Chiman Kwan

Bio: Chiman Kwan is an academic researcher from University of Texas at Arlington. The author has contributed to research in topics: Fault (power engineering) & Hyperspectral imaging. The author has an hindex of 26, co-authored 104 publications receiving 3085 citations.

Active health monitoring of an aircraft wing with embedded piezoelectric sensor/actuator network: I. Defect detection, localization and growth monitoring

Abstract: This work focuses on an ultrasonic guided wave structural health monitoring (SHM) system development for aircraft wing inspection. In part I of the study, a detailed description of a real aluminum wing specimen and some preliminary wave propagation tests on the wing panel are presented. Unfortunately, strong attenuation and scattering impede guided waves for large-area inspection. Nevertheless, small, low-cost and light-weight piezoelectric (PZT) discs were bonded to various parts of the aircraft wing, in a form of relatively sparse arrays, for simulated cracks and corrosion monitoring. The PZT discs take turns generating and receiving ultrasonic guided waves. Pair-wise through-transmission waveforms collected at normal conditions served as baselines, and subsequent signals collected at defected conditions such as rivet cracks or corrosion detected the presence of a defect and its location with a novel correlation analysis based technique called RAPID (reconstruction algorithm for probabilistic inspection of defects). The effectiveness of the algorithm was tested with several case studies in a laboratory environment. It showed good performance for defect detection, size estimation and localization in complex aircraft wing structures.

Robust backstepping control of nonlinear systems using neural networks

Abstract: A controller is proposed for the robust backstepping control of a class of general nonlinear systems using neural networks (NNs). A tuning scheme is proposed which can guarantee the boundedness of tracking error and weight updates. Compared with adaptive backstepping control schemes, we do not require the unknown parameters to be linear parametrizable. No regression matrices are needed, so no preliminary dynamical analysis is needed. One salient feature of our NN approach is that there is no need for the off-line learning phase. Three nonlinear systems, including a one-link robot, an induction motor, and a rigid-link flexible-joint robot, were used to demonstrate the effectiveness of the proposed scheme.

A Novel Cluster Kernel RX Algorithm for Anomaly and Change Detection Using Hyperspectral Images

Abstract: The Reed–Xiaoli (RX) algorithm has been widely used as an anomaly detector for hyperspectral images. Recently, kernel RX (KRX) has been proven to yield high performance in anomaly detection and change detection. In this paper, we present a generalization of the KRX algorithm. The novel algorithm is called cluster KRX (CKRX), which becomes KRX under certain conditions. The key idea is to group background pixels into clusters and then apply a fast eigendecomposition algorithm to generate the anomaly detection index. Both global and local versions of CKRX have been implemented. Application to anomaly detection using actual hyperspectral images is included. In addition to anomaly detection, the CKRX algorithm has been integrated with other prediction algorithms for change detection. Spatially registered visible and near-infrared hyperspectral images collected from a tower-based geometry have been used in the anomaly and change detection studies. Receiver operating characteristics curves and actual computation times were used to compare different algorithms. It was demonstrated that CKRX has comparable detection performance as KRX, but with much lower computational requirements.

An integrated approach to bearing fault diagnostics and prognostics

Abstract: This paper presents an integrated fault diagnostic and prognostic approach for bearing health monitoring and condition-based maintenance The proposed scheme consists of three main components including principal component analysis (PCA), hidden Markov model (HMM), and an adaptive stochastic fault prediction model The principal signal features extracted by PCA are utilized by HMM to generate a component health/degradation index, which is the input to an adaptive prognostics component for on-line remaining useful life prediction The effectiveness of the scheme is shown by simulation studies using experimental vibration data obtained from a bearing health monitoring testbed

Game Theoretic Approach to Threat Prediction and Situation Awareness

Abstract: The strategy of data fusion has been applied in threat prediction and situation awareness and the terminology has been standardized by the Joint Directors of Laboratories (JDL) in the form of a so-called JDL Data Fusion Model, which currently called DFIG model. Higher levels of the DFIG model call for prediction of future development and awareness of the development of a situation. It is known that Bayesian Network is an insightful approach to determine optimal strategies against asymmetric adversarial opponent. However, it lacks the essential adversarial decision processes perspective. In this paper, a highly innovative data-fusion framework for asymmetric-threat detection and prediction based on advanced knowledge infrastructure and stochastic (Markov) game theory is proposed. In particular, asymmetric and adaptive threats are detected and grouped by intelligent agent and Hierarchical Entity Aggregation in Level 2 and their intents are predicted by a decentralized Markov (stochastic) game model with deception in Level 3. We have verified that our proposed algorithms are scalable, stable, and perform satisfactorily according to the situation awareness performance metric.

Neural Network Control of Robot Manipulators and Nonlinear Systems

Abstract: From the Publisher: This graduate text provides an authoritative account of neural network (NN) controllers for robotics and nonlinear systems and gives the first textbook treatment of a general and streamlined design procedure for NN controllers. Stability proofs and performance guarantees are provided which illustrate the superior efficiency of the NN controllers over other design techniques when the system is unknown. New NN properties, such as robustness and passivity are introduced, and new weight tuning algorithms are presented. Neural Network Control of Robot Manipulators and Nonlinear Systems provides a welcome introduction to graduate students, and an invaluable reference to professional engineers and researchers in control systems.