Fault detection and distributed estimation with sensor networks
File(s)
Author(s)
Zhou, Yilun
Type
Thesis
Abstract
A sensor network is a distributed system, consisting of plenty of embedded sensors
that can be deployed over a large-scale physical environment. One of the major
applications of sensor networks is to monitor the state of systems that are evolving in the
sensing field. Thanks to the emergence of advancements in high-performance processors,
nodes in a sensor network can not only collect measurements but coordinate to estimate
the state of systems as well. This thesis proposes a monitoring architecture, where
distributed state estimation and fault detection algorithms are implemented by every
node in the sensor networks to track the system’s state while simultaneously detecting
the faults occurred in either the monitored systems or the sensor networks. Several
approaches for different monitoring tasks are presented in this thesis and classified
into two main parts: distributed state estimation and fault detection algorithms in the
monitoring architecture.
In the first part, we present two distributed state estimation algorithms in the
sensor networks for the monitoring of a system, which can be described by a centralized,
decentralized, or distributed dynamic model. The first one is implemented over a sensor
network, where the local estimator in each node consists of a filtering step – which
uses a weighted combination of neighboring sensors information – and a model-based
state predictor. The filtering weights and prediction parameters jointly minimize both
the mean and variance of the prediction error in a Pareto optimization framework at
each time step. Since each predictor uses the model of the whole system monitored by
the sensor network, the algorithm over a sensor network can only monitor a centralized
system or each subsystem of a decentralized system. For a distributed system, where
several subsystems interact with each other, the second algorithm implemented over
several sensor networks is introduced so that each sensor network can coordinate
with neighboring networks to monitor the corresponding subsystem of the distributed
system.
The second part of the thesis is devoted to fault detection algorithms for process
faults in monitored systems and sensor faults in sensor networks. The aforementioned
estimation algorithm over a sensor network is applied to design process fault detection algorithm for a centralized or decentralized system. A residual is defined, and suitable
stochastic thresholds are designed, allowing to set the parameters so to guarantee
an upper bound of false alarms probability. For detecting sensor faults in the sensor
networks, the centralized, decentralized, and distributed sensor fault detection schemes
are proposed in a discrete-time framework. And the detection performance is compared
by an industrial benchmark simulation in a continuous stirred tank heater (CSTH)
pilot plant. Then a rigorous fault detectability and detection time interval analysis of
the centralized sensor fault detection scheme is presented.
The performance of proposed distributed estimation methods and effectiveness of
presented fault detection methods are evaluated by extensive numerical and industrial
benchmark simulations.
that can be deployed over a large-scale physical environment. One of the major
applications of sensor networks is to monitor the state of systems that are evolving in the
sensing field. Thanks to the emergence of advancements in high-performance processors,
nodes in a sensor network can not only collect measurements but coordinate to estimate
the state of systems as well. This thesis proposes a monitoring architecture, where
distributed state estimation and fault detection algorithms are implemented by every
node in the sensor networks to track the system’s state while simultaneously detecting
the faults occurred in either the monitored systems or the sensor networks. Several
approaches for different monitoring tasks are presented in this thesis and classified
into two main parts: distributed state estimation and fault detection algorithms in the
monitoring architecture.
In the first part, we present two distributed state estimation algorithms in the
sensor networks for the monitoring of a system, which can be described by a centralized,
decentralized, or distributed dynamic model. The first one is implemented over a sensor
network, where the local estimator in each node consists of a filtering step – which
uses a weighted combination of neighboring sensors information – and a model-based
state predictor. The filtering weights and prediction parameters jointly minimize both
the mean and variance of the prediction error in a Pareto optimization framework at
each time step. Since each predictor uses the model of the whole system monitored by
the sensor network, the algorithm over a sensor network can only monitor a centralized
system or each subsystem of a decentralized system. For a distributed system, where
several subsystems interact with each other, the second algorithm implemented over
several sensor networks is introduced so that each sensor network can coordinate
with neighboring networks to monitor the corresponding subsystem of the distributed
system.
The second part of the thesis is devoted to fault detection algorithms for process
faults in monitored systems and sensor faults in sensor networks. The aforementioned
estimation algorithm over a sensor network is applied to design process fault detection algorithm for a centralized or decentralized system. A residual is defined, and suitable
stochastic thresholds are designed, allowing to set the parameters so to guarantee
an upper bound of false alarms probability. For detecting sensor faults in the sensor
networks, the centralized, decentralized, and distributed sensor fault detection schemes
are proposed in a discrete-time framework. And the detection performance is compared
by an industrial benchmark simulation in a continuous stirred tank heater (CSTH)
pilot plant. Then a rigorous fault detectability and detection time interval analysis of
the centralized sensor fault detection scheme is presented.
The performance of proposed distributed estimation methods and effectiveness of
presented fault detection methods are evaluated by extensive numerical and industrial
benchmark simulations.
Version
Open Access
Date Issued
2017-01
Date Awarded
2017-05
Copyright Statement
Attribution NoDerivatives 4.0 International Licence (CC BY-ND)
Advisor
Parisini, Thomas
Publisher Department
Electrical and Electronic Engineering
Publisher Institution
Imperial College London
Qualification Level
Doctoral
Qualification Name
Doctor of Philosophy (PhD)