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Product quality, operation safety, material and energy consumption, and thus the financial performance of industrial plants are directly or indirectly linked to the performance of control systems. Since process control systems are very complex, usually comprising different hierarchy levels, it is hopeless to maintain them on regular basis by plant personal. This is also the main reason why a large portion of industrial control loops has significant performance problems, as found out by audits carried out regularly since the 1990s. All these factors have contributed to the growing of the control performance monitoring (CPM) technology and applications in the last decade. CPM provides a framework for automatically and systematically assessing the performance of control loops, detecting and diagnosing root-causes of poor performance, as well as suggesting measures to improve control performance or avoid performance degradation. For this purpose, CPM techniques should be non-invasive, i.e., based on only routine operating data with limited or no additional process knowledge and without the need for any experimentation with the plant. This thesis deals with the complete CPM technology, from controller assessment (minimum-variance-control-based and advanced methods), over detection and diagnosis of control loop problems (process non-linearities, oscillations, actuator faults), to the improvement of control performance (maintenance, re-design of loop components, automatic controller re-tuning). It provides a contribution towards the development and application of completely self-contained and automatic methodologies in the field. Moreover, within this work, many CPM tools have been developed that goes far beyond available CPM packages. Industrial data from a large number of control loops in different industrial fields (building, chemicals, mining, mineral and metal processing) have been used to demonstrate the presented strategies and methods. Systematic procedures for automatic and continuous control performance monitoring, maintenance and optimisation are recommended, combining different control performance metrics and assessment, diagnosis and improvement methods. The main objective is to sustain top control performance during the whole life cycle of the control system, despite different and changing operational conditions.

Control System Performance Monitoring