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This research discusses the design and development of a computational intelligent based diagnostic system to assess the operating state of a process control valve. Process control valves react to a controller signal and are the main source of faults in a control loop. The elasticity inherent within a valve’s mechanical construction makes it prone to nonlinearities such as backlash, hysteresis and stiction. These nonlinearities negatively affect the performance of a process control loop during a control session. The diagnostic system proposed in this research utilises artificial neural network systems configured as ensembles to classify common control valve faults. Each ensemble functions as a ‘specialist’ trained to identify a specific loop fault. The team of specialized artificial neural networks are configured into a single comprehensive system to detect common control loops problems such as valve hysteresis, backlash, stiction and low air supply. The detection of a specific type of fault is achieved by comparing the mean square error output from each network. The ensemble having the lowest mean square error is the network that has been trained to identify a specific type of fault. Two practical methods to simulate control valve stiction and hysteresis are also presented in this study. These methods make it possible for researchers to investigate dynamics of nonlinear behaviour when these nonlinear effects occur in the control channel.

Design and development of a process control valve diagnostic system based on artificial neural network ensembles