Fault diagnosis in industrial chemical processes using interpretable patterns based on Logical Analysis of Data

This paper applies the Logical Analysis of Data (LAD) to detect and diagnose faults in industrial chemical processes. This machine learning classification technique discovers hidden knowledge in industrial datasets by revealing interpretable patterns, which are linked to underlying physical phenomena. The patterns are then combined to build a decision model that serves to diagnose faults during the process operation, and to explain the potential causes of these faults. LAD is applied to two case studies, selected to exemplify the difficulty in interpreting faults in complex chemical processes. The first case study is the Tennessee Eastman Process (TEP), a well-known benchmark problem in the field of process monitoring and control that uses simulated data. The second one uses a real dataset from a black liquor recovery boiler in a pulp mill. The results are compared to those obtained by other common machine learning techniques, namely artificial neural networks (ANN), Decision Tree (DT), Random Forest (RF), k nearest neighbors (kNN), quadratic discriminant analysis (QDA) and support vector machine (SVM). In addition to its explanatory power, the results show that LAD's performance is comparable to the most accurate techniques.