Evolving Fuzzy Model for Fault Detection and Fault Identification of Dynamic Processes

Abstract

This chapter presents an evolving fuzzy method based on data clouds for fault detection and identification of dynamic processes. The method calculates the local density of the data using the recursive Mahalanobis distance through recursive calculation of the inverse of the covariance matrix. The local density is actually a measure which determines the closeness and the membership degree of the data to the existing data clouds. The structure of the fuzzy model evolves in an online manner and it is capable to incorporate new knowledge in the model. The learning procedure of the model starts with known/labeled data for normal process operation and for faults. Using this knowledge, the method/classifier is capable of identifying the same operation mode the next time it appears. The efficiency of the method is tested on a model of HVAC (Heating, Ventilation, and Air Conditioning) system. The structure of the model was designed directly from a real process, and the model’s parameters were tuned to cope with the dynamics of the real system. Therefore, the model represents the actual behavior of the real system. Besides the overall accuracy of the method, we also tested the efficiency in a manner of true positive and false positive rate. The results were compared to the established statistical fault detection method DPCA (Dynamic Principle Component Analysis).