【摘要】：近幾年,隨著我國(guó)城市化建設(shè)飛速發(fā)展對(duì)地下電纜的安全性需求越來(lái)越迫切。由于在道路改造和房屋建設(shè)等施工過(guò)程中,工作人員的疏忽大意導(dǎo)致電纜被挖斷的事故頻頻發(fā)生,給國(guó)家經(jīng)濟(jì)和人民安全帶來(lái)嚴(yán)重危害。因此,保障地下電纜供電系統(tǒng)不受挖掘設(shè)備破壞成為我國(guó)電力及城建部門亟待解決的問(wèn)題。本文在語(yǔ)音識(shí)別的基礎(chǔ)上,對(duì)常用幾種挖掘設(shè)備(挖掘機(jī)、液壓沖擊錘、電錘、切割機(jī))的聲音信號(hào)展開深入分析研究,構(gòu)建了一套基于頻譜動(dòng)態(tài)特征的聲音信號(hào)提取方法和極限學(xué)習(xí)機(jī)(ELM)作為分類器的挖掘設(shè)備識(shí)別算法。該算法能夠有效地檢測(cè)到威脅電纜安全的挖掘設(shè)備在作業(yè)時(shí)的聲音信號(hào),從而進(jìn)行預(yù)警判斷,達(dá)到對(duì)事發(fā)地進(jìn)行定位的目的。本文主要研究工作如下:1.采用八通道的麥克風(fēng)十字陣列在夜晚較理想的環(huán)境下對(duì)四種挖掘設(shè)備在不同距離作業(yè)下采集聲音信號(hào),用于建立聲音特征庫(kù)。通過(guò)聲陣列對(duì)不同環(huán)境、不同距離下挖掘設(shè)備在白天正常作業(yè)的聲音信號(hào)進(jìn)行采集與識(shí)別,進(jìn)一步驗(yàn)證該算法的有效性。2.采用基于Mel頻率倒譜系數(shù)(MFCC)特征提取方法、基于一階差分Mel頻率倒譜系數(shù)((35)MFCC)特征提取方法、基于二階差分Mel頻率倒譜系數(shù)((35)(35)MFCC)特征提取方法和基于頻譜動(dòng)態(tài)特征的聲音信號(hào)提取方法。通過(guò)對(duì)挖掘設(shè)備聲音信號(hào)的特征提取,進(jìn)行不同的對(duì)比實(shí)驗(yàn)。3.在模式識(shí)別方面,基于識(shí)別率、訓(xùn)練模型和識(shí)別時(shí)間長(zhǎng)短作為本文算法的評(píng)價(jià)標(biāo)準(zhǔn)。選取BP前饋神經(jīng)網(wǎng)絡(luò)、KNN和ELM三種模式識(shí)別方法,用于對(duì)挖掘設(shè)備信號(hào)類型的識(shí)別對(duì)比。4.在實(shí)驗(yàn)中,設(shè)計(jì)了基于MFCC、(35)MFCC、(35)(35)MFCC和頻譜動(dòng)態(tài)特征的系數(shù)提取以及BP前饋神經(jīng)網(wǎng)絡(luò)、KNN和ELM三種分類識(shí)別算法的對(duì)比實(shí)驗(yàn)。進(jìn)一步討論了隱含結(jié)點(diǎn)個(gè)數(shù)以及KNN識(shí)別算法中K值對(duì)識(shí)別結(jié)果的影響。通過(guò)大量實(shí)驗(yàn)進(jìn)行分析,基于頻譜動(dòng)態(tài)特征的聲音特征提取方法和ELM的識(shí)別算法對(duì)挖掘設(shè)備作業(yè)的異常事件識(shí)別及預(yù)警是穩(wěn)定的。5.為增強(qiáng)算法的魯棒性,在地鐵施工現(xiàn)場(chǎng),重新采集挖掘設(shè)備聲音數(shù)據(jù)驗(yàn)證每種設(shè)備的工作狀態(tài)。結(jié)果表明,該算法能夠較準(zhǔn)確地對(duì)挖掘設(shè)備進(jìn)行識(shí)別從而達(dá)到預(yù)報(bào)警的目的。最后將該識(shí)別算法通過(guò)MATLAB軟件建立一個(gè)GUI界面。
[Abstract]:In recent years, with the rapid development of urbanization in China, the security needs of underground cables are becoming more and more urgent. In the process of road reconstruction and building construction, the carelessness of the workers leads to frequent accidents of cable breaking, which brings serious harm to the national economy and the safety of the people. Therefore, the protection of underground cable power supply system from excavation equipment damage has become an urgent problem for power and urban construction departments in China. On the basis of speech recognition, the sound signals of several kinds of excavating equipment (excavator, hydraulic hammer, electric hammer, cutting machine) are deeply analyzed and studied in this paper. A set of acoustic signal extraction methods based on spectrum dynamic features and a mining equipment recognition algorithm based on extreme learning machine (ELM) as classifier are constructed. The algorithm can effectively detect the sound signal of the mining equipment which threatens the safety of the cable in the operation, so as to carry out early warning judgment and achieve the purpose of locating the site of the accident. The main work of this paper is as follows: 1. An eight-channel microphone cross array is used to collect sound signals from four kinds of excavating devices at different distances in an ideal environment at night, which can be used to set up a sound signature database. The acoustic array is used to collect and recognize the sound signals of the mining equipment in different environments and at different distances, which further verifies the effectiveness of the algorithm. 2. The (MFCC) feature extraction method based on Mel frequency cepstrum coefficient and the first order differential Mel frequency cepstrum coefficient (35) MFCC) feature extraction method) are used. Second order difference Mel frequency cepstrum coefficients (35) (35) MFCC) feature extraction method and sound signal extraction method based on spectrum dynamic features are presented. Through the feature extraction of the acoustic signal of mining equipment, different contrast experiments are carried out. 3. In the aspect of pattern recognition, recognition rate, training model and recognition time are the evaluation criteria of this algorithm. Three pattern recognition methods, BP feedforward neural network, KNN and ELM, are selected to identify and compare the signal types of mining equipment. 4. In the experiment, the coefficients extraction based on MFCC, (35) (35) MFCC and spectrum dynamic features, as well as three classification and recognition algorithms based on BP feedforward neural network, KNN and ELM are designed. Furthermore, the effect of the number of hidden nodes and the K value in KNN recognition algorithm on the recognition results is discussed. Based on a large number of experiments, the acoustic feature extraction method based on dynamic spectrum features and the recognition algorithm of ELM are stable for the detection and warning of abnormal events in mining equipment operations. In order to enhance the robustness of the algorithm, the acoustic data of each kind of equipment are collected again at the subway construction site to verify the working state of each equipment. The results show that the algorithm can accurately identify the mining equipment and achieve the purpose of pre-warning. Finally, a GUI interface is built by using MATLAB software.
【學(xué)位授予單位】：杭州電子科技大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2016
【分類號(hào)】：TN912.34;TM75

【相似文獻(xiàn)】

相關(guān)期刊論文 前4條

1 李富安;;挖掘設(shè)備潛力,提高供電質(zhì)量[J];排灌機(jī)械;1986年05期

2 ;挖掘設(shè)備潛力 擴(kuò)大接卸能力[J];哈鐵科技通訊;1983年06期

3 馬守明;連續(xù)墻抓斗挖掘設(shè)備及工藝[J];探礦工程(巖土鉆掘工程);1995年03期

4 ;[J];;年期

相關(guān)會(huì)議論文 前1條

1 紀(jì)建平;焦惠麗;侯小春;;依靠技術(shù)改造 挖掘設(shè)備潛能[A];“常紡院”杯全國(guó)前織生產(chǎn)技術(shù)管理經(jīng)驗(yàn)交流會(huì)論文集[C];2008年

相關(guān)重要報(bào)紙文章 前3條

1 本報(bào)記者 王彥彬;深度挖掘設(shè)備節(jié)能[N];通信產(chǎn)業(yè)報(bào);2013年

2 記者 蘇建鳳;首鋼長(zhǎng)鋼煉鐵廠 降本增效有絕招[N];首都建設(shè)報(bào);2010年

3 曉蘭、永生、開芳;津西鋼鐵利潤(rùn)水平位居全國(guó)鋼鐵行業(yè)前十[N];唐山勞動(dòng)日?qǐng)?bào);2009年

相關(guān)碩士學(xué)位論文 前1條

1 趙拓;基于頻譜動(dòng)態(tài)特征和ELM的挖掘設(shè)備識(shí)別方法研究[D];杭州電子科技大學(xué);2016年

，

本文編號(hào)：2348087