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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.

An experimental method to identify damping characteristics of a dynamic system is reported. The method identifies damping matrices of the equation of motion of the system from measured frequency response functions, each different damping mechanism in a distinct matrix. Related experimental techniques and signal processing issues are discussed. Theoretical validation and error study are conducted by applying the method to a theoretical example. The method is applied experimentally to a thin beam with two different damping characteristics for experimental validation and demonstration of the method. Important advantages of the method over existing methods are explained.

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.

Efforts have been made to utilize Al constructs to identify flaws in the Space Shuttle Main Engine (SSME) faceplate regions. In order to expand the applicability of these algorithms to a larger problem domain, the automatic visual inspection system(AVIS) has been modified to enable a user with little or no image processing background to define a system capable of identifying flaws on a given set of imagery. This system requires the user to simply identify flawed regions and the selection of processing and feature descriptors is performed automatically. This paper explicates the motivations, definitions, and performance issues associated with the AVIS paradigm.

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.

Acoustic array techniques are presented as alternatives to intensity measurements for source identification in automotive and industrial environments. With an understanding of the advantages and limitations described here for each of the available methods, a technique which is best suited to the application at hand may be selected. The basic theory of array procedures for Nearfield Acoustical Holography, temporal array techniques, and an Inverse Frequency Response Function technique is given. Implementation for various applications is discussed. Experimental evaluation is provided for tire noise identification.

In highly reverberant enclosures, the identification of noise sources is a difficult and time consuming task. One effective approach is the Inverse Frequency Response Function (IFRF) method. This technique uses the inverse of an acoustic FRF matrix, that when multiplied by operating pressure response data reveals the noise source locations. Under highly reverberant conditions the deployment of a sound absorbing body is especially useful in reducing the effects of resonant modes that obscure important information in the FRFs. Without the absorption, the IFRF method becomes practically difficult to perform in these environments due to poor conditioning of the FRF matrix. This study investigates the feasibility of using Boundary Element and Finite Element Methods to establish the frequency response functions between selected panel points and microphones in the array.

As the drive to make automobiles more noise and vibration free continues, it has become necessary to analyze transient events as well as periodic and random phenomena. Averaging of transient events requires a repeatable event as well as an available trigger event. Knowing the exact event time, the data can be post-processed by re-sampling the time scale to capture the recorded event at the proper instant in time to allow averaging. Accurately obtaining the event time is difficult given the sampling restrictions of current data acquisition hardware. This paper discusses the ideal hardware needed to perform this type of analysis, and provides analytical examples showing the transient averaging improvements using time scale re-sampling. These improvements are applied to noise source identification of a single transient event using an arrayed microphone technique. With this technique, the averaging is performed using time delays between potential sources and microphones in the array.

A manual task can be performed based on alternative movement techniques. Ergonomic human motion simulation requires consideration of alternative movement techniques, because they could bring different biomechanical, physiological, and psychophysical consequences. A method for identifying movement techniques from existing motion data was developed. The method is based on a JCV (Joint Contribution Vector) index and statistical clustering. A JCV quantifies a motion's underlying movement technique by computing contributions of individual body joint DOFs (degree-of-freedom) to the achievement of the task goal. Given a set of motions (motion capture data) achieving the same or similar task goals, alternative movement techniques can be identified by 1) representing the motions in terms of JCV and 2) performing a statistical clustering analysis. Performance of this movement technique identification method was evaluated based on a set of stoop and squat lifting motions.

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