炉膛声波飞行时间测量中声源信号的选取研究

炉膛内声波飞行时间的精确测量是实现声学法炉膛温度场分布测量技术的关键,而选取一种具有较强抗噪声干扰能力的声源信号是精确测量飞行时间的前提.针对适合于互相关函数法测量声波飞行时间的几种典型的声源信号,在不同信噪比下进行仿真实验比较,结果表明,线性扫频信号在相关法声波飞行时间测量中具有更强的抗噪声干扰能力;通过对不同声源信号进行功率谱分析得出,在采用互相关函数测量炉膛内声波飞行时间时,信号的抗噪声干扰能力不仅与信号的频率范围有关,而且还取决于其相对于噪声的有效频带.     

超声波液体密度测量的一种声反射系数测定方法

用超声波测液体密度时,测量声反射系数是重点.提出一种将半波层反射模型简化后,利用相对稳态幅度测量声压反射系数的方法.将一个较薄固体作为半波层置于被测液体中,两侧对称地放置超声波换能器,换能器一个工作在脉冲回波模式,另一个工作在接收模式.调整信号频率使半波层和被测液体交界面的多重反射波达到最大程度干涉,此时被测液体与半波层之间的声反射系数只与两个换能器在稳态下的回波信号幅度比值有关.因此测得换能器的稳态回波信号幅度比,即可得到声反射系数.此方法简化了原来的半波层反射模型,采取对称布局,利用相对稳态幅度法,简化了理论推导过程,降低了测量上的精度要求.最后文章通过实验验证了方法的正确性.     

Aircraft Classification and Acoustic Impact Estimation Based on Real-Time Take-off Noise Measurements

The acoustic impact of aircraft taking-off is an important subject for monitoring and research. It is very useful to analyze the type or class of aircraft that produces high level noises based on take-off characteristics. This paper presents a new method about aircraft classification and the acoustic impact estimation, in areas near an airport, based on real time noise measurement for each take-off. The noise measurements are made with sampling frequency of 50 ks/s (kilo samples per second) and 24-bit resolution analog-to-digital conversion, during 24 s. The aircraft identification is made through a model of two parallel feed-forward neural network combined with a weighted addition. In order to generate the inputs to the neural networks, the noise signal features were obtained from the auto-regressive model and the 1/12 octave analysis. The aircraft is grouped into categories or classes depending on the installed engine type. This system has 13 aircraft categories and an identification level above 80% in real environments. Noise signals, generated during aircraft take-off are measured in a fixed location on the airport runway end using a linear 4-microphone array. The acoustic impact is presented by means of a noise map for each take-off and displays four layers related to four take-off time intervals. Based on International Organization for Standardization, each time interval is represented by an equivalent point sound source location through the estimation of time-difference-of-arrival of the acoustic wave from aircraft taking-off.     

改进的四阶累积量算法在声学测温中的应用

准确的声波飞渡时间在声学测温中具有非常重要的意义,但是不同容量、不同负荷、不同时刻下运行的锅炉都可能使得背景噪声发生变化。现场炉膛背景噪声的不断变化引起了信噪比的不断变化。指出了声学测温中几种传统的时延估计方法的不足,通过改进的四阶累积量算法,实现了低信噪比、信噪比变化环境下的准确声波飞渡时间的测量。     

基于立体声信号的声源定位算法研究

提出了一种基于立体声信号的声源定位算法.算法主要分为两个步骤:先进行声达时间差估计,并从中获取传声器阵列中阵元间的声延迟;再利用荻取的声达时间差,结合已知的传声器阵列的空间位置和声速进一步定出具体声源的位置.实践结果表明,该算法是一种准确度高、稳定性好的声源定位方法,并且更加有利于实时声信号的处理.     

道旁声学信号多普勒畸变校正

采用道旁声学信号来分析列车滚动轴承故障的方法是该领域故障诊断发展的主要方向,但拾音器和声源的相对运动造成的多普勒效应使得声学信号的频谱发生了畸变,从而无法准确地反映设备状态。为了解决多普勒效应带来的采集信号频谱畸变,准确恢复原信号频谱结构,本文提出一种基于频偏率的变采样技术的方法,在通过外部条件获得信号频偏变化曲线后,根据频偏曲线获得每个采样点的频偏比例,利用变采样技术,通过插值方法获得新采样信号。本文是对作者所提重采样技术校正多普勒频偏方法的深化和提高,该方法能准确恢复原声学信号频偏结构,并通过仿真信号和实验验证了该方法的有效性。     

声发射技术在立柱缸体裂纹监测中的应用研究

针对立柱缸体在做强度试验和型式试验时出现的缸体破裂现象,提出了一种基于声发射技术的立柱缸体裂纹监测方案。该方案采用声发射技术捕捉、分析、处理和显示立柱在受压过程中产生的裂纹弹性波;采用时差定位法获得声发射源位置;采用小波包技术处理声发射信号,以进一步提高了定位精度。测试结果表明,声发射技术用于检测立柱缸体裂纹、焊缝等隐形机理的产生、发展和进行危险性分析是可行的。     

基于单声学矢量传感器的最大能量动态声源波达方向跟踪算法

针对在空域非均匀噪声环境下目标定位跟踪问题,提出一种基于单声学矢量传感器的最大能量动态声源波达方向(DOA)跟踪算法.首先结合噪声协方差矩阵估计结果实现对传感器接收信号的预白化处理,进而确定加权参数值,提出一种加权参数固化的最大能量算法,从而在声压与振速域噪声功率比未知的条件下提高了DOA估计精度.在此基础上,利用最大能量定向估计子输出信息来构建运动目标的量测方程,并在容积Kalman滤波框架下实现对于动态声源的状态跟踪.理论分析与仿真结果验证了算法的可行性和有效性.     

基于时间反转聚焦的声发射源定位算法

声发射检测可以判断损伤的活性和严重性而被广泛应用,但对损伤位置仍没有更准确的定位方法。提出一种基于时间反转理论的声发射源准确定位的方法。根据声发射被动监测的特点,研究采用时间反转聚焦方法使声发射信号能量叠加放大,提高信号的信噪比,并分析推导了具体损伤声源信号时间反转聚焦增强过程;利用时间反转对声发射源的自适应聚焦能力,重建信号传播波动图,通过信号聚焦显示出损伤声源位置和区域;最后通过数值仿真对该方法进行验证,仿真结果表明能有效提高损伤声源信号的能量,能够对检测区域的信号重建,并通过显示准确给出损伤声源位置,定位精度较常规时差定位法有较大提高。     

Near field acoustic localization under unfavorable conditions using feedforward neural network for processing time difference of arrival

Using time difference of arrival (TDOA) is one of the two approaches that utilize time delay for acoustic source localization. Combining the obtained TDOAs together with geometrical relationships within acoustic components results in a system of hyperbolic equations. Solving these hyperbolic equations is not a trivial procedure especially in the case of a large number of microphones. The solution is additionally compounded by uncertainties of different backgrounds. The paper investigates the performance of neural networks in modelling a hyperbolic positioning problem using a feedforward neural network as a representative. For experimental purposes, more than 2000 sound files were recorded by 8 spatially disposed microphones, for as many arbitrarily chosen acoustic source positions. The samples were corrupted by high level correlated noise and reverberation. Using cross-correlation, with previous signal pre-processing, TDOAs were evaluated for every pair of microphones. On the basis of the obtained TDOAs and accurate sound source positions, the neural network was trained to perform sound source localization. The performance was examined using a large number of samples in terms of different acoustic sensors setups, network configurations and training parameters. The experiment provided useful guidelines for the practical implementation of feedforward neural networks in the near-field acoustic localization. The procedure does not require substantial knowledge of signal processing and that is why it is suitable for a broad range of users.     

基于声发射传感器与ChiMerge粗糙集的埋地水管泄漏检测

针对水管泄漏检测,为了从不完整的信息中得出满意的诊断结果,提高泄漏识别率,提出了一种基于声发射传感器与ChiMerge粗糙集的埋地水管泄漏识别方法.这种方法首先用声学传感器采集数据,然后用EMD(经验模态分解方法)和能量特征提取相结合的方法建立决策表,通过ChiMerge算法将决策表离散化,最后通过粗糙集的约简来识别水管泄漏.实验表明,该方法是有效的.     

基于多通道声发射检测系统的管道气体泄漏位置定位方法研究

为了自主开发基于声发射检测原理的管道气体泄漏位置定位系统,研制了基于四通道声发射检测系统的管道气体泄漏模拟实验装置。在测定声波沿管壁传播速度基础上开展了管道气体泄漏检测实验,通过改变声发射传感器与泄漏孔的在管道壁面的相对位置来模拟泄漏孔位置的变化。采用小波去噪和滑动平均滤波方法对所采集的泄漏信号进行了处理,利用互相关算法求取了各传感器之间的延迟时间,进而基于时间差提出了泄漏孔的定位算法。实验结果表明模拟管道上泄漏孔的定位结果相对误差低于4.0%,该系统对泄漏位置的定位精度满足行业标准和国家标准的要求。通过优化设计声发射信号的采样频率、管道材质、管道壁厚和传感器端面面积等能够进一步提高定位的准确度。     

Closed-form solution to motion parameter estimation of an acoustic source exploiting Doppler effect

This paper proposes an analytical solution for the parameter estimation of a moving acoustic source from the instantaneous frequency of a recorded signal on a ground located microphone. The acoustic source could be either a propeller-driven aircraft or a helicopter. During the transit of such acoustic source, the Doppler effect is exploited to estimate the speed, altitude, source frequency and the closest point of approach (CPA) time between the source and the microphone. The proposed solution is an original workout of an existing time–frequency based approach which has been applied previously to localize a propeller-driven aircraft. In contrast to existing solutions, the main advantage of the proposed one is the direct estimation of the flight parameters without resorting to any iterative procedure. The proposed approach is faster, less complex and therefore a good candidate for hardware implementation. The proposed solution is compared with respect to the existing approach in term of parameter estimation, performance and computation complexity. Experimental validations of the proposed solution are also presented.     

基于SAW器件的微间隙压力监测传感器

声表面波SAW(Surface Acoustic Wave)器件能进行无源无线通信,为微间隙等特殊环境下物理量的测量提供了新的解决思路.研究设计了一种基于声表面波延迟线的接触应力传感器.围绕微间隙环境,研究了声表面波器件的结构类型,并确定压力监测的技术方案;根据设计原理,设计一种新型声表面波传感器,并利用有限元分析法对压电基片进行应力仿真;将设计出的传感器进行实验测试并提出温度补偿.通过对声表面波传感器设计的探究,验证了利用声表面波传感器实现微间隙压力监测的可行性.     

基于互相关时间延迟估计的心音源定位方法

提出一种用于正常噪声环境下的心音源定位方法。利用3个传感器同步记录胸前3个位置的心音信号,对自动识别出的心音成分采用互相关法,估计同一心音源发出的信号到达2个传感器的时间延迟。利用时间延迟和传感器坐标确定心音源在传感器连线上的投影点坐标,由投影点与心音源的关系定位心音源。实验结果显示,对于采集到的健康人心音信号,利用该方法可以定位第一心音源。     

块修正插值技术在超声波温度场测量中的应用

如何利用简单的测量系统实现精确的温度场测量,并快速而准确地重建温度场,重建算法的研究很重要。为了实现少量的声波渡越时间(ToF)较高精度的重建温度场,提出了一种新型的基于块修正插值技术的空气温度场重建算法,在被测温度场周围布置8只收发一体的超声波传感器,并将被测温度场划分为100个小区间,利用该新方法对实验室内的单热源空气温度场进行重建,重建结果与被测量的空气温度场符合。     

An advanced hybrid method for the acoustic prediction

The present article proposes an advanced methodology for numerically simulating complex noise problems. More precisely, we consider the so-called multi-stage acoustic hybrid approach, which principle is to couple sound generation and acoustic propagation stages. Under that approach, we propose an advanced hybrid method which acoustic propagation stage relies on Computational AeroAcoustics (CAA) techniques. To this end, first, an innovative weak-coupling technique is developed, which allows an implicit forcing of the CAA stage with a given source signal coming from an a priori evaluation, whether the latter evaluation is of analytical or computational nature. Then, thanks to additional innovative solutions, the resulting CAA-based hybrid approach is optimized so that it can be applied to realistic and complex acoustic problems in an easier and safer way. All these innovative features are then validated on the basis of an academic test case, before the resulting advanced CAA-based hybrid methodology is applied to two problems of flow-induced noise radiation. This demonstrates the ability of the here proposed method to address realistic problems, by offering to handle at the same time both acoustic generation and propagation phenomena, despite their intrinsic multiscale character.     

Three dimensional passive acoustic spatial identification of nucleations in superheated droplet detectors

A new method for spatial localization of bubble nucleations in superheated droplet detectors is presented which has two steps: validation and localization. Validation is accomplished through signal processing techniques serving the purpose of filtering out electromagnetic noise and gaseous micro-leaks. The 3D spatial localization uses a passive acoustic sound source localization technique using a microphone array (5 elements) and a simple generalised cross-correlation (GCC) time delay of arrival (TDOA) algorithm. The approach for the nucleation validation is new and regarding the localization, as of this writing, we are the first to endeavour its feasibility assessment. Experimental results are shown.     

Narrowband signal detection techniques in shallow ocean by acoustic vector sensor array

This paper presents the formulation and performance analysis of four techniques for detection of a narrowband acoustic source in a shallow range-independent ocean using an acoustic vector sensor (AVS) array. The array signal vector is not known due to the unknown location of the source. Hence all detectors are based on a generalized likelihood ratio test (GLRT) which involves estimation of the array signal vector. One non-parametric and three parametric (model-based) signal estimators are presented. It is shown that there is a strong correlation between the detector performance and the mean-square signal estimation error. Theoretical expressions for probability of false alarm and probability of detection are derived for all the detectors, and the theoretical predictions are compared with simulation results. It is shown that the detection performance of an AVS array with a certain number of sensors is equal to or slightly better than that of a conventional acoustic pressure sensor array with thrice as many sensors.     

A stereophonic acoustic signal extraction scheme for noisy and reverberant environments

In this contribution, a novel two-channel acoustic front-end for robust automatic speech recognition in adverse acoustic environments with nonstationary interference and reverberation is proposed. From a MISO system perspective, a statistically optimum source signal extraction scheme based on the multichannel Wiener filter (MWF) is discussed for application in noisy and underdetermined scenarios. For free-field and diffuse noise conditions, this optimum scheme reduces to a Delay & Sum beamformer followed by a single-channel Wiener postfilter. Scenarios with multiple simultaneously interfering sources and background noise are usually modeled by a diffuse noise field. However, in reality, the free-field assumption is very weak because of the reverberant nature of acoustic environments. Therefore, we propose to estimate this simplified MWF solution in each frequency bin separately to cope with reverberation. We show that this approach can very efficiently be realized by the combination of a blocking matrix based on semi-blind source separation (‘directional BSS’), which provides a continuously updated reference of all undesired noise and interference components separated from the desired source and its reflections, and a single-channel Wiener postfilter. Moreover, it is shown, how the obtained reference signal of all undesired components can efficiently be used to realize the Wiener postfilter, and at the same time, generalizes well-known postfilter realizations. The proposed front-end and its integration into an automatic speech recognition (ASR) system are analyzed and evaluated in noisy living-room-like environments according to the PASCAL CHiME challenge. A comparison to a simplified front-end based on a free-field assumption shows that the introduced system substantially improves the speech quality and the recognition performance under the considered adverse conditions.