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

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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

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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

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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

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1

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GBNNM

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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