Paper Info
Title
An Online Outlier Identification and Removal Scheme for Improving Fault Detection Performance
Abstract
Measured data or states for a nonlinear dynamic system is usually contaminated by outliers. Identifying and removing outliers will make the data (or system states) more trustworthy and reliable since outliers in the measured data (or states) can cause missed or false alarms during fault diagnosis. In addition, faults can make the system states nonstationary needing a novel analytical model-based fault detection (FD) framework. In this paper, an online outlier identification and removal (OIR) scheme is proposed for a nonlinear dynamic system. Since the dynamics of the system can experience unknown changes due to faults, traditional observer-based techniques cannot be used to remove the outliers. The OIR scheme uses a neural network (NN) to estimate the actual system states from measured system states involving outliers. With this method, the outlier detection is performed online at each time instant by finding the difference between the estimated and the measured states and comparing its median with its standard deviation over a moving time window. The NN weight update law in OIR is designed such that the detected outliers will have no effect on the state estimation, which is subsequently used for model-based fault diagnosis. In addition, since the OIR estimator cannot distinguish between the faulty or healthy operating conditions, a separate model-based observer is designed for fault diagnosis, which uses the OIR scheme as a preprocessing unit to improve the FD performance. The stability analysis of both OIR and fault diagnosis schemes are introduced. Finally, a three-tank benchmarking system and a simple linear system are used to verify the proposed scheme in simulations, and then the scheme is applied on an axial piston pump testbed. The scheme can be applied to nonlinear systems whose dynamics and underlying distribution of states are subjected to change due to both unknown faults and operating conditions.
Year
DOI
Venue
2014
10.1109/TNNLS.2013.2283456
Neural Networks and Learning Systems, IEEE Transactions  
Keywords
Field
DocType
fault diagnosis,fault tolerant control,neurocontrollers,nonlinear dynamical systems,observers,statistical analysis,FD framework,NN weight update law,OIR scheme,analytical model-based fault detection,axial piston pump,fault detection performance,median,model-based fault diagnosis,model-based observer,moving time window,neural network,nonlinear dynamic system,observer-based techniques,online outlier identification-and-removal scheme,simple linear system,standard deviation,three-tank benchmarking system,Data analysis,fault diagnosis,neural networks,nonlinear systems,nonlinear systems.
Anomaly detection,Nonlinear system,Linear system,Control theory,Computer science,Artificial intelligence,Artificial neural network,Pattern recognition,Fault detection and isolation,Outlier,Algorithm,Observer (quantum physics),Estimator
Journal
Volume
Issue
ISSN
25
5
2162-237X
Citations 
PageRank 
References 
16
0.83
16
Authors
3
Name
Order
Citations
PageRank
Hasan Ferdowsi1464.35
Sarangapani Jagannathan2113694.89
Maciej Zawodniok3354.67