基于小波变换的岩体AE波形特征研究

文章对室内声发射事件能率与应力的关系及波形特性进行了分析。通过小波变换,将室内岩体声发射等波形分解成低频和高频信号,对其原始声发射波形、细节信号和近似信号进行比较与分析,结果表明岩体在变形损伤破裂过程中不同时期的声发射波形特征不同。据此,可建立声发射波形特征库,用于预测预报岩体受损变形过程的发展趋势和规律。对含有信号部分的小波系数进行重构后,该信号与原始信号的误差小于10-10,说明小波分析可使岩体声发射波形特征预测预报的准确度和可靠性得以提高。     

基于无线传感器网络的岩体声发射信号监测系统

在分析了现有岩体声发射信号监测系统存在局限性的基础上,利用无线传感器网络和压缩感知技术,设计了一种新型岩体声发射信号监测系统,详细叙述了系统结构和软硬件实现方法,并将其应用于高速公路岩体边坡稳定性监测.实际应用结果表明,系统设计方案合理可行,且由于使用了压缩感知技术,在采样频率为200 kHz的情况下,也可实现声发射信...     

Coif5小波在煤岩声发射源定位中的应用研究

分析即将失稳的煤岩的声发射信号,可以确定煤岩体的状态。鉴于小波较好滤波特性,而传统时延估计对声发射源定位精度受噪声影响较大。为了准确地定位声发射源,提出了Coif5小波分析和TDE相结合的方法,既解决了传统时延相关估计方法易受噪声的影响,又解决了定位精度低的问题。在实验室建立监测系统,并对煤岩体样品进行单轴压缩的实验。实验结果表明,通过基于Coif5小波的TDE算法在能实现声发射源定位的同时,也大大减小了声发射源定位的误差。将其应用到即将失稳的煤岩体的预测预报中去,将为煤矿的安全生产保驾护航。     

基于声发射的地震、山体滑坡监测诊断系统的应用研究

阐述了采用声发射技术对矿山、岩石、水土、水工、建筑结构等行业进行监测。岩体工程如矿山边坡、山体边坡,因内外力作用以及外界环境因素影响,易发生滑坡蹋陷,在滑坡蹋陷之前都会有声发射现象发生,如何检测到这些声发射信号,是研究的重点内容。如何根据检测到的声发射信号进行危险预报,确定声发射源,是基于声发射的地震、山体滑坡监测诊断系统的主要研究任务。     

基于声发射的地震、山体滑坡监测诊断系统的应用研究

阐述了采用声发射技术对矿山、岩石、水土、水工、建筑结构等行业进行监测.岩体工程如矿山边坡、山体边坡,因内外力作用以及外界环境因素影响,易发生滑坡蹋陷,在滑坡蹋陷之前都会有声发射现象发生,如何检测到这些声发射信号,是研究的重点内容.如何根据检测到的声发射信号进行危险预报,确定声发射源,是基于声发射的地震、山体滑坡监测诊断系统的主要研究任务.     

多功能岩体声发射监测仪

为了监测地下工程岩石开挖过程中声发射的主要参数,如大事件率、总事件率和能率,设计了一种以STC89C58RD+单片机为微控制器的多功能岩体声发射监测仪.该仪器具有三通道同步监测、能够适应多种岩体、大/总事件阈值可调、4 Mbits数据可存储下载、长时间连续监测、能与PC机通信等功能.该仪器已应用于矿山井下监测,并且取得了良好的效果.     

声发射定位中时差计算研究

矿井冲压是目前在煤矿安全中较为严重的一种自然灾害,国内外学者进行了大量理论和实验研究。实践证明,应用声发射监测及地质条件综合分析是行之有效的科学方法,而声发射监测中最基本的一个问题就是声发射源的定位问题。声源的定位中最重要的就是通过声发射信号达到各个传感器的时差来计算确定其位置。声发射信号在煤岩体中的传播中产生了P波、S波等,如何来准确确定时差也就成了定位准确的首要问题。本文主要从声发射信号的时差计算出发,包括直接计数法、互相关法和小波分析的方法。通过分析对比,得出小波分析方法在时差计算上方便简单、准确性高。     

瓦斯喷孔过程声发射响应规律研究

为预防石门揭煤过程中可能诱发的突出危险,基于声发射监测系统,对某矿石门揭煤过程中瓦斯喷孔时的声发射活动进行了测试和分析。分析研究结果表明,声发射强度通常在喷孔发生前2~3h迅速升高,峰值强度可达到无突出危险时的2~3倍,在喷孔发生前又突降至较低水平;喷孔越严重,声发射强度值越高;声发射强度变化的幅度能够反映喷孔严重性,喷孔动力现象越明显,瓦斯体积分数变化越剧烈,煤岩体声发射强度增长率越大且峰值强度越高。该分析研究结果对石门揭煤中突出危险性的监测预报有重要的参考意义。     

岩体声发射信号波形变化特征分析

文章通过对室内和现场采集到的岩体声发射信号波形分析,寻找到岩体在外界因素影响下从稳定状态发展变化到破裂阶段的波形变化规律,可用以判断岩体的危险性。该规律为采场、隧道工程的安全施工提供了可靠的预报信息,有一定的实际意义。     

基于PSO的SVM的煤岩声发射源定位研究

为了准确的预测采空区煤矿煤岩破裂与失稳前岩石所释放出来的声发射信息的位置,并且根据山西焦煤的官地矿16403工作面获得的声发射事件的数据,因为该数据是一个非线性、高维的问题,提出了用PSO和SVM算法相结合的方法在煤矿煤岩声发射定位中的应用进行了研究。以往的方法只是单纯的收集煤岩或岩石声发射信息,以至于定位会出现失准、精度低和误差大的缺点。文章提出了“1+1=1”的定位方法,既收集同一位置的岩石和煤岩体的声发射信号,分析处理后,得到其位置。在煤岩失稳前两者都会发出强烈的信号。仿真结果表明：应用PSO和SVM理论结合的方法进行煤矿煤岩声发射定位的预测,在提高准确性和精确度的同时,也大大的提高了泛化的能力,该方法也大大减小定位失准的误差。     

声发射技术在地压监测中的应用

对某矿的采空区围岩试样进行了单轴压缩声发射实验,得到了岩石破坏时能率、事件率等声发射参数值,根据这些数值对声发射监测系统参数进行了设置;通过对声发射监测系统长期运行结果的统计,总结出了井下多种活动即岩石声发射、打钻、爆破、人工破碎大块矿石、放矿、敲击的波形及各类波形的参数范围;确定了以能率、事件率为特征的采空区失稳判据,为判断地压灾害提供了可靠依据。     

冲击地压多信号监测软件的设计

针对冲击地压监测系统存在监测信号单一,无法实现实时对比分析等缺点,开发设计了一种基于VC++6.0,针对煤岩电磁辐射、微震和声发射等特征信号的多信号监测软件;介绍了该软件的结构和功能及各功能模块的设计。该软件具有多通道实时显示、数据存储、历史查询、趋势分析、统计等功能,通过对比分析多种煤岩特征信号,可以对煤岩动力灾害进行有效的预测。     

基于声音识别的煤矿重特大事故报警方法研究

煤矿瓦斯与煤尘爆炸会产生爆炸声,煤与瓦斯突出会产生煤炮声、支架发出的嘎嘎声和破裂折断声等,冲击地压会产生巨大的岩石破碎声响和震动等,煤矿透水会发出“嘶嘶”的水叫声、大量透水会产生水流声等,煤矿顶板冒落会发出顶板断裂声、煤岩落地撞击声、支护损毁声等。针对煤矿重特大事故声音特点,提出了煤矿井下瓦斯与煤尘爆炸、煤与瓦斯突出、冲击地压、水灾、顶板冒落等事故报警方法:各事故声音的时域和频域特征与其他声音不同,可通过矿用防爆拾音设备和系统实时监测声音,通过声音智能分析和声音频率、幅度、短时能量等特征参数分析感知事故并报警;通过监测和分析不同监测地点声音强度特征、声音发生的先后关系和防爆拾音设备损坏的先后关系等判定事故发生地点;根据各事故特点提出了多信息融合分析的灾害识别方法,减小工作面落煤、爆破作业、采煤设备、掘进设备、运输提升设备、供电设备、乳化液泵、水泵和局部通风机工作等产生的声音干扰。论述了不同拾音设备的优缺点,矿用拾音设备宜采用麦克风阵列;研究了适用于煤矿重特大事故的声音识别分类器。     

Faulted gear identification of a rotating machinery based on wavelet transform and artificial neural network

In this paper, a condition monitoring and faults identification technique for rotating machineries using wavelet transform and artificial neural network is described. Most of the conventional techniques for condition monitoring and fault diagnosis in rotating machinery are based chiefly on analyzing the difference of vibration signal amplitude in the time domain or frequency spectrum. Unfortunately, in some applications, the vibration signal may not be available and the performance is limited. However, the sound emission signal serves as a promising alternative to the fault diagnosis system. In the present study, the sound emission of gear-set is used to evaluate the proposed fault diagnosis technique. In the experimental work, a continuous wavelet transform technique combined with a feature selection of energy spectrum is proposed for analyzing fault signals in a gear-set platform. The artificial neural network techniques both using probability neural network and conventional back-propagation network are compared in the system. The experimental results pointed out the sound emission can be used to monitor the condition of the gear-set platform and the proposed system achieved a fault recognition rate of 98% in the experimental gear-set platform.     

煤岩体声发射监测系统的设计与实现

本文介绍矿山地质条件下煤岩体声发射监测系统的设计与实现。在煤矿生产现场，系统正为有效地预报冲击地压矿井的冲击（矿震）危险发挥着作用。在冶金矿山地质条件下，系统也有良好的应用前景。     

宿舍防盗防火报警系统

系统采用STC89C52单片机作为主控器,采用GSM Modem、红外感应器、红外发射接收管、烟雾感应器和声光报警模块、独立键盘,构成宿舍防盗防火报警系统对宿舍内人员进出情况进行记录及异地监控等.     

Investigation of engine fault diagnosis using discrete wavelet transform and neural network

An investigation of a fault diagnostic technique for internal combustion engines using discrete wavelet transform (DWT) and neural network is presented in this paper. Generally, sound emission signal serves as a promising alternative to the condition monitoring and fault diagnosis in rotating machinery when the vibration signal is not available. Most of the conventional fault diagnosis techniques using sound emission and vibration signals are based on analyzing the signal amplitude in the time or frequency domain. Meanwhile, the continuous wavelet transform (CWT) technique was developed for obtaining both time-domain and frequency-domain information. Unfortunately, the CWT technique is often operated over a longer computing time. In the present study, a DWT technique which is combined with a feature selection of energy spectrum and fault classification using neural network for analyzing fault signal is proposed for improving the shortcomings without losing its original property. The features of the sound emission signal at different resolution levels are extracted by multi-resolution analysis and Parseval’s theorem [Gaing, Z. L. (2004). Wavelet-based neural network for power disturbance recognition and classification. IEEE Transactions on Power Delivery 19, 1560–1568]. The algorithm is obtained from previous work by Daubechies [Daubechies, I. (1988). Orthonormal bases of compactly supported wavelets. Communication on Pure and Applied Mathematics 41, 909–996.], the“db4”, “db8” and “db20” wavelet functions are adopted to perform the proposed DWT technique. Then, these features are used for fault recognition using a neural network. The experimental results indicated that the proposed system using the sound emission signal is effective and can be used for fault diagnosis of various engine operating conditions.     

一种新型高灵敏度双叠片式流体声发射传感器的研制

研制高灵敏度、安装使用方便、抗干扰能力强的传感器是实现刀具磨损在线监测的关键.参考空气声学中常用的压差式微音器的典型结构,考虑自动化加工中使用的要求,研制成功可用于刀具磨损状态监测的非接触高灵敏度双叠片式流体声发射传感器.对研制的流体声发射传感器性能进行了实验分析,结果表明传感器对刀具磨损产生的声发射信号具有较高的灵敏度.     

Cutting parameters analysis for the development of a milling process monitoring system based on audible energy sound

Monitoring of machining processes is a critical requirement in the implementation of any unmanned operation in a shop floor and, particularly, in the establishment of Flexible Manufacturing Systems (FMS) and Computer Integrated Manufacturing (CIM) where most of the operations are carried out in an automated way. During the last years, notable efforts have been made to develop reliable and robust monitoring systems based on different types of sensors such as cutting force and torque, motor current and effective power, vibrations, acoustic emission or audible sound energy. This work is focused on this last sensor technology. The basic objective is to characterise the audible sound energy signals generated during different machining operations carried out on a milling machine. In order to achieve this, rotation speed, feed and depth of cut have been analysed separately. The main contributions of this work are, on the one hand, the application of a systematic methodology to set up the cutting tests and, on the other hand, the independent signal analysis of the noise generated by the milling machine used for the cutting tests in order to filter this noise out from the signals obtained during the actual material processing. The classification of audible sound signal features for process monitoring has been obtained by graphical analysis and parallel distributed data processing using a supervised neural network (NN) paradigm.