Showing papers by "Bo Chen published in 2011"

A hybrid immune model for unsupervised structural damage pattern recognition

Abstract: This paper presents an unsupervised structural damage pattern recognition approach based on the fuzzy clustering and the artificial immune pattern recognition (AIPR). The fuzzy clustering technique is used to initialize the pattern representative (memory cell) for each data pattern and cluster training data into a specified number of patterns. To improve the quality of memory cells, the artificial immune pattern recognition method based on immune learning mechanisms is employed to evolve memory cells. The presented hybrid immune model (combined with fuzzy clustering and the artificial immune pattern recognition) has been tested using a benchmark structure proposed by the IASC-ASCE (International Association for Structural Control-American Society of Civil Engineers) Structural Health Monitoring Task Group. The test results show the feasibility of using the hybrid AIPR (HAIPR) method for the unsupervised structural damage pattern recognition.

An interdisciplinary program for education and outreach in hybrid & Electric Drive Vehicle Engineering at Michigan Technological University

Abstract: The automotive industry is in a transformation towards powertrain electrification, requiring automotive engineers to develop and integrate technologies from multiple disciplines. Michigan Technological University is rolling out a new program in interdisciplinary master of engineering degree and graduate and undergraduate certificates in Advanced Electric Drive Vehicle Engineering. Distinctively we are focusing our education program at the vehicle level and the interconnection to the electric grid. The vehicle level aspects of the program include vehicle requirements, integration of propulsion technologies, safety, diagnostics, control and calibration. Michigan Tech and our industrial partners see these as critical limiting factors in the development and production of advanced electric transportation systems. Additionally, the effort leverages the existing distance learning program in electric power. The result are an interdisciplinary program that meets the needs of the transportation and power industries and provides students with a unique skill set that will accelerate the Introduction

Stochastic Knock Detection Model for Spark Ignited Engines

Abstract: This report presents the development of a Stochastic Knock Detection (SKD) method for combustion knock detection in a spark-ignition engine using a model based design approach. Knock Signal Simulator (KSS) was developed as the plant model for the engine. The KSS as the plant model for the engine generates cycle-to-cycle accelerometer knock intensities following a stochastic approach with intensities that are generated using a Monte Carlo method from a lognormal distribution whose parameters have been predetermined from engine tests and dependent upon spark-timing, engine speed and load. The lognormal distribution has been shown to be a good approximation to the distribution of measured knock intensities over a range of engine conditions and spark-timings for multiple engines in previous studies. The SKD method is implemented in Knock Detection Module (KDM) which processes the knock intensities generated by KSS with a stochastic distribution estimation algorithm and outputs estimates of high and low knock intensity levels which characterize knock and reference level respectively. These estimates are then used to determine a knock factor which provides quantitative measure of knock level and can be used as a feedback signal to control engine knock. The knock factor is analyzed and compared with a traditional knock detection method to detect engine knock under various engine operating conditions. To verify the effectiveness of the SKD method, a knock controller was also developed and tested in a model-in-loop (MIL) system. The objective of the knock controller is to allow the engine to operate as close as possible to its borderline spark-timing without significant engine knock. The controller parameters were tuned to minimize the cycle-to-cycle variation in spark timing and the settling time of the controller in responding to step increase in spark advance resulting in the onset of engine knock. The simulation results showed that the combined system can be used adequately to model engine knock and evaluated knock control strategies for a wide range of engine operating conditions.

Agent Recommendation for Agent-Based Urban-Transportation Systems

Abstract: Mobile-agent technology has been adopted in many transportation fields to take advantages of different agents to deal with dynamic changes and uncertainty in traffic environments. However, few research studies have been conducted in urban-transportation systems on decision making about what kind of agents to be used in coping with a specific traffic states. With the increasing availability of control and service agents for agent-based urban-transportation systems, an agent recommendation system is necessary to manage and select those agents so original objectives can be fulfilled. In this article, the authors address issues related to the creation of such a platform.

Emergent damage pattern recognition using immune network theory

Abstract: This paper presents an emergent pattern recognition approach based on the immune network theory and hierarchical clustering algorithms. The immune network allows its components to change and learn patterns by changing the strength of connections between individual components. The presented immune-network-based approach achieves emergent pattern recognition by dynamically generating an internal image for the input data patterns. The members (feature vectors for each data pattern) of the internal image are produced by an immune network model to form a network of antibody memory cells. To classify antibody memory cells to different data patterns, hierarchical clustering algorithms are used to create an antibody memory cell clustering. In addition, evaluation graphs and L method are used to determine the best number of clusters for the antibody memory cell clustering. The presented immune-network-based emergent pattern recognition (INEPR) algorithm can automatically generate an internal image mapping to the input data patterns without the need of specifying the number of patterns in advance. The INEPR algorithm has been tested using a benchmark civil structure. The test results show that the INEPR algorithm is able to recognize new structural damage patterns.

Bio-Inspired Computing Algorithms for Adaptive Structural Health Monitoring

Abstract: This paper studies a bio-inspired pattern recognition algorithm for the structural damage detection and classification in changing environments. Biological system such as natural immune system is a remarkable distributed information processing system. It uses feature extraction, signaling, learning, and memory mechanisms to solve pattern recognition and classification problems. Artificial-immune-systembased damage pattern recognition algorithms detect damage patterns by examining the deviations of real-time sensor data from a normal pattern model (a set of representative feature vectors for the normal pattern). To enhance the adaptability of the presented algorithm, the normal pattern model is updated when the temperature changes. The performance of the updating of the normal pattern model is evaluated using environmental monitoring data collected by the SIMCES (System Identification to Monitor Civil Engineering Structures) project. The validation result shows that the updating of the normal pattern model increases the success rates of the damage detection.

Comparison study of feature extraction methods in structural damage pattern recognition

Abstract: This paper compares the performance of various feature extraction methods applied to structural sensor measurements acquired in-situ, from a decommissioned bridge under realistic damage scenarios. Three feature extraction methods are applied to sensor data to generate feature vectors for normal and damaged structure data patterns. The investigated feature extraction methods include identification of both time domain methods as well as frequency domain methods. The evaluation of the feature extraction methods is performed by examining distance values among different patterns, distance values among feature vectors in the same pattern, and pattern recognition success rate. The test data used in the comparison study are from the System Identification to Monitor Civil Engineering Structures (SIMCES) Z24 Bridge damage detection tests, a rigorous instrumentation campaign that recorded the dynamic performance of a concrete box-girder bridge under progressively increasing damage scenarios. A number of progressive damage test case data sets, including undamaged cases and pier settlement cases (different depths), are used to test the separation of feature vectors among different patterns and the pattern recognition success rate for different feature extraction methods is reported.