A Robust Sensor Fault Diagnosis System Using Neural Networks and a Dynamic Process Model
Abstract
Back propagation (BP) neural networks are applied for reactor fault diagnosis. Analyzed is the output prediction error between a neural network model and an independent dynamic process model, which serves as a residual for diagnosing actuator/component/sensor faults. It is found that the neural network without the process model is less sensitive to sensor faults than actuator or component faults. A scheme is developed utilizing a second neural model to analyze the difference between the output prediction of the process model and the neural network for diagnosing sensor faults in a simulated reactor. Results from the reactor fault diagnosis system are presented to demonstrate the satisfactory detection and isolation of sensor faults using this approach.
Description
PresentationSubject
Robust Sensor Fault DiagnosisCollections
Citation
Zhou, Yifeng; Kazantzis, Nikolas; Mannan, M. Sam (2001). A Robust Sensor Fault Diagnosis System Using Neural Networks and a Dynamic Process Model. Mary Kay O'Connor Process Safety Center; Texas &M University. Libraries. Available electronically from https : / /hdl .handle .net /1969 .1 /193866.