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A multi-point hypoid gear mesh model based on 3-dimensional loaded tooth contact analysis is incorporated into a coupled multi-body dynamic and vibration hypoid gear model to predict more detailed dynamic behavior of each tooth pair. To validate the accuracy of the proposed model, the time-averaged mesh parameters are applied to linear time-invariant (LTI) analysis and the dynamic responses, such as dynamic mesh force, dynamic transmission error, are computed, which demonstrates good agreement with that predicted by single-point mesh model. Furthermore, a nonlinear time-varying (NLTV) dynamic analysis is performed considering the effect of backlash nonlinearity and time-varying mesh parameters, such as mesh stiffness, transmission error, mesh point and line-of-action. Simulation results show that the time history of the mesh parameters and dynamic mesh force for each pair of teeth within a full engagement cycle can be simulated.

Current powertrain active noise control (ANC) systems are not sufficient enough to track the fast engine speed variations, and yield consistent convergence speeds for individual engine order such that a balanced noise reduction performance can be achieved over a broad frequency range. This is because most of these ANC systems are configured with the standard filtered-x least mean squares (FxLMS) algorithm, which has an inherent limitation in the frequency-dependent convergence behavior due to the existence of secondary path model (electro-acoustic path from the input of control loudspeaker to the output of monitoring error microphone) in the reference signal path. In this paper, an overview is given first to compare several recently modified FxLMS algorithms to improve the convergence speed for harmonic responses such as eigenvalue equalization FxLMS (EE-FXLMS) and normalized reference LMS (NX-LMS) algorithms.

Active noise control systems have been gaining popularity in the last couple of decades, due to the deficiencies in passive noise abatement techniques. In the future, a novel combination of passive and active noise control techniques may be applied more widely, to better control the interior sound quality of vehicles. In order to maximize the effectiveness of this combined approach, smarter algorithms will be needed for active noise control systems. These algorithms will have to be computationally efficient, with high stability and convergence rates. This will be necessary in order to accurately predict and control the interior noise response of a vehicle. In this study, a critical review of the filtered-x least mean square (FXLMS) algorithm and several other newly proposed algorithms for the active control of vehicle powertrain noise, is performed. The analysis examines the salient features of each algorithm, and compares their system performance.

The analytic wavelet transform (AWT) is a wavelet transform that works much like a transient Fourier transform. Therefore the AWT enables utilizing advantages of both the wavelet transform and Fourier transform. A special form of AWT developed for transient vibration and acoustics signal analyses is applied to various engineering signals in this paper. Application examples include a general time-frequency (T-F) analysis, analysis of exposures to impulsive vibrations and noises, and estimation of reverberation times. Some new definitions such as the T-F noise reduction and frequency weighted time history are defined by taking the advantage of unique capabilities of the AWT. Possible automotive applications of these new concepts are briefly discussed.

Automobile brake squeal has been experimentally studied in many ways over the past 65 years. A large body of published research and a substantial amount of unpublished work have attempted to experimentally define the variables involved with and describe the system dynamics initiating the friction-induced self-excited vibration. Much of this work has centered on pin on disk type test rigs used to characterize the contact mechanics and/or friction laws without considering the brake system influence. This paper describes a dynamometer designed and constructed to study brake squeal on a system level.

The High Pressure Oxidizer Turbine (HPOT) discharge temperature of the Space Shuttle Main Engine (SSME) was estimated using Radial Basis Function Neural Networks (RBFNN) during the startup transient. Estimation was performed for both nominal engine operation and during simulated input sensor failures. The K-means clustering algorithm was used on the data to determine the location of the basis function centers. The performance of the RBFNN is compared with that of a feedforward neural network trained with the Quickprop learning algorithm.

Building and operating dependable systems is fundamental to many critical applications, such as designing integrated hardware and software systems for vehicles or satellites. Dependable systems techniques, methods, and tools are developed and used by researchers and practitioners working in widely varying disciplines. In order to provide a unifying framework for the successful dissemination and sharing of dependability results, the development of a dependable systems knowledge base is underway.1 Two database support subsystems are under development: one that manages the storage and retrieval of document information, as well as communicating between the user interface layer and the physical database layer, and another that manages the lexicon of dependability terminology for the user interface layer. The system will provide access to information in a sophisticated, intelligent manner that enables a human user to function more effectively in learning and decision-making capacities.

Looking forward to an autonomous Unmanned Combat Aerial Vehicle (UCAV) for future applications, it becomes apparent that on-board intelligent controllers will be necessary for these advanced systems. LETHA (Learning Enhanced Tactical Handling Algorithm) was created to develop intelligent managers for these advanced unmanned craft through the novel means of a genetic cascading fuzzy system. In this approach, a genetic algorithm creates rule bases and optimizes membership functions for multiple fuzzy logic systems, whose inputs and outputs feed into one another alongside crisp data. A simulation space referred to as HADES (Hoplological Autonomous Defend and Engage Simulation) was created in which LETHA can train the UCAVs intelligent controllers.

A new algorithm to detect and to quantify the seriousness of the detected squeak and rattle (S&R) events was developed. A T-F analysis technique called AWT, the Zwicker loudness model and leaky integration are employed to define new concepts we called transient specific loudness time histories and perceived transient loudness time history. The detection threshold of the perceived transient loudness was identified by a clever interpretation of jury test results. The proposed algorithm showed a good promise producing results that are well correlated with the jury tests. The new algorithm developed in this work will be able to automate detection and rating of the S&R events with good accuracy and with minimum possibility of false alarm under normal operating conditions