Chemical plant fault diagnosis through a hybrid symbolic-connectionist approach and comparison with neural networks

A novel hybrid symbolic-connectionist approach to machine learning is illustrated for fault diagnosis of a hydrocarbon chlorination plant. The learning algorithm addressed the knowledge acquisition problem by developing and maintaining the knowledge base through inductive learning. The performance of the learning system is discussed in terms of the knowledge extracted from example cases and its classification accuracy on the test cases. Results indicate that the introduced system is a promising alternative to neural networks for fault diagnosis and a complement to expert systems.