Fault Diagnosis Methods and their Applications in the Process industry

Abstract

Global competition is forcing the process industry to optimize the production processes. One key factor in optimization is effective process state monitoring and fault diagnosis. Another motivator to improve process monitoring systems is the substantial losses of revenue resulting from abnormal process conditions. In recent years there has been an increasing interest in applying different process monitoring and fault diagnosis systems and as a result a large number of successful applications have been reported. In this paper the model and data-based FDD methods and their applications in the process industry are reviewed and discussed. The paper ends with the large scale case studies from the mineral processing, oil refining and pulp and paper industry.