Model-Based Fault Detection, Estimation, and Prediction for a Class of Linear Distributed Parameter Systems


This paper addresses a new model-based fault detection, estimation, and prediction scheme for linear distributed parameter systems (DPSs) described by a class of partial differential equations (PDEs). An observer is proposed by using the PDE representation and the detection residual is generated by taking the difference between the observer and the physical system outputs. A fault is detected by comparing the residual to a predefined threshold. Subsequently, the fault function is estimated, and its parameters are tuned via a novel update law. Though state measurements are utilized initially in the parameter update law for the fault function estimation, the output and input filters in the modified observer subsequently relax this requirement. The actuator and sensor fault functions are estimated and the time to failure (TTF) is calculated with output measurements alone. Finally, the performance of detection, estimation and a prediction scheme is evaluated on a heat transfer reactor with sensor and actuator faults.


Electrical and Computer Engineering

Research Center/Lab(s)

Intelligent Systems Center


This research is supported in part by NSF Grant IIP #1134721 for Center on Intelligent Maintenance Systems and Intelligent Systems Center.

Keywords and Phrases

Actuators; Fault detection; Forecasting; Heat transfer; Partial differential equations; Actuator and sensor faults; Distributed parameter systems; Fault estimation; Fault prognosis; Model-based fault detection; Parameter update law; Partial Differential Equations (PDEs); Sensor and actuators; Parameter estimation

International Standard Serial Number (ISSN)


Document Type

Article - Journal

Document Version


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© 2016 Elsevier, All rights reserved.

Publication Date

01 Apr 2016