Estimating rock properties using sound signal dominant frequencies during diamond core drilling operations

In many engineering applications such as mining,geotechnical and petroleum industries,drilling operation is widely used.The drilling operation produces sound by-product,which could be helpful for preliminary estimation of the rock properties.Nevertheless,determination of rock properties is very difficult by the conventional methods in terms of high accuracy,and thus it is expensive and timeconsuming.In this context,a new technique was developed based on the estimation of rock properties using dominant frequencies from sound pressure level generated during diamond core drilling operations.First,sound pressure level was recorded and sound signals of these sound frequencies were analyzed using fast Fourier transform (FFT).Rock drilling experiments were performed on five different types of rock samples using computer numerical control (CNC) drilling machine BMV 45 T20.Using simple linear regression analysis,mathematical equations were developed for various rock properties,i.e.uniaxial compressive strength,Brazilian tensile strength,density,and dominant frequencies of sound pressure level.The developed models can be utilized at early stage of design to predict rock properties.

Noise cancellation of a multi-reference full-wave magnetic resonance sounding signal based on a modified sigmoid variable step size least mean square algorithm

Nano-volt magnetic resonance sounding(MRS) signals are sufficiently weak so that during the actual measurement, they are affected by environmental electromagnetic noise, leading to inaccuracy of the extracted characteristic parameters and hindering effective inverse interpretation. Considering the complexity and non-homogeneous spatial distribution of environmental noise and based on the theory of adaptive noise cancellation, a model system for noise cancellation using multi-reference coils was constructed to receive MRS signals. The feasibility of this system with theoretical calculation and experiments was analyzed and a modified sigmoid variable step size least mean square(SVSLMS) algorithm for noise cancellation was presented. The simulation results show that, the multi-reference coil method performs better than the single one on both signal-to-noise ratio(SNR) improvement and signal waveform optimization after filtering, under the condition of different noise correlations in the reference coils and primary detecting coils and different SNRs. In particular, when the noise correlation is poor and the SNR<0, the SNR can be improved by more than 8 dB after filtering with multi-reference coils. And the average fitting errors for initial amplitude and relaxation time are within 5%. Compared with the normalized least mean square(NLMS) algorithm and multichannel Wiener filter and processing field test data, the effectiveness of the proposed method is verified.

Detection of Mitral Valve Diseases by Bicoherence Analysis of Heart Sound Signals

The analysis of 10 normal and 51 mitral valve pathology making a total of 61 heart sound signals that were obtained with Littmann 4100 Digital Stethoscope were conducted in this study.Following the recorded sound signals were denoised by using wavelet filters,the signals were applied bicoherence analysis that is an high order spectral analysis method.It has been demonstrated that varieties of mitral valve pathology could be determined by three-dimensional surfaces of bicoherence and maximum bicoherence values.

Condition Monitoring of Roller Bearing by K-star Classifier andK-nearest Neighborhood Classifier Using Sound Signal

Most of the machineries in small or large-scale industry have rotating elementsupported by bearings for rigid support and accurate movement. For proper functioning ofmachinery, condition monitoring of the bearing is very important. In present study soundsignal is used to continuously monitor bearing health as sound signals of rotatingmachineries carry dynamic information of components. There are numerous studies inliterature that are reporting superiority of vibration signal of bearing fault diagnosis.However, there are very few studies done using sound signal. The cost associated withcondition monitoring using sound signal (Microphone) is less than the cost of transducerused to acquire vibration signal (Accelerometer). This paper employs sound signal forcondition monitoring of roller bearing by K-star classifier and k-nearest neighborhoodclassifier. The statistical feature extraction is performed from acquired sound signals. Thentwo-layer feature selection is done using J48 decision tree algorithm and random treealgorithm. These selected features were classified using K-star classifier and k-nearestneighborhood classifier and parametric optimization is performed to achieve the maximumclassification accuracy. The classification results for both K-star classifier and k-nearestneighborhood classifier for condition monitoring of roller bearing using sound signals werecompared.

Study on characteristics of acoustic signals generated by different DC discharge modes

Acoustic signals contain rich discharge information.In this study,the acoustic signal characteristics of transient glow,spark,and glow discharges generated through DC pin–pin discharge were investigated.The signals were analyzed in the time,frequency,and time–frequency domains,and the correlation between the electric and the acoustic signal was studied statistically.The results show that glow discharge does not produce measurable sound signals.For the other modes,with a decrease in the discharge gap,the amplitude of the acoustic signal increases sharply with mode transformation,the short-time average energy becomes higher,and the frequency components are more abundant.Meanwhile,the current pulse and sound pressure pulse have a one-to-one relationship in the transient glow and spark regimes,and they are positively correlated in amplitude.A brief theoretical analysis of the mechanism of plasma sound and the trends of signals in different modes is presented.Essentially,the change in the discharge energy is closely related to the sound generation of the plasma.

Recognition System for Diagnosing Pneumonia and Bronchitis Using Children’s Breathing Sounds Based on Transfer Learning

Respiratory infections in children increase the risk of fatal lung disease,making effective identification and analysis of breath sounds essential.However,most studies have focused on adults ignoring pediatric patients whose lungs are more vulnerable due to an imperfect immune system,and the scarcity of medical data has limited the development of deep learning methods toward reliability and high classification accuracy.In this work,we collected three types of breath sounds from children with normal(120 recordings),bronchitis(120 recordings),and pneumonia(120 recordings)at the posterior chest position using an off-the-shelf 3M electronic stethoscope.Three features were extracted from the wavelet denoised signal:spectrogram,mel-frequency cepstral coefficients(MFCCs),and Delta MFCCs.The recog-nition model is based on transfer learning techniques and combines fine-tuned MobileNetV2 and modified ResNet50 to classify breath sounds,along with software for displaying analysis results.Extensive experiments on a real dataset demonstrate the effectiveness and superior performance of the proposed model,with average accuracy,precision,recall,specificity and F1 scores of 97.96%,97.83%,97.89%,98.89%and 0.98,respectively,achieving superior performance with a small dataset.The proposed detection system,with a high-performance model and software,can help parents perform lung screening at home and also has the potential for a vast screening of children for lung disease.

Intelligent Sound-Based Early Fault Detection System for Vehicles

An intelligent sound-based early fault detection system has been proposed for vehicles using machine learning.The system is designed to detect faults in vehicles at an early stage by analyzing the sound emitted by the car.Early detection and correction of defects can improve the efficiency and life of the engine and other mechanical parts.The system uses a microphone to capture the sound emitted by the vehicle and a machine-learning algorithm to analyze the sound and detect faults.A possible fault is determined in the vehicle based on this processed sound.Binary classification is done at the first stage to differentiate between faulty and healthy cars.We collected noisy and normal sound samples of the car engine under normal and different abnormal conditions from multiple workshops and verified the data from experts.We used the time domain,frequency domain,and time-frequency domain features to detect the normal and abnormal conditions of the vehicle correctly.We used abnormal car data to classify it into fifteen other classical vehicle problems.We experimented with various signal processing techniques and presented the comparison results.In the detection and further problem classification,random forest showed the highest results of 97%and 92%with time-frequency features.

DEVELOPMENT AND USAGE OF SIGNAL PR OCESSING AND SOUND SITIMULATING SYSTEM CONTROLLED BY COMPUTER

In hearing physiological experiments and clinic tests,we need not only a signal processing system,but also a synchronous sound stimulator’ Most of stimulators we are now using are function generators which are independent to processing units,and can be controlled only by hand. Although some of them have ports through which they can be controlled by computer,but as they are designed for industrial aims,not for hearing research,most of them can’t generate the special waveforms we need. We use the TDT signal processing system and develop a software package have both usage. On the interface of the program we can control the sampling parameters and generate stimulating waveforms’

考虑小波包-灰度共生矩阵的高压断路器弹簧疲劳故障程度诊断研究

弹簧操动机构作为高压断路器(High voltage circuit breakers,HVCBs)分合闸操作的储能单元,其可靠性对电力系统的安全运行具有重要意义。本文以六氟化硫高压断路器的弹簧操动机构为研究对象,分析分合闸弹簧的动作机理,对弹簧进行不同程度的故障设置。介绍了振动、声音传感器设备及采集参数,针对小波包时频分析法的缺点,提出一种基于小波包-灰度共生矩阵(Gray level co-occurrence matrix,GLCM)的特征提取方法。从诊断速度和诊断准确度两方面对比了支持向量机(Support vector machine,SVM)、决策树(Decision tree,DT)、朴素贝叶斯、K近邻(K nearest neighbors,KNN)4种诊断模型。实验结果表明,在模拟实际应用场景中,选用K近邻算法对分合闸弹簧故障进行深度诊断能够准确判断故障类型及故障程度,对高压断路器安全可靠运行具有实际应用价值。

多导睡眠监测设备和肠鸣音设备之间数据同步采集方法研究

目的提出一种基于敲击脉冲信号的两个独立设备数据同步采集的方法,以实现数据的同步采集。方法利用多导睡眠监测设备的鼾声传感器与肠鸣音设备相互敲击产生瞬态脉冲信号作为同步标记信号,基于同步标记信号的三阶累积量实现同步标记信号的识别和对齐,并采用多组同步标记信号评价数据同步的精度。结果多导睡眠监测设备的鼾声传感器和肠鸣音设备相互敲击,产生了清晰明确的同步标记脉冲信号,结合时域特征和三阶累积量实现了同步标记信号的精确识别和对齐。经过多组同步标记信号识别验证表明,此种同步方法可保证同步精度范围在5 ms内。结论该数据同步采集方法简单易行,可保证多导睡眠监测设备和肠鸣音设备的高精度同步采集,同时也为其他完全独立的多个设备数据同步采集提供参考。

基于活动性检测动态估计噪声的心音降噪算法

针对基于小波分解和最优改进对数幅度谱估计的心音降噪算法存在噪声残留和心音失真的问题,提出一种基于心音活动性检测(HSAD)动态估计噪声的心音降噪算法。通过设计的HSAD判断当前心音帧是否为基础心音帧(FHS),根据判断结果分别采用改进最小值控制递归平均(IMCRA)算法和递归平滑算法对噪声功率进行动态估计与更新,采用非因果先验信噪比,实现心音信号的降噪。实验结果表明,提出算法能更好在提升降噪性能的同时,降低FHS的失真。

MEMS心音传感器及检测电路优化设计

对基于微电子机械系统(MEMS)技术的心音传感器声敏结构进行了优化且设计了其检测电路。首先,针对心音信号的特点,设计了二次集成的扁平状仿生纤毛结构,对该结构进行仿真,确定了纤毛的尺寸参数和梁上最大应力1.2×10^(5) N/m^(2),对纤毛进行特征频率仿真,在硅油域中结果为711 Hz;其次由扁平状纤毛结构X轴接收噪声时梁上的应力仿真结果可知该结构具有抗干扰能力;最后设计了后端的放大电路和滤波电路并对传感器封装后进行测试。测试结果表明,该结构的信噪比达到了27 dB,较传统的圆柱形纤毛提高了35%,且其抗干扰能力也优于传统的圆柱形纤毛。优化过后的MEMS心音传感器具有抗干扰、低噪声、低成本、采集的信号不失真等优势,可为临床心音信号的采集提供关键核心部件。

心肺音分离方法研究进展

听诊是诊断心血管和呼吸系统疾病最有效的方法。为了达到准确诊断的目的,设备必须能够识别各种临床情况下的心肺音。然而,记录的胸腔声音通常为心肺音混合信号。因此,将心肺音混合信号分离对于医生听诊至关重要。本文介绍了心音信号和肺音信号的频率范围和信号特征,综述了目前心肺音分离方法的研究进展,阐述了现有各种心肺音分离方法的优缺点,指出了选取一种合适的心肺音分离算法来分离心肺音对辅助医疗的重要意义。

基于声音信号的汽车安全带卷收器质量检测方法研究

为提高汽车安全带生产现场质量检测效率,根据《QC/T987—2014汽车安全带卷收器性能要求和试验方法》搭建实验平台,采集卷收器合格品与次品工作过程中的声音信号,将卷积注意力模块(CBAM)嵌入残差网络(ResNet-18)残差块之前,设计CBAM-ResNet-18“Before Blocks”模型,对采集到的卷收器声音信号进行分类。与不加注意力机制的ResNet-18模型、在残差块后加注意力机制的CBAM-ResNet-18“Within Blocks”模型、传统分类模型支持向量机和随机森林相比,模型在卷收器声音信号分类任务中的混淆矩阵、准确率、精确率、召回率和F 1值等方面均表现良好。实验结果表明:所设计的模型对于基于声音信号的汽车安全带卷收器质量检测十分有效。

柔性可持续穿戴的肺音信号监测听诊贴研究

针对当前数字听诊器在长时间穿戴监测方面的不足,研制了一种具有可延展电路结构、可紧密贴合的柔性肺音无线听诊贴。对听诊贴的前端进音腔室、体表贴合性、可延展电路结构的力学-电学性能以及信号降噪方面展开研究。结果表明:该柔性听诊贴可贴合体表,可延展电路在体表动态环境中力学-电学性能稳定,最后对采样的肺音信号经过小波阈值降噪后信噪比显著提高,实现了持续动态监测的可穿戴数字化听诊,可为后续肺部的持续诊疗工作提供一定的研究价值。

退役锂电池高压放电声音信号时频特征研究

为实现退役动力电池的梯次利用,以10 A·h方壳电池高压放电声音为研究对象,将不同SOH电池内部离子活性、放电能力、电荷迁移与补给能力等与其放电声纹建立联系,提取相关统计指标表征电池老化状态,为后续通过声音特征快速检测退役电池SOH打下理论基础。通过自搭建声音采集实验平台采集电池放电声音,而后对放电声音进行预处理,最后提取其时域特征(时域图、均方根能量)与频域特征(声谱图)进行分析。通过时频域特征分析得知:电池在高压电场诱导下放电发声,电池放电高能区域集中于10 kHz附近,随着SOH的降低,电池内部离子失活,电荷迁移与补给能力减弱,时域图中声音信号波形稳定性越差,且振幅主体范围不断减小,电池放电声音的均方根能量不断降低,语谱图中则表现为放电现象微弱,放电频率以及能量大幅下降。

基于Zynq和蜜蜂进化遗传算法的声源实时定向系统

针对声源定向系统利用多重信号分类(MUSIC)算法测向时存在精准度高,但实时性偏低的问题,基于Zynq平台和蜜蜂进化遗传算法(BEGA),设计了一款软硬件协同工作的声源实时定向系统。系统利用MEMS麦克风均匀圆阵和Zynq采集和传输声源数据,并引入BEGA提升MUSIC算法搜索谱峰的速度。实验表明,硬件平台具有不掉帧、延迟低的优良性能,同时数据处理单元利用BEGA大幅缩短了谱峰搜索的时间,并且具备精准的定向性能。因此,该系统满足实时性要求,也保留了MUSIC算法精准度高的优点。

An Innovation of Evaluation and Design of Vehicle Acceleration Sound Based on EEG Signals

There is a bottleneck in the design of vehicle sound that the subjective perception of sound quality that combines multiple psychological factors fails to be accurately and objectively quantified.Therefore,EEG signals are introduced in this paper to investigate the evaluation and design method of vehicle acceleration sound with powerful sound quality.Firstly,the experiment of EEG acquisition and subjective evaluation under the stimulation of powerful vehicle sounds is conducted,respectively,then three physiological EEG features of PSD_β,PSD_γand DE are constructed to evaluate the vehicle sounds based on the correlation analysis algorithms.Subsequently,the Adaptive Genetic Algorithm(AGA)is proposed to optimize the Elman model,where an intelligent model(AGA–Elman)is constructed to objectively predicate the perception of subjects for the vehicle sounds with powerful sound quality.The results demonstrate that the error of the constructed AGA–Elman model is only 2.88%,which outperforms than the traditional BP and Elman model;Finally,two vehicle acceleration sounds(Design1 and Design2)are designed based on the constructed AGA–Elman model from the perspective of order modulation and frequency modulation,which provide the acoustic theoretical guidance for the design of vehicle sound incorporating the EEG signals.

基于改进粒子群算法的矢量水听器多目标方位估计

为了研究单矢量水听器多目标方位估计能力,分别利用互谱声强法、MUSIC算法及信号统计量方法对多个目标方位进行估计。互谱声强法可以估计出多个不同频的单频目标方位,但对于频谱混叠的目标无法分辨;MUSIC算法可以分辨单频和宽带目标,但利用单矢量水听器最多可估计2个目标方位。为此,针对文章提出的信号统计量方法,构建了声压和振速的统计量模型,将其与粒子群优化算法及改进算法相结合,实现了基于改进粒子群算法的多目标方位估计。对多个单频和宽带信号目标进行仿真分析,结果表明,进粒子群算法具有良好的估计效果;对3种方法的估计结果进行比较,验证了改进粒子群算法有较好的适用性。通过对2022年千岛湖试验数据的处理再一次验证了算法的有效性。

SPA-HRDE在机械设备声信号故障诊断中的应用

针对现有故障诊断方法存在接触式采集、精度低等问题,提出了一种结合平滑先验分析和层次反向散布熵的机械设备故障诊断方法。首先,通过SPA将声音信号分解为趋势项和去趋势项。随后,利用HRDE提取趋势项和去趋势项信号的层次熵值,构建故障特征样本;最后,利用蜜獾算法对支持向量机的关键参数进行搜索,建立参数最优的故障识别模型,将故障特征输入到HBA-SVM分类器中进行故障识别,并基于离心泵和滚动轴承两种机械设备的实验评估证实了所提方法的有效性。试验结果表明:该方法分别取得了100%和97%的故障识别精度。相较于其他故障诊断方法,该方法能够充分提取声信号中的故障信息,实现更高精度的故障诊断,具有很强的鲁棒性。