Chair: Norman Otto, Ford Motor Company (USA)
A.M. Zoubir, Queensland University of Technology (AUSTRALIA)
A comparative study of techniques for finding optimal sensor positions in a group of vibration sensors for knock detection in spark ignition engines is presented. The methods assume the transmission of the acoustical oscillations in the combustion chamber to the engine housing to be time invariant and linear. Based on this model, suitable multiple tests have been performed to reject sequentially irrelevant sensors from the sensor group under consideration. It was found in various simulations that two of the proposed methods that do not assume any probability distribution of the data lead to the expected results. In a real experiment performed on a test bed using a four cylinder spark ignition engine, the two methods reveal the same optimal sensor location for monitoring knock in two cylinders at three different speeds.
Sridhar Lakshmanan, University of Michigan- Dearborn
Karl C. Kluge, University of Michigan (USA)
This paper addresses the problem of detecting lane boundaries in color images of road scenes acquired from a car mounted visual sensor. It is shown that the lane boundaries in such images have to obey a set of global constraint equations. All images with such constrained lanes are modeled via deformable templates. The observed image is related to the underlying lane boundary features through a likelihood function which is based on the degree of match (in magnitude/direction) between the deformed template and the lane edges. The lane detection problem is formulated in a Bayesian setting, and it is posed as an equivalent problem of maximizing a posterior PDF which sits over a low-dimensional deformation space. This PDF is multi-modal, hence a Metropolis algorithm is employed to obtain its maximum. Experimental results are shown to illustrate the performance of this algorithm.
David A. Whitney, TASC (USA)
Bruce Broder, TASC (USA)
This paper describes a new signal processing technique for understanding the dynamics of time-varying signals in vehicles: Hyperstate analysis. Vehicle noise and vibration are examples of randomly-varying transient or non-stationary signals that are not effectively analyzed with classical spectral analysis techniques. By the use of nested Hidden Markov Models, Hyperstate analysis explicitly identifies transient and nonstationary behavior on many time scales for better signal discrimination. It uses a probability- based framework that allows for automated, objective classification of noisy signals. The technique is applied to engine starting sequence from different types of vehicles. This work demonstrates that Hyperstate analysis discerns similarities and differences in randomly-varying signals of this type, and can perform effective automatic, objective classification and signal decomposition for NVH (noise, vibration, and harshness) studies.
William B. Ribbens, University of Michigan (USA)
Steven Bieser, University of Michigan (USA)
This paper presents an application of artificial neural networks to the reliable detection of misfires in automotive engines. By government regulations, automobiles are required to be equipped with instrumentation to detect engine misfires and to alert the driver whenever the misfire rate has the potential to affect the health of emission control systems. A relevant model for the powertrain dynamics is developed in this paper as well as an explanation of the instrumentation. The basis for using a neural network to detect these misfires is explained and experimental system performance data (including error rates) are given. It is shown in this paper that the present method has the potential to meet the government mandated requirements.
Andrew Sterian, University of Michigan (USA)
Paul Runkle, University of Michigan (USA)
Gregory H. Wakefield, University of Michigan (USA)
The subjective tuning of multidimensional systems is considered. L:istener preference for certain attributes of sound over others involves multiple options, costs, and payoffs, and may require complex strategies to arrive at an optimal solution. When the complexity of such strategies becomes excessive, listeners adopt simpler strategies that may lead to poor, but achievable, solutions. Active sensory tuning (AST) is aimed at providing the listener an efficient search strategy that yields good solutions within reasonable time bounds. Within an engineering context, AST provides a direct link between the human user and the engineer's design parameters whereby the user can tune the design to their desired goal. This contrasts with current, though much simpler techniques whereby the user can rank their preference but the engineer must interpret such rankings with respect to the selected parameters.
Yong W. Kim, Ohio State University (USA)
Giorgio Rizzoni, Ohio State University (USA)
Bahman Samimy, Ohio State University (USA)
Yue Y. Wang, Ohio State University (USA)
This paper presents the application of some modern signal processing methods to the analysis of angular velocity signals in a rotating machine for diagnostic purposes. The signal processing techniques considered in this paper include: classical non-parametric spectral analysis; principal component analysis; joint time-frequency analysis; the discrete wavelet transform; and change detection algorithm based on residual generation. These algorithms are employed to process shaft angular velocity data measured from an internal combustion engine, with the intent of detecting engine misfire. The results of these analyses show that these algorithms have potential for on-board diagnostic application in the passenger and commercial vehicles, and more generally for failure detection of other classes of rotating machines.
Tony Giutsos, Artistic Analytical Methods Inc. (USA)
In recent years, the complexity of automobiles has increased sharply. Consumer demands for better performance at a low cost have caused a boom in electronic components. Many of these components require the use of signal processing techniques to provide the desired response. In this paper, we discuss signal processing for use in "smart" sensor design for automotive applications. The paper begins with a general overview of the automotive signal processing environment. It then describes a general framework for algorithm design and performance measurement. Finally, two examples of automotive algorithm design are presented: vehicle crash detection for airbag deployment and engine cylinder misfire detection to reduce environmental emissions.
H. Abut, San Diego State University
A. Bilgin, San Diego State University
R.M. Bernardi, San Diego State University
L.A. Wherry, Intelligent Transportation Systems (USA)
In this study we present our findings on image processing applications to the continuous and automatic monitoring and verification of the status of control devices and vehicle speed estimation on the Interstate-15 Reversible High Occupancy Vehicle (HOV) lanes as examples of IVHS at Work. The overall goal of this study has been to supply additional enhanced monitoring capabilities for the current HOV operations. These capabilities have been intended to assist, rather than to eliminate the human operators from the loop. In the next few paragraphs we will describe this unique undertaking together with the issues related to the systems architecture, hardware and software components, the integration, image processing tools, and preliminary field test results.