1. 1. Test, diagnosis and reliability for Electronics, Renewable energy systems and Smart-grid
Component
failures
Related deliverables:
[1] Z. Cen. Condition Parameter Estimation for Buck Converter based on
Model Observer. IEEE Transactions on Industrial Electronics. 2015. (in
revision)
[2]Z. Cen, Abdelkader Bousselham. Fault diagnosis for Photo-Voltaic
Power Converter based on Model Observers. The International
Conference on Advances in Computing, Communication and Information
Technology -CCIT 2014, London, UK, June 2014. (Best Paper Award)
[3]Y. Cao, Z. Cen and J. Wei, "FDSAC-SPICE: Fault diagnosis software for
analog circuit based on SPICE simulation," in International Conference on
Space Information Technology 2009.
Registered Software:
Wei Jiaolong, Cen Zhaohui, JIang rui, Fault simulation software for
aircraft attitude control system. (Registered No: 2010SR004895).
Zhaohui Cen2015/3/18 1
Fig. 1-1 faulty electronic components and typical failures
Fig. 1-2 ATE environment hardware for eletronics test and
diagnosis
Fig. 1-3 “Fault doctor” software operation panel
Fig. 1-4 Diagnosis reasoning-logic procedure Fig. 1-5 Fault-tree analysis
Fig. 1-6 self-diagnosis and condition parameter estimation for power
electronics devices
Fig. 1-7 experiment platform for power electronics based on NI compact
RIO and labview

2. 2. Fault prognosis and recovery for Aerocrafts and Unmanned Aerial Vehicles
Fig.2-4 diagram of Satellite Attitude Control system
Disturbance
Controller
Reaction
Wheel
Attitude
Dynamics Model
Attitude
Determine
Model
Attitude
Sensors
Model
Attitude Motion
Model
- +
Refer
Attitude
Fault
inu outu
Selected deliverables:
[1] Z.Cen, H.Noura, T.Bagus, Al Younes. Robust Fault Diagnosis for Quadrotor
UAVs Using Adaptive Thau Observer*J+ , Journal of Intelligent & Robotic Systems,
January 2014, Volume 73, Issue 1-4, pp 573-588.
[2] Z. Cen, J. Wei and R. Jiang, A Grey-Box Neural Network based Model
Identification and Fault Estimation Scheme for Nonlinear Dynamic Systems*J+,
International journal of neural system. 23(6), 2013, 1350025. (Currently IF=6.056
and a rank of 3 out of 114 in the Computer Science and Artificial Intelligence
category).
[3] J. Wei, Z. Cen and R. Jiang. A sensor fault-tolerant observer for satellite attitude
control*J+. Journal of System Engineering and Electronics. 2012. Vol. 23, No. 1,
February 2012, pp.99–107.
Patents:
[1]Wei Jiaolong, Cen Zhaohui, JIang rui, Fault tolerant observing method of
sensor for satellite attitude control system. (Authorized No: ZL200910060816.2).
[2]Wei Jiaolong, Cen Zhaohui, JIang rui, Blind system fault detection and isolation
method for real-time signal processing of spacecraft. (Authorized No:
ZL200910272265.6).
Zhaohui Cen2015/3/18 2
Fig. 2-1 FDD methods utilized in my research
Fig. 2-2 Studied satellite prototype
Fig. 2-3 Hardware-in-Loop Simulation Environment
Fig. 2-5 studied Quad-rotor UAV
Fig. 2-6 studied Hexi-rotor UAV

3. 3. Quadrotor UAV and Thrust-Vectoring UAV aerodynamics modeling and control
z zF T
y yF T
x xF T
a
e
r
Ty
Tz
T
F
,n p
,m q
,l r
6DOFNonlinearaircraft
Controlallocation
Fastdynamics(innerloop)
Fastloopcontroller
Slowdynamics(outerloop)
Slowerloopcontroller
slowerdynamics
(outererloop)
Slowerloopcontroller
Navigationdynamics
(outerestloop)
Navigationloopcontroller
+
+
+
+
+
+
+
+
+
+
+
Thrust Vector control+
V
yz
V

pqr
a
e
r
Ty
Tz
dp
dq
dr
cp
cq
cr
pq r
d
d
d
-
-
-
-
-
--
-
-
-
c
c
c
dV
d
d
cV
c
c

V
dy
dz
yz
cy
cz
-
-
cT
-200
0
200
400
600
800-40 -20 0 20
-80
-60
-40
-20
0
20
Y (m)
X (m)
Z(m)
-50
0
50
100
150
200
250 -20
0
20
-20
0
20
40
Y (m)
X (m)
Z(m)
Related deliverables:
[1] Z.Cen, H.Noura, Al Younes. Systematic Fault Tolerant Control based on
Adaptive Thau Observer Estimation for Quadrotor UAVs *J+ ,International
Journal of Applied Mathematics and Computer Science (AMCS), 2015, Vol. 25,
No. 1.
[2]Z. Cen, Tim Smith, Paul Stewart and Jill Stewart. Integrated flight/thrust
vectoring control for jet-powered unmanned aerial vehicles with ACHEON
propulsion *J+. Proceedings of the Institution of Mechanical Engineers, Part G:
Journal of Aerospace Engineering, July, 2014, DOI: 10.1177/0954410014544179.
Zhaohui Cen2015/3/18 3
Fig. 3-1 Quad-rotor UAV in hovering Fig. 3-2 Studied TV-UAV
Fig. 3-3 kinetics and dynamics of
Quad-rotors
Fig. 3-4 kinetics and dynamics of
Thrust-vectoring Fixed-wing aircrafts
Fig. 3-5 Proposed full Position Controller for TV-UAV
Fig. 3-6 Trajectory of UAVs under position control
Fig. 3-7 High-Attack-Angle control for TV-UAV
Fig. 3-8 Velocity-Vector-Roll control for TV-UAV

4. 4. Neural Networks and its applications in modeling and fault identification
0 200 400 600 800 1000
-0.15
-0.1
-0.05
0
0.05
0.1
0.15
0.2
t(s)
resdual
tuned SPNN
expected
GBNNM
tuned SNN
tuned RNN
tuned PNN
0 200 400 600 800 1000
-0.25
-0.2
-0.15
-0.1
-0.05
0
0.05
0.1
0.15
0.2
0.25
t(s)
PartialLOEfaultprameter(N.m)
estimation of IESONN
estimation of ESO
real fault value
d=0.1
d=0.2
d=0.15
INDEX SNN RNN PNN SPNN
GBNN
M
expected
R2 98.84% -21.25 46.98% 97.46% 99.99% 100%
RMSE 3.95e-2 1.73 2.692e-1 5.85e-2 2.7e-3 0
Selected deliverables:
[1] Z. Cen, J. Wei and R. Jiang, A Grey-Box Neural Network based Model
Identification and Fault Estimation Scheme for Nonlinear Dynamic Systems*J+,
International journal of neural systems. 23(6), 2013, 1350025. (Currently IF=6.056
and a rank of 3 out of 114 in the Computer Science and Artificial Intelligence
category).
[2]Z. Cen, J. Wei, R. Jiang, and X. Liu. Application of Mallat wavelet fast transforms
and IDRNN in real-time fault detection and identification for satellites *J+, Journal
of University of Science and Technology Beijing,2012. 32(1), 90-95.
[3] Z. Cen, J. Wei, R. Jiang, and X. Liu, "Real time fault diagnosis of Infrared Earth
Sensor using Elman neural network," Zhendong Ceshi Yu Zhenduan/Journal of
Vibration, Measurement and Diagnosis, vol. 30, pp. 504-509, 2010.
Patents:
[1]Wei Jiaolong, Cen Zhaohui, JIang rui, Blind system fault detection and isolation
method for real-time signal processing of spacecraft. (Authorized No:
ZL200910272265.6).
Zhaohui Cen2015/3/18 4
Fig 4-1“P2P” diagnosis strategy for general systems Fig. 4-2 Various NNs as a reference for residuals
nonlinear dynamic system
1
S2 ( )f
1( )f
3 ( )f
4 ( )f1
S
1
S
1
S2NN
1NN
3NN
4NN
1
S
1
S
Grey-box NN identification
u y
Fig. 4-3 Proposed “Grey-Box” NN concept
1
S
( , )F x u
( )x t
( , )h x x
( )x t
( )fy t
NN1
1 1( , , )g x u w
NN2
2 2( , , )g x x w
actuators
1
S
ˆ( )x t
ˆ( )x t
GBNNM

+
-
ˆ( )y t
( )r t
( )u t
Improved ESO based on Neural Network
NN1
1 1( , , )g x u w
NN2
2 2( , , )g x x w
1
S
 
-
2
2
ˆ ( , , )f g e   
+
+
-
1
+
Estimation of
Fault parameter
GBNNM
Residual
Fig. 4-4 GBNNM application for fault estimation
Fig. 4-5 Fault identification results
Tab. 4-1 Modeling Comparison for Various NN and proposed GBNNM