本文選題：故障圖像識別 + 自適應中值濾波�。� 參考：《哈爾濱工業(yè)大學》2016年博士論文

【摘要】：隨著我國鐵路事業(yè)的高速發(fā)展,傳統(tǒng)的人工巡檢模式已經不能滿足日益增長的貨車安檢工作需求。近年來,貨車運行故障動態(tài)圖像檢測(TFDS)系統(tǒng)的投入使用,極大地提高了車檢作業(yè)質量和效率。該系統(tǒng)可以識別貨車關鍵部位圖像的故障狀態(tài),有著重要的工程應用價值和理論研究意義。目前國內外在貨車故障圖像識別領域開展的研究工作相對較少,應用的技術也較為傳統(tǒng)。針對貨車螺栓部位的故障圖像識別問題,本文建立了一套有效可靠的貨車螺栓丟失故障圖像識別系統(tǒng),對其算法實現過程中的預處理、特征提取和分類識別等環(huán)節(jié)的相關技術進行了研究。本文主要包括以下四個方面的內容:針對預處理環(huán)節(jié)的濾波降噪問題,提出了改進自適應中值濾波(IRAMF)算法。設計了多級噪聲檢測策略用于準確地鑒別噪聲,包括全局檢測、局部檢測、鄰域相似度檢測和邊角檢測等方法;設計了自適應濾波策略用于有效地濾除噪聲,包括局部紋理走向子窗口合成濾波、曼哈頓距離加權均值濾波和最小有效窗口均值濾波等方法。IRAMF算法可以根據噪聲點局部鄰域內的噪聲密度狀態(tài)和紋理分布情況,自適應地調整濾波窗口的尺寸、形狀和輸出結果的計算方法。選取了4幅現場圖像進行實驗,結果表明該算法具有優(yōu)秀的濾波性能,能夠更好地保護紋理信息。針對特征提取環(huán)節(jié),提出了完整方向局部二值模式(CDLBP)算子。設計了局部紋理信息編碼策略用于提取圖像局部差分結果的符號和幅值變化信息;設計了全局比較信息編碼策略用于提取局部區(qū)域與圖像全局的灰度和幅值均值的比較信息。這樣可以提高特征向量的信息含量,增強區(qū)分能力。同時提出了分類加權排序模式選擇(LWR-DLBP)方法,綜合考慮了不同模式的出現概率、分布的一致性和類別信息的影響,這就降低了特征向量中的信息冗余。選取貨車心盤螺栓丟失故障圖像作為實驗對象,測試了CDLBP算子和LWR-DLBP方法的性能,實驗結果表明它們提取的特征向量具有優(yōu)秀的區(qū)分能力。針對分類識別過程中的多參數優(yōu)化問題,提出了改進師生交流優(yōu)化(ITLBO)算法。將種群更新的貪婪策略改進為優(yōu)選策略;設計了自適應教師教學階段,包括自適應學習步長、自適應知識差距和教師解的Lévy學習等策略;設計了自適應學生交流階段,包括自主學習、交流學習和補課學習等策略。這樣可以加快算法的收斂速度,提高其全局探索能力。以固定維和可變維的標準測試函數為對象,將ITLBO算法與其他11種算法進行對比實驗,結果表明該算法擁有更快的收斂速度和更高的尋優(yōu)精度。對貨車螺栓丟失故障圖像識別算法的設計與應用進行了研究。介紹了TFDS系統(tǒng)的組織結構和技術要求。給出了貨車故障圖像識別算法的設計方案:首先應用CDLBP算子提取原始圖像在不同尺度和方向下的Gabor響應圖像的紋理特征,然后使用LWR-DLBP方法對各個通道的原始特征進行優(yōu)化,接著對每個通道分別進行SVM分類識別,最后利用ITLBO算法對不同通道的預測標簽分配適宜的權重,進而獲得了最終的故障判別結果。測試了系統(tǒng)在識別心盤螺栓丟失故障、鉤尾扁銷螺栓丟失故障和安全鏈脫落故障時的性能表現,實驗結果表明本文算法可以有效地識別貨車螺栓部位圖像的故障狀態(tài)。
[Abstract]:With the rapid development of China's railway industry, the traditional manual inspection mode has not been able to meet the increasing demand for the safety inspection of freight cars. In recent years, the dynamic image detection (TFDS) system has greatly improved the quality and efficiency of the vehicle inspection operation. The system can identify the malfunction of the key parts of the freight car. State, it has important engineering application value and theoretical research significance. At present, there are relatively few research work in the field of vehicle fault image recognition at home and abroad, and the applied technology is more traditional. In this paper, a set of effective and reliable identification of truck bolt loss fault image recognition is established for the problem of fault image recognition of freight car bolt parts. In this paper, the related technologies of pre processing, feature extraction and classification recognition are studied. This paper mainly includes the following four aspects: the improved adaptive median filter (IRAMF) algorithm is proposed for the filtering and noise reduction problem of the preprocessing link. A multi level noise detection strategy is designed for accuracy. To identify the noise, including global detection, local detection, neighborhood similarity detection and edge angle detection, the adaptive filtering strategy is designed to filter the noise effectively, including local texture to sub window synthetic filtering, and the.IRAMF algorithm can be based on the method of the.IRAMF distance weighted mean filtering and the minimum effective window mean filtering. The noise density state and texture distribution in the local neighborhood of noise are adapted to adjust the size, shape and output of the filter window adaptively. 4 field images are selected to carry out experiments. The results show that the algorithm has excellent filtering performance and can better protect the texture information. The local two value mode (CDLBP) operator is used in the whole direction. The local texture information coding strategy is designed to extract the symbol and amplitude change information of the local difference results of the image, and the global comparison information coding strategy is designed to extract the comparison information of the gray and amplitude mean values of the local region and the image global. This can improve the feature vector. At the same time, the classification weighted sorting model selection (LWR-DLBP) method is proposed, which comprehensively considers the occurrence probability of different modes, the consistency of the distribution and the influence of category information. This reduces the information redundancy in the feature vector. It selects the lost fault image of the truck's heart disk bolt loss as the experimental object and tests the test. The performance of the CDLBP operator and the LWR-DLBP method, the experimental results show that the extracted feature vectors have excellent distinguishing ability. In view of the multi parameter optimization problem in the classification and recognition process, an improved teacher student communication optimization (ITLBO) algorithm is proposed. The greedy strategy of the population updating is improved to the optimal strategy, and the adaptive teacher teaching stage is designed. It includes the strategies of adaptive learning step, adaptive knowledge gap and teacher's L e vy learning, and designs adaptive student communication stages, including autonomous learning, exchange learning and lesson learning, so as to speed up the convergence speed of the algorithm and improve its global exploration ability. The ITLBO algorithm is compared with the other 11 algorithms. The results show that the algorithm has faster convergence speed and higher optimization accuracy. The design and application of the algorithm for identifying the fault image of the truck bolt loss are studied. The organization structure and technical requirements of the TFDS system are introduced. The recognition algorithm of the truck fault image is given. The CDLBP operator is used to extract the texture features of the Gabor response image of the original image in different scales and directions. Then the LWR-DLBP method is used to optimize the original features of each channel. Then, each channel is classified by SVM classification. Finally, the ITLBO algorithm is used to allocate the predictive labels for different channels. The results of the final fault discrimination are obtained, and the performance of the system is tested to identify the failure of the bolt loss, the loss of the bolt and the tail bolt and the failure of the safety chain. The experimental results show that the algorithm can effectively identify the fault status of the bolt position image of the freight car.

【學位授予單位】：哈爾濱工業(yè)大學
【學位級別】：博士
【學位授予年份】：2016
【分類號】：U279.3;TP391.41

【相似文獻】

相關期刊論文 前10條

1 范永法,鄭長宏;用于表面裂紋自動檢測的圖像識別算法[J];機械設計與制造;2002年04期

2 王宏;莫裕孟;尹莉軍;向時平;;野外復雜背景下輕武器射擊目標的圖像識別算法[J];火力與指揮控制;2009年07期

3 蔡政;丁宣浩;陳麗霞;劉憶寧;;敏感圖像識別算法研究[J];企業(yè)科技與發(fā)展;2012年05期

4 王斌,羅志勇,劉棟玉,呂新民;帶鋼表面重皮缺陷在線圖像識別算法研究[J];華中理工大學學報;1996年08期

5 朱華;應保勝;;鋼坯表面裂紋的圖像識別算法研究[J];機械設計與制造;2013年01期

6 程詠梅,潘泉,張洪才,王剛;信息融合圖像識別算法及其在三維飛機圖像識別中的應用研究[J];航空學報;2004年02期

7 唐盛;陳思平;尹立東;劉國文;;一種快速全自動超聲子宮節(jié)育環(huán)圖像識別算法[J];深圳大學學報(理工版);2008年03期

8 彭博;蔣陽升;;路面裂縫圖像識別算法研究進展[J];中外公路;2013年06期

9 張冬娟;唐萬有;朱捷航;;燙印在線檢測圖像識別算法的研究[J];包裝工程;2013年05期

10 段思迪;;紙幣清分圖像識別算法研究[J];鹽城工學院學報(自然科學版);2007年03期

相關會議論文 前1條

1 張冬娟;唐萬有;;基于投影特征的燙印圖像識別算法的研究[A];2013中國食品包裝學術會議論文摘要集[C];2013年

相關重要報紙文章 前1條

1 上海 王良;內存不良致電腦故障數例[N];電子報;2014年

相關博士學位論文 前4條

1 張洪健;貨車螺栓丟失故障圖像識別算法研究[D];哈爾濱工業(yè)大學;2016年

2 趙萌;圖像識別算法在細胞篩查及火災探測中的研究[D];天津大學;2016年

3 周哲;基于k近鄰的復雜工業(yè)過程故障診斷方法研究[D];浙江大學;2016年

4 程瑤;衛(wèi)星姿態(tài)控制系統(tǒng)的混合故障診斷方法研究[D];哈爾濱工業(yè)大學;2016年

相關碩士學位論文 前10條

1 王銀立;輸電線路導線斷股及絕緣子自爆的圖像識別算法研究[D];電子科技大學;2014年

2 胡偉;一種新型的太陽能硅片數片機[D];中國計量學院;2015年

3 王靜;基于ICA和ELM的圖像識別算法研究[D];中國海洋大學;2015年

4 傅天駒;基于深度學習的林火圖像識別算法及實現[D];北京林業(yè)大學;2016年

5 胡惠賢;基于異構平臺的視頻圖像識別算法研究與實現[D];華南理工大學;2016年

6 陳文一;動車組異常圖像識別算法及應用研究[D];華中科技大學;2015年

7 程嘉暉;基于深度卷積神經網絡的飛行器圖像識別算法研究[D];浙江大學;2017年

8 張潔;煙霧視頻圖像識別算法的研究[D];天津大學;2016年

9 王明瀟;圖像識別算法研究及其智能終端上的實現[D];北京郵電大學;2010年

10 張綠云;基于奇異值分解與正交可判別向量的圖像識別算法研究[D];廣西師范大學;2013年

，

本文編號：1894804