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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 A large body of published research and a substantial amount of unpublished work have attempted to experimentally define the variables involved with and describe the system dynamics initiating the friction-induced self-excited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 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.

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.

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

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.

In this work, the goal of enhanced reliability through redundancy is explored. Two levels of fusion have been defined: the first is a fusion of sensors, redundant in both number and type, and the second is a statistical fusion of the resulting data at a software level. An intermediate preprocessing level is required to connect both fusions. The various types of sensors which are included are bulk micromachined flow, pressure and hydrogen sensors and a thin film poly-crystalline silicon temperature sensor. Individual sensors have been fabricated and packaged in arrays. Associated preprocessing has been designed to be able to handle all of the signals coming from each sensor and prepare them for statistical analysis. Data fusion algorithms have been written and tested.

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.

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.

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.