Acoustics Australia最新文献

Sound radiation from thin metal plates has consistently been recognized as a severe noise problem. One of the most popular approaches to suppressing this noise is applying viscoelastic layers, also called free layer damping (FLD), on the plate surface, which can damp the structural motion and minimize the radiated sound. The thickness of the FLD is an important parameter. It needs to be optimized for the target acoustic limits through numerical simulations, as the total mass and the costs may rise unnecessarily. This paper investigates the sound radiation from thin metals of particular sizes with different thickness values of FLD. A unique test setup was established to measure vibration and sound for three different sized plates, with each one having three different FLD thicknesses, namely, 0.5 mm, 0.75 mm, and 1 mm. In parallel, vibro-acoustic analyses were performed for the same configurations using the finite element method. The damping of the FLD was defined using the Rayleigh damping model, of which coefficients were obtained through a prediction formula developed earlier by the authors. After validating the model with the test, the effect of FLD on the extended acoustic parameters (radiated sound power, directivity) was also analyzed.

Acoustic attenuation of a hybrid sonic crystal made with periodic cylindrical scatterers and cascaded porous panels in a broad frequency range is endeavoured in this paper. It is observed via simulations that, the insertion loss (IL) of hybrid configuration is larger than the summation of IL of individual contributors such as periodic scatterers and parallel porous panels in post first Bragg resonance frequency band. The key finding of the research is that the passband in post first Bragg resonance is turning to stopband on introducing the cascaded porous panels within scatterers. Other configurations such as periodic array of cylindrical scatterers in series with porous panels in upstream, downstream and bounded with porous panels are examined and compared. The potential of said claim is shown by investigating a multi-resonant array of scatterers with cascaded porous panels. Finally, the experimental results are presented to authenticate the observed findings of simulations.

The number of research papers dealing with vibration-based condition monitoring has been exponentially growing in recent decades. As a consequence, one may identify some trends that emerge from this vast literature. The present paper delineates a methodology that can be recognized in several research works, which is rooted in a succession of three stages. The first stage embodies a linear transform of the data, typically in the form of a filterbank, the second stage reduces the dimension of the data through a nonlinear functional, typically in the form of health indicators, and the last stage supplies a statistical decision. Although several variants of this methodology exist, its fundamental principles seem to have converged to a general consensus, at least implicitly. This paper provides a critical overview of this methodology. It discusses its working assumptions under some typical scenarios and formulates several caveats. It also provides a few prospects that may nourish future research.

This paper discusses trends in condition monitoring of modern offshore wind turbines. First an overview is given of design changes that have been made over the years to large offshore wind turbines and how this resulted in novel opportunities from a monitoring perspective. Similarly, the evolution in data source availability is discussed. From these opportunities, some ongoing research activities in the field are discussed and how they fit with the open challenges. This list is far from exhaustive. It gives an overview of some capita selecta. Particularly, the fields of advanced signal processing and requirement for innovations towards prognostic frameworks are highlighted.

This paper provides an overview of the recent advances made in the field of fault diagnosis of industrial machines operating under variable speed conditions. First, the shortcomings of the traditional techniques in extracting reliable fault information are laid down, followed by a discussion on the different approaches adopted to overcome these issues. Next, these approaches are discussed by categorizing them as resampling based and resampling free methods. The principle and implementation procedures of these methods are discussed by summarizing the key literature in this area. Finally, the paper is concluded by highlighting the future challenges to address in this area.

With the huge success of applying deep learning (DL) methodologies to image recognition and natural language processing in recent years, researchers are now keen to use them in the machine condition monitoring (MCM) context. There are numerous papers in applying various DL techniques, such as auto-encoder, restricted Boltzmann machine, convolutional neural network and recurrent neural network, to MCM problems ranging from component level condition monitoring (machine tool wear prediction, bearing fault diagnosis and classification and hydraulic pump fault diagnosis) to system level health management (aircraft and spacecraft diagnosis). In this paper, we give a brief overview in the area of DL for MCM with a focus on reviewing the most recent papers published since 2019. In Part 1, we present some critical views regarding whether any breakthrough has been achieved from an MCM domain expert perspective, with the main conclusion that DL has great potential for MCM applications and a major breakthrough could come soon since the shortfalls lie more in data than in the DL methodologies. Our overall impression is that (a) DL models are not really showing their great potentials with only a small training data; (b) faulty-condition data is hard to come by for training DL, but normal condition data is abundant, so anomaly detection makes more sense; (c) applying DL only to the Case Western Reserve University (CWRU) bearing fault dataset is not sufficient for real-world industrial applications as it was from a very simple test rig, and applying DL to data from complex systems like helicopter gearbox data may deliver much more convincing results. In Part 2, we enhance the main conclusion of the critical review with supplement views and a case study on analyzing Bell-206B helicopter main gearbox planet bearing failure data using some traditional MCM techniques in contrast to applying the long short-term memory (LSTM) DL method. We can conclude from the case study that the DL-based methods are not necessarily always superior to the traditional MCM techniques for dataset from moderately complex machinery.

A simplified finite element method (FEM) simulation method has been established and validated for analyzing the sound absorption mechanism of structures with periodic axisymmetric cavities. Combined with genetic algorithm, the simplified FEM method is used to optimize the sound absorption coefficient of the structure containing periodic cylindrical cavities and variable cross section cavities. The result of variable section cavities is much better than the case of cylindrical cavities. The effect of cavity shape on sound absorption mechanism is discussed through energy dissipation, structure deformation and modal analysis of the absorption structures. It is found that the cavity structure resonances include bending vibration of the surface layer and radial motion of particles near the cavities. The radial motion also changes along the axial direction. Adding geometric design parameters of the cavity cross section are conducive to moving the radial mode to low frequency. The radial vibration has a great influence on absorption performance, which is more conducive to promoting the conversion of longitudinal waves into transverse waves with more energy dissipation. Finally, a better sound absorption performance is obtained by introducing the material parameter of Young's modulus into the optimization model, indicating that comprehensive consideration of geometry and material parameters for optimization is expected to obtain the desired sound absorption structure in engineering practice.

With the huge success of applying deep learning (DL) methodologies to image recognition and natural language processing in recent years, researchers are now keen to use them in the machine condition monitoring (MCM) context. There are numerous papers in applying various DL techniques, such as auto-encoder, restricted Boltzmann machine, convolutional neural network and recurrent neural network, etc., to MCM problems ranging from component-level condition monitoring (machine tool wear prediction, bearing fault diagnosis and classification and hydraulic pump fault diagnosis) to system-level health management (aircraft and spacecraft diagnosis). In this paper, we give a brief overview in the area of DL for MCM with a focus on reviewing the most recent papers published since 2019. In Part 1, we present some critical views regarding whether any breakthrough has been achieved from an MCM domain expert perspective, with the main conclusion that DL has great potential for MCM applications, and a major breakthrough could come soon since the shortfalls lie more in data than in the DL methodologies. Our overall impression is that (a) DL models are not really showing their great potentials with only a small training data; (b) faulty-condition data is hard to come by for training DL, but normal condition data is abundant, so anomaly detection makes more sense; (c) applying DL only to the Case Western Reserve University (CWRU) bearing fault dataset is not sufficient for real world industrial applications as it was from a very simple test rig, and applying DL to data from complex systems like helicopter gearbox data may deliver much more convincing results. In Part 2, we enhance the main conclusion of the critical review with supplement views and a case study on analysing Bell-206B helicopter main gearbox planet bearing failure data using some traditional MCM techniques in contrast to applying the long short-term memory (LSTM) DL method. We can conclude from the case study that the DL-based methods are not necessarily always superior to the traditional MCM techniques for dataset from moderately complex machinery.

Helmholtz resonator is considered and widely accepted as a basic acoustic model in engineering applications and research. In this paper, the normal incidence sound absorption characteristics of series and parallel configurations of Helmholtz resonators is studied analytically, numerically and experimentally. The proposed analytical model for series configuration of HRs comprises of Johnson–Champoux–Allard model and transfer matrix method while parallel configuration of HRs is described using parallel transfer matrix method. The results from proposed analytical models fit well with the finite element method (FEM) results obtained from COMSOL multiphysics. Incorporation of parallel configuration and proper tuning of geometric parameters helps to overcome the trade-off between broad band sound absorption and minimum space utilization. Also, the experimental observations of one of the parallel configuration substantiates the FEM results. Moreover, the FEM models are more accountable for the variation in neck position and also provide better visualization of acoustic absorption with frequency.

To expand the spatial and temporal scales of passive acoustic monitoring of animals, automatically detecting target sounds among noises with similar acoustic properties is essential but challenging. In particular, the classification of tonal vocalisations and tonal noise remains a universal problem in bioacoustics research. The vocalisations of dugong, which is an endangered marine mammal that inhabits coastal seas, need to be monitored to enhance our understanding of its habitat use. However, detecting dugong tonal vocalisations is difficult due to the presence of tonal noise in the same frequency band. In this study, a classification method was developed for these signals to handle large acoustic data by reducing the labour required for manual inspection. Mel-frequency cepstral coefficients (MFCC) were extracted to characterise background sounds along with a few parameters of the signal contour, and a support vector machine was trained for binary classification. The classifier achieved an 84.4% recall and a 93.5% precision on the testing dataset even in a noisy shallow marine environment. This methodology enables the effective classification of dugong calls and similar tonal noises by combining contour and MFCC features and can extend the spatial and temporal scale of acoustic monitoring of the endangered dugong. This technique is potentially applicable to the monitoring of other endangered marine mammals that produce tonal vocalisations.