Mingsian R. Bai

Application of BEM (boundary element method)-based acoustic holography to radiation analysis of sound sources with arbitrarily shaped geometries

A method employing holograms that conform with arbitrarily shaped sources has been developed for enhancing conventional near‐field acoustic holography, which has been limited to sources with simple geometries, e.g., planar or cylindrical surfaces. Four holography transformation algorithms have been developed, based on acoustic holography theory and the boundary element method (BEM). Singular value decomposition (SVD) has been incorporated into the algorithms in order to alleviate the ill‐posed nature frequently encountered in backward reconstruction of a field. A pulsating sphere, a cylinder with spherical endcaps, and a vibrating piston set in a rigid sphere have been adopted in a numerical simulation for verifying the algorithms. Satisfactory agreement has been achieved between the holographically transformed results and the analytical solutions.

Upmixing and Downmixing Two-channel Stereo Audio for Consumer Electronics

upmixing, downmixing, and joint up/downmixing are examined. Two upmixing algorithms are employed to convert two-channel stereo signals to five-channel signals. For downmixing, methods ranging from mixing with simple gain adjustment to more sophisticated Head Related Transfer Function (HRTF) filtering and Crosstalk Cancellation System (CCS) are utilized to downmix the center channel and the surround channels into the available two frontal loudspeakers. For situations where only two-channel content and loudspeakers are available, a number of up/downmixing schemes are used to simulate a virtual surround environment. Emphasis of comparison is placed on a dual- loudspeaker MP3 handset. Listening tests are conducted to compare the processing methods in terms of three levels of subjective indices. The results are processed by using the Multivariate ANalysis Of VAriance (MANOVA) to justify the statistical significance1.

An expert system for the diagnosis of faults in rotating machinery using adaptive order-tracking algorithm

This paper describes an application of an adaptive order-tracking technique for the diagnosis of faults in rotating machinery. Conventional methods of order-tracking are primarily based on Fourier analysis with reference to shaft speed. Unfortunately, in some applications of order-tracking performance is limited, such as when a smearing problem arises and also in a multiple independent shaft system. In this study, the proposed fault diagnostic system is based on a recursive least-square (RLS) filtering algorithm. The problem is treated as the tracking of various frequency bandpass signals. Order amplitudes can be calculated with high-resolution in real-time implementation. The algorithm is implemented on a digital signal processor (DSP) platform for diagnosis and evaluated by experimental investigation. An experimental investigation is implemented to evaluate the proposed system in two applications of gear-set defect diagnosis and in the diagnosis of damaged engine turbocharger blades. The results of the experiments indicate that the proposed algorithm is effective in fault diagnosis for both experimental cases. Furthermore, a characteristic analysis and experimental comparison of a vibration signal and a sound emission signal for the present algorithm are also presented in this report.

Experimental evaluation of adaptive predictive control for rotor vibration suppression

An on-line active control technique for suppressing rotor vibration is proposed. Linear voice coil motors mounted on a ball bearing housing are used for generating counter forces to cancel the transverse vibrations of shaft due to imbalance, misalignment, and so forth. Controllers are designed by using the impulse response-based model predictive control (IMPC) and generalized predictive control (GPC). Recursive-least-square (RLS) method is employed for real-time system identification. Multiple channel active control systems are implemented on the platform of a digital signal processor (DSP). Experimental evaluation indicated that the proposed methods were effective in suppressing the periodic disturbances due to constant as well as variable rotor speed. In particular, GPC has achieved the most satisfactory performance in terms of vibration attenuation and convergence speed.

Acoustical source characterization by using recursive Wiener filtering

This paper discusses a feedback iteration technique that provides the enhancement of backward reconstruction in acoustical imaging. Reconstruction of a vibrating surface motion from acoustic holographic data causes computational difficulties because the problem is ill posed. In order to deal with these difficulties, a method is developed based on a recursive algorithm, where the inverse problem is converted to a well‐posed forward propagation problem. An initial guess regarding the source images is required to activate the iterative inversion method. Then, the tentative image is propagated forward to the hologram plane and the residue is determined. Next, a feedback operator is used to process the residue by which the image is updated. Two types of feedback operators were investigated: (1) a Wiener suboptimal operator, and (2) a dynamic, optimal operator (designed subject to minimum mean‐square‐error optimization criteria). The performance of these iteration methods was investigated by numerical simulatio...

A study of fault diagnosis in a scooter using adaptive order tracking technique and neural network

An expert system for scooter fault diagnosis using sound emission signals based on adaptive order tracking and neural networks is presented in this paper. The order tracking technique is one of the important approaches for fault diagnosis in rotating machinery. The different faults present different order figures and they can be used to determine the fault in mechanical systems. However, many breakdowns are hard to classify correctly by human experience in fault diagnosis. In the present study, the order tracking problem is treated as a parametric identification and the artificial neural network technique for classifying faults. First, the adaptive order tracking extract the order features as input for neural network in the proposed system. The neural networks are used to develop the training module and testing module. The artificial neural network techniques using a back-propagation network and a radial basis function network are proposed to develop the artificial neural network for fault diagnosis system. The performance of two techniques are evaluated and compared through experimental investigation. The experimental results indicated that the proposed system is effective for fault diagnosis under various engine conditions.

Electroacoustic analysis of an electret loudspeaker using combined finite-element and lumped-parameter models

An unconventional type of electrostatic loudspeaker is presented in this paper. The loudspeaker made of thin, light, and flexible electret material lends itself well to the space-concerned applications. Electrical impedance measurement reveals that the coupling between the electrical system and the mechanical system is weak, which renders conventional parameter identification based on electrical impedance measurement impractical. A different approach is thus employed to model the electret loudspeaker. To predict the loudspeakers dynamic response, finite-element analysis (FEA) is conducted on the basis of a simple model and a full model. In the simple model, FEA is applied to model the electret membrane, leaving the rest of system as rigid parts. In the full model, FEA is applied to model the entire membrane-spacer-back plate assembly. Velocity response of the membrane subject to a uniformly distributed force is calculated using FEA harmonic analysis. Mechanical impedance is then calculated with the velocity response. The acoustical impedance due to the back cavity, pores, and the radiation loading at the front side is calculated by theoretical formulas. The volume velocity of the membrane and the resulting on-axis sound pressure level are predicted with electrical-mechanical-acoustical analogous circuits. The response data predicted by the simulation compare very well with experimental measurements.

Active Noise Control of Enclosed Harmonic Fields by Using BEM-Based Optimization Techniques

Abstract Boundary element methods (BEM) in conjunction with optimization algorithms are employed for active noise control of sound fields in three-dimensional enclosures. Total time-averaged acoustic potential energy is chosen as the cost function for the optimization procedure. The BEM is used to model the monochromatic sound field generated by noise sources in the enclosure. The constrained steepest descent method proves to be effective when both positions and amplitudes of the secondary sources are to be optimized, while the direct matrix inversion, the steepest descent method, and the conjugate gradient method prove to be useful when only the amplitudes of secondary sources are to be optimized. The developed BEM-based optimization techniques are applied to control the noise in a rectangular enclosure and a vehicle cabin.

Experimental modeling and design optimization of push-pull electret loudspeakers

A fully experimental modeling technique and a design optimization procedure are presented in this paper for push-pull electret loudspeakers. Conventional electrical impedance-based parameter identification methods are not completely applicable to electret speakers due to the extremely weak electromechanical coupling. This prompts the development of an experimental technique for identifying the electroacoustic parameters of the electret speakers. Mechanical parameters are identified from the membrane velocity measured using a laser vibrometer. The voltage-force conversion factor and the motional impedance are estimated, with the aid of a test-box method. This experimentally identified model serves as the simulation platform for predicting the response of the electret loudspeaker and optimizing the design. Optimal parameters are calculated by using the simulated annealing (SA) algorithm to fulfill various design goals and constraints. Either the comprehensive search for various parameters or the simple search for the optimal gap distance can be conducted by this SA procedure. The results reveal that the optimized design has effectively enhanced the performance of the electret loudspeaker.

Preconditioning multichannel adaptive filtering algorithms using EVD- and SVD-based signal prewhitening and system decoupling

Abstract It is well known that the convergence rate of multichannel LMS-based algorithms is limited by the correlation properties of the reference signals and the cross-coupling within the plant dynamics. These factors give rise to excessive eigenvalue spread and slow convergence rate of a gradient descent algorithm. A preconditioning technique is developed in this study for the multichannel LMS algorithm so as to improve its convergence rate. Signal prewhitening and system decoupling are the two key elements of the proposed techniques. Preconditioning filters are first formulated in the frequency domain by using eigenvalue decomposition and singular value decomposition. These filters are then transformed into the time domain with causality taken into account. The preconditioning filters are incorporated into a multichannel LMS algorithm, where the reference signals are prewhitened and the plants are decoupled prior to the adaptation process. Simulations for a two-channel/one listener cross-talk cancellation problem illustrate the effectiveness of the preconditioning technique in improving the convergence rate of the adaptive algorithms.