【正文】 憶能力、自學(xué)習(xí)能力 以及強(qiáng)容錯(cuò)性為故障診斷問(wèn)題提供了一個(gè)新 方法 。 與我一同工作的同志對(duì)研究所做的任何貢獻(xiàn)均已在論文中作出了明確的說(shuō)明。 基于小波神經(jīng)網(wǎng)絡(luò)的設(shè)備故障診斷方法研究 Research on Fault Diagnosis Method of Equipment Based on Wavelet Neural Network Research on Fault Diagnosis Method of Equipment Based on Wavelet Neural Network A Thesis Submitted for the Degree of Master Candidate： SUN Shihui Supervisor： Prof. ZHAO Shijun College of Informationamp。 若有不實(shí)之處，本人愿意承擔(dān)相關(guān)法律責(zé)任。 本文針對(duì)科學(xué)實(shí)驗(yàn)中廣泛使用的平流泵的故障特點(diǎn)， 深入研究了 BP神經(jīng)網(wǎng)絡(luò)的故障診斷方法。 為了解決上述問(wèn)題，本文研究設(shè)計(jì)了 GA+BP 算法。 連接基因采用二進(jìn)制編碼方法，參數(shù)基因采用實(shí)數(shù)編碼方法； 連接基因采用一 點(diǎn)交叉方式和基本變異方式， 參數(shù)基因 中的權(quán)閾基因和速率基因各自 采用算術(shù)交叉方式和 非均勻 變異方式 。 關(guān)鍵詞： 故障診斷 ， 小波包，神經(jīng)網(wǎng)絡(luò)，遺傳算法 ii Research on Fault Diagnosis Method of Equipment Based on Wavelet Neural Network SUN Shihui（ Detection Technology and Automatic Equipment） Directed by Prof. ZHAO Shijun Abstract Neural work offers a new method for fault diagnosis owing to its memory ability, selflearning ability and strongly fault tolerance. This paper makes research on the fault diagnosis method of neural work deeply based on the fault characteristics of pump which is widely used in experiment. Wavelet packet analysis is used to do the signal processing. Wavelet 3db is chosen, and all signals are denoised by hard threshold denoising method. Then wavelet packet deposes and constructs the energy eigenvectors which are regarded as the input eigenvectors of the neural work. A threelayer BPNN is applied to do the fault diagnosis. The results of simulation show that the work traps in local minimum easily, and both the number of hidden neurons and the learning rate are difficult to decide either. In order to solve these questions above, this paper designs GA+BP algorithm. In this algorithm, geic algorithm is used to optimize the number of hidden neurons, the initial weights and thresholds, and the learning rate of BPNN first, and then fault diagnosis is done by this neural work which has the optimum structure and parameters. In GA+BP neural work, each chromosome is divided into the connection genes and the parameter genes, and different geic operations are carried on two parts. Connection genes are binary type and parameter genes are realvalued. Mixed crossover and mutation operations are operated on the connection genes and parameter genes separately. It means the connection genes adopt singlepoint crossover and simple mutation, and the parameter genes adopt arithmetic crossover and nonuniform mutation. Both the crossover and mutation operators adopt selfadaptive method. iii Comparing the simulation results of GA+BP neural work with BPNN, we know that GA+BP neural work has less work but high training performance, and the local minimum is inexistent. In addition, the GA+BP neural work can diagnose the failure more correctly than BPNN. In conclusion, GA+BP neural work can acplish the pump fault diagnosis much better. Key words: fault diagnosis, wavelet packet, neural work, geic algorithm iv 目 錄 第 1 章 緒論 ............................................................................................................................ 1 故障診斷的意義 .............................................................................................................. 1 故障診 斷技術(shù)的研究現(xiàn)狀 .............................................................................................. 1 故障診斷方法概述 .......................................................................................................... 2 MATLAB 仿真平臺(tái)簡(jiǎn)介 ................................................................................................. 3 論文的研究?jī)?nèi)容 .............................................................................................................. 4 論文的組織結(jié)構(gòu) .............................................................................................................. 4 第 2 章 故障信號(hào)的采集 ........................................................................................................ 6 儀器簡(jiǎn)介 .......................................................................................................................... 6 實(shí)驗(yàn)方案設(shè)計(jì) .................................................................................................................. 6 實(shí)驗(yàn)裝置構(gòu)成 ........................................................................................................... 6 應(yīng)用軟件介紹 ............................................................................................................ 7 故障信號(hào)的數(shù)據(jù)采集 ...................................................................................................... 8 第 3 章 小波分析及信號(hào)處理 ................................................................................................ 9 小波分析在信號(hào)處理中的應(yīng)用現(xiàn)狀 .............................................................................. 9 小波分析理論 ................................................................................................................ 10 小波分析的基本概念及特點(diǎn) .................................................................................. 10 多分辨率分析 ..........................................................................................................11 3． 3 小波包分析 ................................................................................................................. 12 小波包的定義 ......................................................................................................... 12 小波包的子空間分解 ............................................................................................. 12 小波包的分解與重構(gòu)算法 ..................................................................................... 12 小波基函數(shù)的選擇 ........................................................................................................ 13 信號(hào)的小波包降噪 ............................