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  本文關(guān)鍵詞：基于EMD與神經(jīng)網(wǎng)絡(luò)的柱塞泵故障診斷方法　出處：《華中科技大學(xué)》2011年碩士論文　論文類型：學(xué)位論文

  更多相關(guān)文章： 柱塞泵 故障診斷 EMD Fuzzy ARTMAP 神經(jīng)網(wǎng)絡(luò)

【摘要】：混凝土泵車是一種負(fù)載變化復(fù)雜、工作環(huán)境惡劣的工程機(jī)械,其內(nèi)部元件常由于疲勞或油液污染引起各種故障的發(fā)生,混凝土泵車一旦出現(xiàn)事故往往對(duì)生產(chǎn)安全造成很大危害。根據(jù)統(tǒng)計(jì),柱塞泵是混凝土泵車中最主要的故障源之一,但是柱塞泵作為泵車泵送動(dòng)力的來(lái)源,既是旋轉(zhuǎn)機(jī)械,又是往復(fù)運(yùn)動(dòng)機(jī)械和機(jī)液轉(zhuǎn)換元件,工作過(guò)程中既有機(jī)械零件間的振動(dòng),又有工作介質(zhì)引起的沖擊,因而其給診斷帶來(lái)了很大的難度。 本研究分析了柱塞泵的結(jié)構(gòu)特點(diǎn)與運(yùn)動(dòng)規(guī)律,指出常見(jiàn)故障的發(fā)生位置與振動(dòng)頻率,通過(guò)傳統(tǒng)頻譜分析方法做出功率譜與包絡(luò)譜,但由于介質(zhì)沖擊影響嚴(yán)重?zé)o法找出故障特征。進(jìn)而提出利用Hilbert-Huang變換的核心理論EMD,將實(shí)驗(yàn)臺(tái)中測(cè)取的五種常見(jiàn)故障與正常狀態(tài)的振動(dòng)信號(hào)進(jìn)行分解,得到與自身頻率組成相符的各頻段信號(hào)波形,通過(guò)分析將其中蘊(yùn)含故障信息豐富的高頻段分量建立時(shí)間序列AR模型,并將模型參數(shù)作為故障診斷的特征參數(shù),作為后續(xù)神經(jīng)網(wǎng)絡(luò)的輸入。神經(jīng)網(wǎng)絡(luò)具有強(qiáng)大的非線性映射和并行處理的功能,網(wǎng)絡(luò)中的權(quán)值向量模擬人大腦神經(jīng)元的記憶方式,可以較為穩(wěn)定的存儲(chǔ)來(lái)自輸入樣本的特征信息,并利用已有信息對(duì)新輸入做出分類辨識(shí),因此本研究利用較新型的Fuzzy ARTMAP神經(jīng)網(wǎng)絡(luò)對(duì)柱塞泵六種狀態(tài)振動(dòng)信號(hào)的特征參數(shù)進(jìn)行學(xué)習(xí)與分類,結(jié)果表明該方法可以有效地完成診斷。本文還就特征參數(shù)個(gè)數(shù)對(duì)神經(jīng)網(wǎng)絡(luò)的分類效率影響進(jìn)行了討論,并實(shí)現(xiàn)了在不影響診斷準(zhǔn)確率的條件下對(duì)特征參數(shù)數(shù)量的減少。
[Abstract]:Concrete pump car is a kind of construction machinery with complex load change and bad working environment. The internal components of concrete pump car are often caused by various failures due to fatigue or oil pollution. According to statistics, piston pump is one of the main fault sources of concrete pump vehicle, but piston pump is the source of pump power. It is not only a rotating machine but also a reciprocating moving machine and a mechanical and hydraulic conversion element. In the working process there is both the vibration between the mechanical parts and the impact caused by the working medium, so it is very difficult to diagnose. In this paper, the structure characteristics and motion law of piston pump are analyzed, and the location and vibration frequency of common faults are pointed out. The power spectrum and envelope spectrum are obtained by traditional spectrum analysis method. However, due to the serious impact of the medium, the fault characteristics can not be found, and then the core theory of EMD based on Hilbert-Huang transform is proposed. By decomposing the five kinds of common faults and the vibration signals in normal state, the signal waveforms of each frequency band which are consistent with its own frequency composition are obtained. Time series AR model is established by analyzing the components of high frequency band which contain abundant fault information, and the model parameters are regarded as the characteristic parameters of fault diagnosis. As the input of the subsequent neural network, the neural network has powerful nonlinear mapping and parallel processing functions. The weight vector in the network simulates the memory mode of human brain neurons. It can store the feature information from the input sample stably and use the existing information to classify and identify the new input. Therefore, a new type of Fuzzy ARTMAP neural network is used to study and classify the characteristic parameters of six state vibration signals of piston pump. The results show that the method can effectively complete the diagnosis. The effect of the number of characteristic parameters on the classification efficiency of neural networks is also discussed in this paper. The reduction of the number of feature parameters without affecting the diagnostic accuracy is realized.
【學(xué)位授予單位】：華中科技大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2011
【分類號(hào)】：TH322

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本文編號(hào)：1355863