Abstract
There have been many studies on observer-based fault detection and isolation (FDI), such as using unknown input observer and generalized observer. Most of them require a nominal mathematical model of the system. Unlike sensor faults, actuator faults and process faults greatly affect the system dynamics. This paper presents a new process fault diagnosis technique without exact knowledge of the plant model via Extended State Observer (ESO) and soft computing. The ESO’s augmented or extended state is used to compute the system dynamics in real time, thereby provides foundation for real-time process fault detection. Based on the input and output data, the ESO identifies the un-modeled or incorrectly modeled dynamics combined with unknown external disturbances in real time and provides vital information for detecting faults with only partial information of the plant, which cannot be easily accomplished with any existing methods. Another advantage of the ESO is its simplicity in tuning only a single parameter. Without the knowledge of the exact plant model, fuzzy inference was developed to isolate faults. A strongly coupled three-tank nonlinear dynamic system was chosen as a case study. In a typical dynamic system, a process fault such as pipe blockage is likely incipient, which requires degree of fault identification at all time. Neural networks were trained to identify faults and also instantly determine degree of fault. The simulation results indicate that the proposed FDI technique effectively detected and isolated faults and also accurately determine the degree of fault. Soft computing (i.e. fuzzy logic and neural networks) makes fault diagnosis intelligent and fast because it provides intuitive logic to the system and real-time input-output mapping.
Highlights
The main function of an observer, known as estimator, is to extract information of the otherwise immeasurable variables for a vast number of applications that include feedback controls and system health monitoring or fault diagnosis
Based on the input and output data, the Extended State Observer (ESO) identifies the un-modeled or incorrectly modeled dynamics combined with unknown external disturbances in real time and provides vital information for detecting faults with only partial information of the plant, which cannot be accomplished with any existing methods
This study mathematically proves that the ESO’s estimation error is upper-bounded and its upper-bound monotonously decreases with the observer bandwidth
Summary
The main function of an observer, known as estimator, is to extract information of the otherwise immeasurable variables for a vast number of applications that include feedback controls and system health monitoring or fault diagnosis. Observer-based residual-generation methods for fault diagnosis in nonlinear dynamic system. Observer-based fault-diagnosis was applied to robot manipulators using a mathematical technique called algebra of functions to design the nonlinear diagnostic observer [11]. An observer only provides the state estimation; but with what is known as Extended State Observer (ESO) [16,17,18,19], the term f is treated as another state and estimated in real time Such additional information proves to be crucial for the FDI purposes, as will be shown in this paper. Gao [18] improved the ESO technique and made it more practical by using a particular parameterization method that reduces the number of tuning parameters to one Such parameterized ESO has been successfully applied in many applications, in the context of the Active Disturbance Rejection Control (ADRC) [19].
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