrial process that is commonly used to study and evaluate a variety of topics, including the design of process monitoring and control techniques. The proposed approach is called Logical Analysis of Data (LAD). LAD is a machine learning approach that is used to discover the hidden knowledge in historical data. The discovered knowledge in the form of extracted patterns is employed to construct a classification rule that is capable of characterizing the physical phenomena in the TEP, wherein one can detect and identify a fault and relate it to the causes that contribute to its occurrence. To evaluate our approach, the LAD is trained on a set of observations collected from different faults, and tested against an independent set of observations. The results in this paper show that the LAD approach achieves the highest accuracy compared to two common machine learning classification techniques; Artificial Neural Networks and Support Vector Machines.