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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 Cen 2015/3/18 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. Fault prognosis and recovery for Aerocrafts and Unmanned Aerial Vehicles
Fig.2-4 diagram of Satellite Attitude Control system
Disturbance
ControllerReaction
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 Cen 2015/3/18 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. Quadrotor UAV and Thrust-Vectoring UAV aerodynamics modeling and control
z zF Ty yF T
x xF T
a
e
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F
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OF
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ear a
ircra
ft
Co
ntro
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Fast d
yn
am
ics (in
ne
r loop
)
Fa
st lo
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trolle
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Slo
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yna
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s (o
ute
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)
Slo
wer lo
op c
on
trolle
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slo
wer d
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(oute
rer lo
op)
Slo
we
r loop
co
ntro
ller
Na
vig
atio
n d
yn
am
ics
(oute
rest lo
op
)
Navig
atio
n lo
op
con
trolle
r
+
+
+
+
+
+
+
+
++
+
Thrust Vector control+
V
yz
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pqr
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Ty
Tz
dp
dq
dr
cp
cq
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d
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-
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-
c
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d
d
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c
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dy
dz
yz
cy
cz
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cT
-200
0
200400
600
800-40 -20 0 20
-80
-60
-40
-20
0
20
Y (m)X (m)
Z (m
)
-50
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250 -200
20
-20
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20
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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 Cen 2015/3/18 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. 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)
res
du
al
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)
Pa
rtia
l LO
E f
au
lt p
ram
ete
r(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 Cen 2015/3/18 4
Fig 4-1“P2P” diagnosis strategy for general systems Fig. 4-2 Various NNs as a reference for residuals
nonlinear dynamic system1
S2 ( )f
1( )f
3( )f
4 ( )f1
S
1
S
1
S2NN
1NN
3NN
4NN1
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 wNN2
2 2( , , )g x x w
actuators
1
Sˆ( )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
2ˆ ( , , )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