Ieeepro techno solutions 2013 ieee embedded project an integrated design framework of fault-tolerant wireless networked control systems

462 IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS, VOL. 9, NO. 1, FEBRUARY 2013
An Integrated Design Framework of Fault-Tolerant
Wireless Networked Control Systems for Industrial
Automatic Control Applications
Steven X. Ding, Ping Zhang, Shen Yin, and Eve L. Ding
Abstract—In this paper, a design framework of fault-tolerant
wireless networked control systems (NCSs) is developed for indus-
trial automation applications. The main objective is to achieve an
integrated parameterization and design of the communication pro-
tocols, the control and fault diagnosis algorithms aiming at meeting
high real-time requirements in industrial applications. To illustrate
the design framework, a laboratory wireless fault-tolerant NCS
platform is presented.
Index Terms—Fault detection, fault-tolerant systems, networked
control systems, time-varying systems, wireless networks.
I. INTRODUCTION
THE rapid development of microelectronic, informa-
tion, and communication technologies has significantly
enhanced networking of sensors, actuators, controllers, and
microprocessors and accelerated the application of networked
control systems (NCSs) in major industrial sectors. This trend
is strongly driven by the industrial needs for distributed process
control and networked embedded systems [1]–[3]. In recent
years, research on NCSs receives considerably enhanced at-
tention in the control community [4]–[7]. The main focuses
of the research activities are on system performance analysis
[8] and controller design [9]–[11] under consideration of
network-induced effects, which are expressed in terms of the
so-called quality of service (QoS) parameters. The major QoS
parameters of a network are data transmission delays, jitter,
packet loss, and network failure rates. Investigations on the
codesign of the network QoS and the controller parameters
build the mainstream in this research field [3], [5]–[7].
Application of NCSs to automatic control of distributed pro-
cesses is the state of the art in major industrial sectors. Such
NCSs are distributed and consist of: 1) a great number of PNC
(process near component) nodes with integrated sensors, actua-
Manuscript received August 07, 2011; revised December 07, 2011; accepted
August 07, 2012. Date of publication August 21, 2012; date of current version
December 19, 2012. Paper no. TII-11-403.
S. X. Ding is with the Institute for Automatic Control and Complex Sys-
tems, University of Duisburg-Essen, 47057 Duisburg, Germany (e-mail: steven.
ding@uni-due.de).
P. Zhang is with the Rhine-Waal University of Applied Sciences, Germany.
S. Yin is with the Institute of Intelligent Control and Systems, Harbin Institute
of Technology, 150001 Harbin, China.
E. L. Ding is with the Department of Physical Engineering, University of
Applied Sciences, 45877 Gelsenkirchen, Germany.
Color versions of one or more of the figures in this paper are available online
at http://ieeexplore.ieee.org.
Digital Object Identifier 10.1109/TII.2012.2214390
tors and microprocessors; 2) control stations (CSs), each of
which coordinates and supervises a set of PNC nodes; and 3)
communication systems that network the CSs at the higher level
and the PNC nodes at the lower level.
Studies in the past have revealed that data transmission de-
lays, jitter, and packet loss rate strongly depend on the net-
work load [12]. In particular, for those networks like Ethernet,
WLAN, the randomness in the QoS parameters may increase
rapidly as the network is heavily loaded. The main reason for
it is the use of the carrier sense multiple access with collision
detection (CSMA/CD) approach as the medium access control
(MAC) protocol. In a typical industrial NCS, the number of the
nodes and the networked sensors, actuators as well as micropro-
cessors is constant during the normal process operation and only
varies in case of faults. On the other hand, to meet high real-time
control performance and reliability requirements, the QoS pa-
rameters should satisfy the requirements of the highest Class
of Service. These facts motivate the automation industry to de-
velop new network solutions to reduce the random behavior in
data transmissions and to enhance the real-time ability of the
network.
Both in the application and research domains, wireless com-
munications are viewed as a future-oriented trend in automatic
control [13], [14]. In the research area, much attention has been
paid to performance analysis of wireless NCSs (W-NCSs) [15]
and development of new control and observer schemes [16],
[17]. The major efforts in industrial automation are devoted to
the development of standards and protocols and new wireless
networks [18]–[20].
A most critical issue surrounding the design of NCSs is to
meet the requirements on system reliability and dependability,
while guaranteeing a high system performance [21]. In recent
years, a number of methods have been developed for achieving
reliable and robust fault detection and identification (FDI) as
well as fault-tolerant control (FTC) in NCSs [7], [22]–[24].
Analog to the trends in the control and filtering of NCSs, the
major research focus is on dealing with uncertainties in NCSs
caused by data transmission delays and packet losses.
It is worth noticing that the research activities in the con-
trol community are mainly dedicated to the development of ad-
vanced control and FDI schemes with the integrated QoS pa-
rameters, while the automation industry fully concentrates on
developing new generation of networks and MAC mechanisms
[12], [25]. For a wide application of the W-NCSs in industry,
an advanced design framework is needed, which allows an in-
tegrated design of controllers, FDI units and networks. In this
1551-3203/$31.00 © 2012 IEEE

DING et al.: INTEGRATED DESIGN FRAMEWORK OF FAULT-TOLERANT WIRELESS NCSs FOR INDUSTRIAL AUTOMATIC CONTROL APPLICATIONS 463
Fig. 1. Fault-tolerant W-NCS configuration.
way, not only the required control and FDI performance but also
high real-time ability and reliability can be achieved. This is the
motivation of our work. The main objective of this paper is to
develop a framework, based on which a W-NCS can be designed
and constructed for the real-time application in industrial au-
tomation. The core of this framework is an integrated design of
the MAC protocols, the control and FDI schemes, which allows:
1) a deterministic data transmission via wireless networks; 2) a
reduced data transmission amount and at the same time; and 3)
meeting the requirements on the control and FDI performance.
To illustrate our study, WiNC, an experimentation platform for
fault-tolerant wireless networked control, will be presented.
II. OUTLINE OF THE FAULT-TOLERANT W-NCS DESIGN
FRAMEWORK AND PROBLEM FORMULATION
Here, the process and control system models are first de-
scribed. The basic ideas behind the integrated design framework
of FT W-NCSs are then highlighted. Finally, the major topics to
be addressed are formulated.
A. Process and Control Loop Models
Suppose that the process under consideration consists of
sub-processes modeled by
(1)
where , denotes the state vector of the th
subprocess, and , , and are known matrices of appro-
priate dimensions. feedback control loops are applied for the
regulation of the th subprocess with actuators
...
(2)
and associated with it (3)
and sensors, ,
... (4)
where , denote the actuators/controllers and sensors em-
bedded in the th control loop of the th subprocess. It is as-
sumed that the sensors are nominally modeled by
(5)
with known matrix . In the following, the th subprocess
and the associated actuators and sensors embedded in
the control loops are called the th subsystem. For the real-time
implementation, the maximum allowed sampling time depends
on the process dynamics. We denote the critical sampling time
for the th subprocess by .
B. Outline of the System Configuration
In order to achieve high reliability and to meet the demands
for the control performance, the W-NCS configuration sketched
in Fig. 1 is proposed, which consists of the following.
• Execution layer: At this layer, PNC nodes are integrated
into (local) feedback control loops.
• Coordination and supervision layer: This layer consists of
CSs. They coordinate and synchronize the the overall

W-NCS operation. Another task of the CSs is process
monitoring, in which observer-based FDI algorithms are
implemented.
• Management layer: In our study, FTC is implemented in
the context of resource management [26]. Any component,
either sensor or actuator or process component, is defined
as system resource associated with some functionality. A
fault in one component is considered as a loss of the corre-
sponding resource or redundancy and activates a resource
reallocation.
C. Communication Structure
As shown in Fig. 1, the data transmissions in the NCS can be
classified as: 1) communications within a subsystem, which will
operate in a master–slave mode with the CS as the master and 2)
communications between the CSs, which serve as synchroniza-
tion and execution of the control, monitoring, and communica-
tion actions, and, in case of a fault, the activation and execution
of the resource reallocation and FTC algorithms. The data ex-
changes at this layer are periodic and regulated by a protocol
e.g., in the token passing mechanism. In the industrial real-time
control systems, data transmissions are often regulated based
on the simplified ISO/OSI three-layer model [12], in which the
physical layer is standardized. At the data link layer, also called
MAC, and at the application layer, the user is able to imple-
ment a scheduler that guarantees the required real-time perfor-
mance and regulates the QoS parameters of the network. In our
study, scheduler design will be done for the both communica-
tion forms.
D. Problem Formulation
The basic idea of establishing a framework for the design of
a fault-tolerant W-NCS is to address the multilayer fault-tol-
erant control and communication systems in an integrated way.
In order to meet the real-time requirements and ensure a de-
terministic data transmission, TDMA mechanism is adopted in
the proposed framework. In this way, the MAC protocol can be
modeled in form of a scheduler, whose design and parameteri-
zation will be achieved in a codesign with the development of
the control and FDI algorithms at the different functional layers.
For our purpose, the control and FDI schemes to be developed
should: 1) enable the realization of standard control and FDI
schemes; 2) be easily parameterized; and 3) take into account
the distributed process structure. In addition, to reduce the data
transmission costs, the scheduler parameters for the data trans-
missions as well as the synchronization within and between the
CSs should be integrated into the development of the controllers
and FDI/FTC algorithms.
III. INTEGRATED DESIGN FRAMEWORK FOR AN FT W-NCS
Here, we describe the integrated design framework for an in-
dustrial fault-tolerant W-NCS shown in Fig. 1.
A. Integrated Design at the Execution Layer
We first present the local control and FDI structures, algo-
rithms and the corresponding scheduling mechanism for the
data transmissions within a subsystem.
1) On the Data Transmissions: Following the TDMA mech-
anism, the data transmissions within a subsystem are periodic.
Let be the maximum data transmission time (including phys-
ical transmission and software operation times) between any
two nodes within the th subsystem. Define a time slot .
An operating cycle includes: i) time slots for the transmis-
sion of sensor data; 2) time slots for control commands; and
3) time slots for implementation of the communication
strategy. Let be the cyclic time with
(6)
To ensure the required deterministic real-time behavior,
should be bounded by the critical sampling time , i.e.,
(7)
where defines the upper bound of the maximum data trans-
mission time between any two nodes within the th subsystem
and is a parameter for dimensioning the capacity of the th
subnet.
Different from the application of robust control and filtering
theory as, for instance, reported in [27]–[29], in the integrated
design framework data transmission delays and packet loss are
mainly dealt with: 1) by dimensioning the capacity of the com-
munication nets according to (7) and 2) by running special com-
munication actions during the time slots reserved for the com-
munication. Supported by the TDMA mechanism, the first mea-
sure ensures that the influence of the transmission delays can be
generally neglected. In case that the transmission delay is larger
than , it will be treated as a missing packet. The handling of
packet loss is realized by means of the communication actions
like ACK, RTS (for repeating sending).
2) On Local Control and FDI Algorithms: Assume that an
estimate of , denoted by , is available at the be-
ginning of the th cycle, and sampling of is done at the
time instant where is some
integer. The following (local) control law is proposed:
(8)
(9)
where is the reference signal and received, to-
gether with from the th CS, denotes a stable
discrete-time LTI system which serves as the parameter transfer
matrix, , . The first term in (8) is
an observer-based state feedback law, while the second term is
the feedback of , which builds the so-called residual
signal vector. For the sake of simplifying the notation and design
issues it is assumed that and
thus . It is well
known that residual generation is the first step for a successful
FDI. Based on , , , faults
in the sensors and actuators embedded in the control loop
can be detected and isolated. The reader is referred to [31] for
the existing algorithms.
3) Realization of the Data Transmissions and the Scheduler:
Note that in the proposed W-NCS configuration ,

instead of the sensor data, will be transmitted to the th CS,
immediately after it is generated. Considering that the value
range of is generally smaller than the one of
and, moreover, the residual signals contain all
information needed for the controllers and observers, trans-
mitting results in reduced transmission costs
without losing needed information. It follows from (8) that
all controllers , share received
from the th CS. It motivates saving the data ,
, , in one packet. In the multicast
way, this packet is sent by the th CS to all control loops
in the th subsystem. Having received the data from the th
CS, each control loop will decode the packet for ,
. As a result, it holds for in (6) ,
, then .
B. Integrated Design at the Supervision and Coordination
Layer
Recall that the th CS will receive the residual signals and
send the state estimate and the reference signals to the associated
control loops in each cycle time. While the reference signals
are set by the user, will be delivered
by an observer. For the observer and controller design purpose,
the subprocess model (1) is discretized with the sampling time
to yield
(10)
In order to construct an observer for estimating , information
about the couplings with the other CSs expressed in terms of
, , is needed. Suppose that in the time interval
updates in the th CS are realized at the time
instants
, and assume for
for . It turns out
(11)
where denotes the last time instant, at which a up-
date in the -th CS is realized before the time instant :
no update
in the
update
in the
no update in the
...
update in
the
As a result, the following discrete-time model is obtained for
the th subsystem:
(12)
with , . Note
that is time-varying.
For the purpose of constructing the observers at the CSs,
the following scheduler for the wireless networking of the CSs
is proposed. Assume that , , can be expressed
by with an integer . Let , be
(13)
In the time interval , the th CS,
, will, in the broadcast mode, send the estimate for
to all other CSs at the time instants
. It is evident that this
scheduler is periodic with as period time.
Remember that the subsystems may have different cycle
times. The introduction of the scheduler for the communica-
tions among the CSs is helpful to synchronize the actions in the
subsystems. For this purpose, we introduce the deﬁnition: the
time instants, , are
denoted by , , and they are ordered as
.

Based on the above scheduler and the discrete subsystem
model (12), the observer embedded in the th CS is constructed
as follows:
... (14)
where is the observer gain matrix to be designed.
Since the scheduler results in a periodic data transmis-
sions, it holds .
For the implementation of (14), the th CS receives: 1)
, sent by the (local) control loops
and 2) , sent by the other CSs, during the time
interval . The residual signal is used
both for improving the estimation performance and, together
with the available , , ,
for the computation of the local controller
while is applied for constructing , as defined
in (11), which describes the couplings between the th CS and
the other CSs.
It is well known that a successful FDI for the components
embedded in the th subsystem can be achieved: 1) by a suitable
selection of the stable post-filter for building
(15)
and 2) by evaluating and threshold settings [31]. It is worth
noting that the th CS has access to all available residual signals
from the control loops within the subsystem. As a result, the FDI
based on is more reliable and efficient in comparison with
a local FDI algorithm.
C. Management Layer
Depending on application purposes, the management layer
can be designed individually. In the laboratory WiNC platform
that will be described in Section V, a resource monitoring is
built, which is driven by fault knowledge provided by the FDI
algorithms. It is realized in form of a database, in which the
available sensors (including observers as soft sensors), actu-
ators, communication systems, process components together
with their redundancy are clustered in terms of their role for
executing a defined functionality (e.g., control or FDI). Re-
source management and reallocation can be formulated as an
optimization problem and solved by means of an optimization
algorithm [26]. In this paper, the management layer design
issues will not be addressed.
TABLE I
MAIN DESIGN PARAMETERS
IV. DESIGN ISSUES
It follows from (7), (14), and (15) that the control, FDI al-
gorithms, and communications are well parameterized, as sum-
marized in Table I. This section deals with the design issues of
these controller, observer and FDI parameters.
A. Overall Model of the W-NCS
To begin with, the overall W-NCS is modeled based on the
discrete-time models of the subsystems and the scheduler for the
communications between the subsystems, which are periodic
with the period time . To this end, ,
, in the time interval
(16)
are first brought into a vector. To simplify the notation, ,
as defined in (16) will be applied in the sequel if no con-
fusion is caused. We denote this vector by and, without
loss of the generality, suppose that
...
...
...
...
(17)
where denotes the vector consisting of all of those state
variables that have a update at the time instant . Again, for
the sake of simple notation, is now introduced to denote
the set of all of those subsystems, which have a update at the
time instant . It is clear that the vector works like a
buffer with a variable length, in which all state variables in the
time interval are saved. At the next time instant
, the state variables at the time instant are removed
from the buffer and those at the time instant are added. As
a result, is formed, which includes all state variables

in the time interval . This procedure can be
modeled by
(18)
...
...
(19)
After a straightforward computation, we have ﬁnally
...
...
...
...
...
...
...
...
(20)
In this way, , , and can be determined. It is
clear that (18) is a linear time-periodic (LTP) system.
B. Observer Design
Using (20), the observer algorithm (14) can be rewritten as
...
...
...
...
(21)
where , , respectively, denote the estimation
for , , the residual vector
generated in the th subsystem with , and in-
cludes all the residual vectors generated in the subsystems be-
longing to . Recall that
...
It turns out
...
...
(22)
Now, taking into account the special form of the overall NCS
model (18), we can rewrite the observers of the form (14)
into one (distributed) observer as follows:
(23)
(24)
(25)
whose error dynamics is governed by
(26)
(27)
In [32], observer design schemes for LTP systems are presented,
which can be applied for our purpose.
C. Controller Design
To begin with, the idea behind the control law (8) is brieﬂy in-
troduced. Let be the plant model
of a feedback control loop. It has been proven in [30] that all sta-
bilizing controllers (the so-called Youla parameterization [33])
can be written as
(28)

where is a state feedback gain matrix, is a stable param-
eterization matrix, is an estimation delivered by
(29)
and . Comparing (8) and (28) makes it
clear that (8) is a local realization of (28) as far as is
delivered by a full order observer like (29). For the purpose of
designing the controllers given in (8), apply the control law (8)
to (18), which leads to
(30)
...
...
...
... (31)
with being the time instant equal to the one of the th row
block in the state vector , numbering like
...
...
...
...
Hence, the overall system dynamics is described by
(32)
(33)
The basic requirement on the selection of is formulated as
finding so that the system
is stable. To this end, the approaches, e.g., given in [32] for
LTP controller design can be applied. Recall that the second
term in the control law (8) is the feedback of the residual vector
. The selection of a stable post-filter
...
will have no influence on the system stability, and can be dedi-
cated to improving the system robustness.
D. FDI Algorithms
Both at the execution and supervision and coordination
layers, the core of the FDI algorithms consists of the computa-
tion of the post-filters. For our purpose, the design of ,
, , is based on the local model
(34)
(35)
where the terms , model the unknown inputs e.g.,
caused by the couplings with other subsystems, process, and
sensor noises, etc. Similarly, the design of the post-filter ,
, is based on
(36)
(37)
Note that both models, (34)–(35) and (36)–(37), are LTP sys-
tems. In [34] and [35], a decoupling approach and a unified so-
lution for the LTP systems are presented, which can be used for
the design of the local and higher level FDI subsystems.
E. Advanced Design Issues
Below, some advanced design issues for improving the
W-NCS performance, robustness, and reliability are addressed.
1) Increasing Data Transmissions Among the Subsystems:
Recall that the observer (14) at the th subsystem is driven only
by the (local) residual vector . As a result, the struc-
ture of the observer gain matrix is restricted. One way to im-
prove the observer performance is to increase the data transmis-
sions between the sub-systems. To this end, the -th sub-system
can transmit together with in one packet
to all of the other subsystems.
2) Enhancing System Robustness and Reliability: In the pre-
vious study, uncertainties caused by for instance a linearization,
sampling errors and packet drops have not been taken into ac-
count by modeling. In order to increase the robustness of LTP
systems against disturbances and unknown inputs, the design
schemes presented in [32] and [35] can be applied. Recently,
there are a great of number of publications addressing the packet
drop issue in NCSs for the control issues [9], [28], [36], for the
filtering and observer design issues [37], [38], as well as for the
FDI issues [29], [39]–[41].

Fig. 2. Schematic description of the WiNC platform.
V. EXPERIMENTATION PLATFORM WINC
Parallel to the theoretical study, efforts have been made to
construct a W-NCS platform, called WiNC [26]. This work is
strongly motivated by the need to demonstrate the application
of the proposed design framework. To this end, standard wire-
less cards supporting IEEE 802.11a/b/g standards have been
selected. In almost every available 802.11 hardware the MAC
protocol employs the CSMA/CA approach. In order to achieve
a reliable deterministic data transmissions, a new MAC pro-
tocol optimized for industrial wireless communication using the
available 802.11 based hardware is developed in our lab using
SoftMAC [42]. In SoftMAC several measures are taken to pro-
vide real-time capability. The result is that the carrier sensing
and the backoff procedure can be controlled and no additional
packets, like ACK or RTS/CTS, occur. SoftMAC allows user-
defined protocol implementation.
A. Platform Configuration
Fig. 2 shows the configuration of the WiNC platform that is
composed of several functionalities, a driver, and a protocol
stack optimized for industrial wireless communication. In the
platform, the communication functionality of organizing the
network access by use of a scheduler is based on time slots
and tailored. The control, observer and FDI functionalities are
integrated into the WiNC application layer. The protocol stack
integrates a simplified addressing scheme with a custom packet
structure and robust channel coding techniques, thus ensuring
deterministic transmission times. The main technical data are:
1) hardware: desktop computers with x86 architecture micro-
processors, equipped with one D-Link DWL-AG530 Tri-Mode
Dual-band PCI card and one data acquisition card and 2)
software: Linux operating system (Fedora 6 with RT-Preempt
Patch 2.6.20-rt8), SoftMAC, and COMEDI (COntrol and
MEasurement Device Interface).
B. Interfaces for the Parameterization and NCS Monitoring
WiNC is supported by a graphic user interface (GUI) and an
NCS monitor. The GUI is essential for the realization of the
integrated design, and consists of: 1) a parameter setting for
the controllers, observers, and FDI algorithms and 2) a direct
access to the MAC and setting of the scheduler. By means of
this GUI, the control and FDI parameters given in Table I, and
the scheduler described in the last sections can be directly given
by the user. It is also possible to input these parameters in the
MATLAB/SIMULINK environment. The WiNC monitor includes:
1) the standard functions like plots of all control and output
variables; 2) the diagnostic module that displays all fault types
and plots of residual signals; and 3) system statistics including
the control performance, the QoS of the network.
C. Tests and Applications
The WiNC has been tested under different working condi-
tions. For this purpose, a test bed has been built with two well-
known laboratory setups: Three-Tank as sub-system 1 with a
critical sampling time larger than 1 s and Inverted Pendulum
as sub-system 2 with a critical sampling time 50 ms. The first
subsystem is composed of three level sensors and two pumps
as actuators. The second one has two sensors and one actuator.
One of the tests was to run the system over night so that the wire-
less transmission channels remain relatively time-invariant. In
this test, a fixed antenna setup has been used with all sensors
and actuators being attached to the plants in the laboratory and
the controller being placed one floor below. The 2.4 GHz band

and the binary phase shift keying (BPSK) modulation with the
direct sequence spread spectrum (DSSS) spreading technique
which is expected to provide the most reliable communication
of all available modulation methods of the 802.11a/b/g stan-
dards have been applied. Data have been collected for two sce-
narios: without flexible retransmission request (FRR) and with
FRR. For these scenarios, the transmission power of all cards
has been varied in the range from 1 to 16 dBm. In [26], some
of the test results have been reported in details. WiNC and the
benchmark setups have also been successfully used in research
and education projects. In [26] and [39] successful realizations
of the FDI schemes for the subsystems Inverted Pendulum and
Three-Tank are reported.
VI. CONCLUSION
In this paper, a design framework for a fault-tolerant W-NCS
has been presented for industrial automatic control with high
real-time requirements. The essential idea of this framework is
the integrated design and parametrization of the control, FDI
algorithms and communication nets in a multilayer system con-
figuration. The core of the W-NCS is the application of a dis-
tributed observer and the local residual generators that serve
both for the control, FDI, and communication purposes. The
design and construction of the observer and residual generators
are realized with respect to the TDMA-based scheduler for the
data transmissions. This guarantees a deterministic data trans-
mission and allows a system design in the LTP system-theoret-
ical framework. In order to demonstrate the proposed integrated
design framework, a platform, WiNC, has been developed and
briefly presented in this paper.
ACKNOWLEDGMENT
The platform WiNC has been developed by the control (AKS)
and communication (NTS) groups at the University of Duis-
burg-Essen, which is in part funded by the German Research
Foundation. The authors would like to thank the NTS group
headed by Prof. Czylwik for the successful collaboration, Dr.
Chihaia, and Mr. Goldschmidt for the technical contributions.
Also, the authors are grateful to the anonymous reviewers for
their valuable comments.
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Steven X. Ding received the Ph.D. degree in elec-
trical engineering from the Gerhard-Mercator Uni-
versity of Duisburg, Duisburg, Germany, in 1992.
From 1992 to 1994, he was an R&D Engineer with
Rheinmetall GmbH. From 1995 to 2001, he was a
Professor of control engineering with the University
of Applied Science Lausitz, Senftenberg, Germany,
and served as Vice President of this university during
1998 to 2000. Since 2001, he has been a Professor
of control engineering and the head of the Institute
for Automatic Control and Complex Systems (AKS)
at the University of Duisburg-Essen, Duisburg, Germany. His research interests
are model-based and data-driven fault diagnosis, fault-tolerant systems and their
application in different industrial sectors.
Ping Zhang received the Ph.D. degree in control en-
gineering from Tsinghua University, Beijing, China,
in 2002.
From 2002 to 2007, she was with the Institute for
Automatic Control and Complex Systems (AKS),
University of Duisburg-Essen, Duisburg, Germany.
From 2007 to 2012, she was with the Competence
Center of Automation Technology, BASF SE,
Germany. Since 2012, she has been a Professor
of automation engineering with the Rhine-Waal
University of Applied Sciences, Germany. Her
research interests are model and data based fault diagnosis, fault tolerant
control, periodic and time-varying systems, networked control systems, plant
asset management, and their applications in the process industry, automobile
industry and energy systems.
Shen Yin received the B.E. degree in automation
from Harbin Institute of Technology, Harbin, China,
in 2004, and the M.Sc. degree in control and in-
formation system and Ph.D. degree in electrical
engineering and information technology from Uni-
versity of Duisburg-Essen, Duisburg, Germany, in
2007 and 2012, respectively.
He is currently with the Institute of Intelli-
gent Control and Systems, Harbin Institute of
Technology, Harbin, China. His research interests
are model-based and data-driven fault diagnosis,
fault-tolerant control, and their application to large-scale industrial processes.
Eve L. Ding received the Ph.D. degree in mechanical
engineering from the Gerhard-Mercator University,
Duisburg, Germany, in 1991.
Between 1991–1994, she held academic positions
with the University of Bremen and the University
of Rostock. From 1995 to 1999, she was an R&D
Engineer with Continental Teves AG & Co. OHG,
Germany, where she was responsible for the develop-
ment of fault diagnosis systems in ESP. Since 1999,
she has been a Professor of control engineering with
the University of Applied Science, Gelsenkirchen,
Germany. Her research interests are model-based fault diagnosis, fault-tolerant
systems, and their application in industry with a focus on automotive systems.

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