Safety in industrial process and production plants is a concern of rising importance, especially if people would be endangered by a catastrophic system failure. On the other hand, because the control devices, which are now exploited to improve the overall performance of industrial processes, include both sophisticated digital system design techniques and complex hardware (input–output sensors, actuators, components and processing units), there is an increased probability of failure. As a direct consequence of this, control systems must include automatic supervision of closed-loop operation to detect and isolate malfunctions as early as possible. One of the most promising methods for solving this problem is the ”analytical redundancy” approach, in which residual signals are obtained. The basic idea consists of using an accurate model of the system to mimic the real process behaviour. If a fault occurs, the residual signal, i.e., the difference between real system and model behaviours, can be used to diagnose and isolate the malfunction. This paper is focussed on model identification oriented to the analytical approach of fault diagnosis and identification. The problem is treated in all its aspects covering the choice of model structure, the parameter identification methods, the residual generation techniques, and the fault diagnosis and isolation strategies. A sample case study will be described in order to demonstrate the application of these comprehensive identification and fault diagnosis techniques.
Model-Based Fault Diagnosis for Dynamic Processes Using Identification Techniques
SIMANI, Silvio
2011
Abstract
Safety in industrial process and production plants is a concern of rising importance, especially if people would be endangered by a catastrophic system failure. On the other hand, because the control devices, which are now exploited to improve the overall performance of industrial processes, include both sophisticated digital system design techniques and complex hardware (input–output sensors, actuators, components and processing units), there is an increased probability of failure. As a direct consequence of this, control systems must include automatic supervision of closed-loop operation to detect and isolate malfunctions as early as possible. One of the most promising methods for solving this problem is the ”analytical redundancy” approach, in which residual signals are obtained. The basic idea consists of using an accurate model of the system to mimic the real process behaviour. If a fault occurs, the residual signal, i.e., the difference between real system and model behaviours, can be used to diagnose and isolate the malfunction. This paper is focussed on model identification oriented to the analytical approach of fault diagnosis and identification. The problem is treated in all its aspects covering the choice of model structure, the parameter identification methods, the residual generation techniques, and the fault diagnosis and isolation strategies. A sample case study will be described in order to demonstrate the application of these comprehensive identification and fault diagnosis techniques.I documenti in SFERA sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
