Quantifying non-linear effects in acoustic source localization

In recent years some progresses have been made in acoustic source localization (ASL) in highly anisotropic plates when the plate material properties are not known. Some simplifying assumptions in many of the earlier source localization techniques that the wave propagates along a straight line from the source to the receiving sensor is not correct for many anisotropic plates. Wave energy in anisotropic solids generally propagate along curved lines and forms non-circular wave fronts. As a result, for highly anisotropic solids the acoustic source localization techniques that assume straight-line propagation of waves from the source to the receiver are bound to give erroneous results. In this paper earlier techniques as well as new developments for acoustic source localization in homogeneous isotropic and anisotropic plates are discussed. The newest source localization techniques in anisotropic plates analyze the non-circular shapes of the wave front. Following this analysis, the acoustic source has been successfully localized in some anisotropic plates without knowing the plate properties. Limitations of the current techniques that still exist and require further investigation are discussed.

Structural health monitoring (SHM) is important for reducing the maintenance and operation cost of safety-critical components and systems in offshore wind turbines. This paper proposes anin situwireless SHM system based on an acoustic emission (AE) technique. By using this technique a number of challenges are introduced due to high sampling rate requirements, limitations in the communication bandwidth, memory space, and power resources. To overcome these challenges, this paper focused on two elements: (1) the use of anin situwireless SHM technique in conjunction with the utilization of low sampling rates; (2) localization of acoustic sources which could emulate impact damage or audible cracks caused by different objects, such as tools, bird strikes, or strong hail, all of which represent abrupt AE events and could affect the structural health of a monitored wind turbine blade. The localization process is performed using features extracted from aliased AE signals based on a developed constraint localization model. To validate the performance of these elements, the proposed system was tested by testing the localization of the emulated AE sources acquired in the field.

Location template matching (LTM) is a source localization technique in solids that is robust to dispersion and multipath. This is possible since LTM compares the input with a database of signals made at known locations. With this in place, it is possible to employ LTM in situations where the surface of interest takes an irregular shape. However, one of the existing LTM approaches uses crosscorrelation to compare the input and the database. It should be noted that if any two of the known locations stored in the database are too close, the cross-correlation method may have difficulties differentiating between signals generated from the neighboring points. To address this, we propose an algorithm which employs the linear predictive coding (LPC) that takes into account the dominant frequencies of a received signal. Using this approach, we show that the proposed algorithm is able to improve LTM's source localization accuracy under a real environment in the context of source localization for a touch interface.

Acoustic waves are well-suited for remote sensing applications and structural health monitoring purposes because they convey information about their source and can be measured using non-contacting methods. Source localization is an important structural health monitoring task; however, traditional time-of-flight array signal processing techniques used to localize acoustic sources are ill-suited for many structural engineering applications due to the potential for complicated propagation paths, the dispersive propagation of acoustic waves in structures, and the coupling of the vibrating structure and the surrounding medium. Thus, source localization experiments were conducted using matched field processing (MFP) for a 0.9-m diameter round aluminum plate excited by the impact of a 1.3-cm stainless-steel ball bearing dropped from 7.6-cm. A 14-sensor linear remote acoustic array placed 8.9-cm above the plate measured the sound radiated by the 0.64-cm thick plate. MFP array signal processing localization techniques were used along with a physics-based finite element acoustic model to localize the excitation on the structure. Source localization results in both a quiet environment and environment with additive white Gaussian noise are discussed, and these results are compared to those from an acoustic model where plate edge reflections are neglected. [Work was sponsored by a SMART Scholarship and the NEEC.]

Advances in Bayesian computational technology in the last decade have enabled the development of new source separation and source localization algorithms. These algorithms are greatly improved by the encoding of prior information about a specific problem in the form of the chosen relevant model parameters, the assignment of the likelihood functions, and the assignment of the prior probabilities of the model parameter values. I refer to such source separation algorithms as informed source separation for the reason that they are endowed with specific and often vital information about the problem. Furthermore, the Bayesian methodology allows source separation to be united with source localization simply by including the model parameters that are of interest to the researcher. Here, I will discuss the union of source separation and source localization under the Bayesian methodology, the incorporation of prior information, and the construction of an informed algorithm using the new computational technologies that allow us to estimate the values of the parameters that define these high‐dimensional problems.

Acoustic emission source localization is the basic function of the application of acoustic emission technology. For complex structures, mathematical analysis positioning algorithms cannot be obtained, and accurate acoustic emission source localization cannot be obtained, which has always been one of the problems in the actual applications of acoustic emission technology. To solve this problem, this paper proposes an acoustic emission source localization method based on deep learning, which can obtain high-precision acoustic emission source positioning without the need for mathematical analysis positioning algorithms. The AI deep learning acoustic emission source localization method adopts the method of meshing, dividing the grid in the measured structure, generating label data at the grid position, using the label data to conduct AI deep learning training to establish a positioning model, and using the trained model to analyze the actual acoustic emission data (non-labeled data) to locate the acoustic emission source. This paper uses a multi-layer perceptron model to train multi-dimensional features (arrival time and amplitude). This method effectively improves the accuracy of acoustic emission source localization. Experimental results show that the positioning accuracy of the designed deep learning model in the test set reaches 99.625%, which is significantly better than the traditional Time Difference of Arrival (TDOA) positioning algorithm. In addition, this paper further verifies the stability and reliability of the model in localization tasks through credibility metrics such as Score, Margin and Entropy. This article provides a new solution for locating acoustic emission sources in complex structures, and lays a theoretical and practical foundation for the future development of non-destructive testing technology.

For the burst-type acoustic emission signal, time delay estimation of two sensors calculated by signal cross-correlation is not accurate, and it leads the source localization results is not also accurate. A new method is proposed to improve accuracy of source localization results based on multi-scale analysis and multi-sensors. Acoustic emission signal multi-scale analysis using wavelet transform, then the most accurate time delay is selected in sub-band of signal multi-scale analysis by multi-sensors time delay vector close rule. Finally, the simulation acoustic emission source localization experiment has high accuracy.

Chapter 15 - A novel sound source localization method using a global-best guided cuckoo search algorithm for drone-based search and rescue operations

The problem of passive localization of an acoustic source is a classic area of research in underwater acoustics. While the three-dimensional problem presents difficulties, most past investigations have been able to make suitable approximations to reduce the localization to one in a plane involving two dimensions. However, acoustic source localization in anechoic tanks, pressure vessels, or other similar enclosures often requires a truly three-dimensional formulation. This paper presents the equations describing source localization in three dimensions, given time delays between four receiving channels. Numerical difficulties associated with their solution are discussed, particularly those involved in the nonlinear solution of sets of intersecting hyperboloids. An alternate formulation is presented which makes use of intersecting spheres rather than hyperboloids. This reduces the numerical complexity markedly since finding intersections of closed surfaces such as spheres is inherently more stable than for open surfaces like hyperboloids. Several possible algorithms for implementing this approach are presented, and the practical limitations of each for use with nonideal data are contrasted. Results indicate that the proposed method of intersecting spheres is numerically simpler and provides a more accurate solution for passive source localization than the method of intersecting hyperboloids. [Work partially supported by NAVSEA PMS 409 and by NAVSEA 63X5.]

Acoustic source localization in an anisotropic plate without knowing its material properties – A new approach

The material will generate elastic waves during the damage, and analyze the damage signal to achieve accurate positioning of the material damage, which is called acoustic emission (AE) damage source localization. The carbon fiber composite material plate is an anisotropic material, and the propagation speeds of the acoustic emission waves are different in different directions, and it is difficult to achieve accurate positioning of the damage by conventional methods. In this paper, a method based on wavelet neural network for acoustic emission damage source location is proposed. Firstly, the characteristics of the mode wave and the wave velocity propagation characteristics in the carbon fiber composite board are analyzed. The appropriate acoustic emission signal mode is selected as the measurement signal. Then, according to the propagation velocity of the modal wave along different directions of the carbon fiber composite plate, the relationship between the velocity and the angle of the carbon fiber is fitted; the time difference between the arbitrary position of the carbon fiber composite plate and the four sensors is obtained by the fitting speed relationship formula, and the time difference is calculated. The relationship between the time difference of all positions and the positioning position; then some data is selected as the training data, and the wavelet is passed. The damage localization model of carbon fiber composite board was established by neural network. Finally, the acoustic emission source localization experiment of carbon fiber composite board was carried out to prove the correctness of this method.

The paper considers the results of experiments on localization of the sources of geoacoustic radiation generated in near-surface sedimentary rocks. Geoacoustic signals from sources were recorded by a spaced sensor system consisting of two combined receivers and a hydrophone. The system was installed near the bottom of a natural water body (Mikizha lake) in Kamchatka. Radiation sources were located by two methods, a triangulation survey and estimation of the signal arrival time difference from spaced receivers. Coordinates for more than 40 sources were measured, and their space distribution was mapped. As the result of the experiment, it was stated that geoacoustic radiation sources are located in bottom rocks at the depths up to 2.20 ± 0.25 m at the distances of up to 8 ± 0.25 m. Localization of geoacoustic radiation sources is topical for projecting a new alarm system for the notification on the possibility of strong earthquake occurrence. The results of the analysis of the frequency of rock AE source generation and accurate estimation of their location will be the basis of this system.

Soldiers equipped with linear and nonlinear earplugs have been exposed to large impulses (weapon noises) and audiometric tests have been performed just before and after the exposures. In particular, new nonlinear perforated earplugs (allowing speech communication, detection and localization of acoustic sources) which have been developed at the French–German Research Institute of Saint-Louis are efficient for repeated exposures up to 187-dB peak when they are well fitted [Johnson and Patterson (1996) personal communication]. From various TTS studies it is concluded that: (i) the peak pressure attenuation (measured with a specially designed ATF) grossly underestimates the protection afforded by the earplugs when used in conjunction with the classical criteria for weapon noises (CHABA, MIL-STD, criterion of Pfander, criterion of Smmorenburg). The risk corresponding to an exposure to a slow rise-time impulse (i.e., under an earplug) is much lower than the risk corresponding to an exposure to an impulse of the same peak pressure with an instantaneous rise time, (ii) the LAeq8 attenuation values based on objective insertion loss measurements (ATF) give in most exposure conditions a good evaluation of the auditory hazard, but in some other cases they still underestimate the efficiency of the hearing protectors.

Field biologists use animal sounds to discover the presence of individuals and to study their behavior. The recent development of new deployable acoustic sensor platforms presents opportunities to develop automated tools for bio-acoustic field research. In this work, we demonstrate a real-time wireless sensor system that implements an AML-based source localization algorithm. We will demonstrate a system that is easy to set up and that can localize a whistle in the demonstration area in real time. This demonstration will use the techniques we described in our corresponding paper [1].

In distributed microphone arrays, each microphone is connected to a separate recording device, which requires compensation for their different clock shifts. In this paper, we propose a new type of distributed arrays, in which the microphones are moving. This mobility introduces innovation that can be used to estimate the positions of both the sound sources and the microphones, as well as compensate for the clock shifts. Estimation is done using extended Kaiman filters with heuristics that improve the convergence. We discuss the proposed method in the context of mobile robotics: each microphone is attached to a controllable mobile robot. In this scenario, the array can be used to localize sound-emitting objects in the robot's environment, while providing information about robots' positions. Results show that the error of estimating robots' positions is as low as 5 cm with the source source localization error of 6 mm and the clock synchronization error of 178 μs, for the case of 9 microphones and 5 sound sources.