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Hi! This is Zhenbao Liu’s (刘贞报) personal website. Currently, I am a professor at School of Aeronautics, Northwestern Polytechnical University, China. I received PhD degree in College of Systems and Information Engineering from University of Tsukuba, Japan in 2009, Bachelor and Master degrees in Northwestern Polytechnical University, China in 2001 and 2004. My research interests are concentrated on intelligent flight control, autonomous Unmanned Aerial Vehicle, aircraft intelligent fault diagnosis and prognostics, and related fields. I have published approximately 50 papers in major international journals and conferences, including the IEEE TIE, TII, TNNLS, IEEE TCYB, IEEE TCSVT, TIM, Automatica, and Pattern Recognition.
My Recent Publications (2017)
Signal Model-based Fault Coding for Diagnostics and Prognostics of Analog Electronic Circuits
Zhenbao Liu, Taimin Liu,Junwei Han, Shuhui Bu, Xiaojun Tang, Michael Pecht, Signal Model-based Fault Coding for Diagnostics and Prognostics of Analog Electronic Circuits, IEEE Transactions on Industrial Electronics,64(1), pp. 605-614, 2017.
Analog circuits have been extensively used in industrial systems, and their failure may make the systems work abnormally and even cause accidents. In order to monitor their status, detect faults, and predict their failure early, this study proposes signal model-based fault coding to monitor circuit response after being stimulated to perform fault diagnosis without training a large amount of sample data and fault classifiers. Manifold features extracted from circuit responses are associated with a faultindicating curve in the feature space, on which a group of fault bases are uniformly and continuously distributed along with gradual deviation from the nominal value of one critical component. These bases can be deployed in a factory setting but used during field operation. Fault coding is converted to a novel optimization problem, and the optimized solution forms a fault code representing fault class, suitable for realizing fault detection and isolation for different components. A fault indicator based on comparison between fault codes can describe performance degradation trends. To improve prediction accuracy, historical degradation data are collected and considered as a priori exemplars, and a novel exemplar-based conditional particle filter is proposed to track degradation process for the prediction of remaining useful performance. Case studies on two analog filter circuits demonstrate that the proposed method achieves relatively high fault diagnosis and prognosis accuracy. The main advantages of our work are two-fold: first, high diagnostic accuracy can still be obtained even if there is no large amount of training data; second, prognostic effect remains relatively stable whenever triggering prognosis module.
Capturing High Discriminative Fault Features for Electronics-rich Analog System via Deep Learning
Zhenbao Liu, Zhen Jia, Chi Man Vong, Shuhui Bu, Junwei Han, Capturing High Discriminative Fault Features for Electronics-rich Analog System via Deep Learning, IEEE Transactions on Industrial Informatics, 13(3), 1213-1226, 2017.
In this paper, a novel fault diagnostic application of Gaussian-Bernoulli deep belief network (GB-DBN) for electronics-rich analog systems is developed which can more effectively capture the high order semantic features within the raw output signals. The novel fault diagnosis is validated experimentally on two typical analog filter circuits. Experimental results show the fault diagnosis based on GB-DBN is with superior diagnostic performance than the traditional feature extraction methods.
Particle Learning Framework for Estimating the Remaining Useful Life of Lithium-ion Batteries
Zhenbao Liu, Gaoyuan Sun , Shuhui Bu , Junwei Han , Xiaojun Tang , Michael Pecht. Particle Learning Framework for Estimating the Remaining Useful Life of Lithium-ion Batteries, IEEE Transactions on Instrumentation and Measurement, 66(2), pp. 280-293, 2017.
As an important part of prognostics and health management (PHM), accurate remaining useful life (RUL) prediction for lithium-ion batteries can provide helpful reference for when to maintain the batteries in advance. This paper presents a novel method to predict the RUL of lithium-ion batteries. This method is based on the framework of improved particle learning (PL). The PL framework can prevent particle degeneracy by resampling state particles first with considering the current measurement information and then propagating them. Meanwhile, PL is improved by adjusting the number of particles at each iteration adaptively to reduce the running time of the algorithm, which makes it suitable for on-line application. Furthermore, the kernel smoothing algorithm is fused into PL to keep the variance of parameter particles invariant during recursive propagation with the battery prediction model. This entire method is referred to as PLKS in this paper. The model can then be updated by the proposed method when new measurements are obtained. Future capacities are iteratively predicted with the updated prediction model until the predefined threshold value is triggered. The RUL is calculated according to these predicted capacities and the predefined threshold value. A series of case studies which demonstrate the proposed method are presented in the experiment, including Unmanned Aerial Vehicle.
Full publication list can be found at here.