發(fā)布時(shí)間：2018-06-02 10:51

  本文選題：通風(fēng)機(jī)軸承 + 特征頻率��； 參考：《中國礦業(yè)大學(xué)》2017年碩士論文

【摘要】：通風(fēng)機(jī)是一種典型的機(jī)械設(shè)備,運(yùn)行狀態(tài)直接影響經(jīng)濟(jì)發(fā)展和日常生產(chǎn)。軸承作為維持通風(fēng)機(jī)持續(xù)穩(wěn)定旋轉(zhuǎn)的關(guān)鍵零部件,對(duì)其進(jìn)行狀態(tài)監(jiān)測(cè)和故障診斷研究具有非常重要的意義。論文以通風(fēng)機(jī)軸承為研究對(duì)象,采集了正常、輕度內(nèi)圈故障、重度內(nèi)圈故障、輕度滾動(dòng)體故障、重度滾動(dòng)體故障、輕度外圈故障和重度外圈故障7種狀態(tài)的振動(dòng)信號(hào),對(duì)信號(hào)特征提取與故障診斷分類等問題進(jìn)行研究。論文介紹了局部均值分解(LMD)算法,通過對(duì)仿真信號(hào)分析,證明LMD在處理非平穩(wěn)信號(hào)優(yōu)于EMD和傳統(tǒng)時(shí)頻分析方法;針對(duì)LMD存在模態(tài)混疊的問題,引入總體局部均值分解(ELMD)算法;針對(duì)ELMD分解完備性差,采用改進(jìn)補(bǔ)充總體局部均值分解(ICELMD),不僅解決模態(tài)混疊問題,同時(shí)具有較高的完備性;使用ICELMD對(duì)通風(fēng)機(jī)軸承不同狀態(tài)振動(dòng)信號(hào)分解,并提取能量熵和峭度熵作為其特征值,為故障識(shí)別奠定了基礎(chǔ)。最后,采用概率神經(jīng)網(wǎng)絡(luò)(PNN)辨識(shí)故障類型。針對(duì)PNN的模式層結(jié)構(gòu)復(fù)雜,采用主元分析法(PCA)對(duì)輸入樣本降維;針對(duì)PNN的平滑因子σ難以確定,采用粒子群算法(PSO)對(duì)σ的優(yōu)化,提高了分類精度;再針對(duì)PSO算法易陷入局部極值和收斂速度慢的缺點(diǎn),分別采用慣性權(quán)重凹函數(shù)減小策略和適應(yīng)度值穩(wěn)定作為迭代終止條件的優(yōu)化策略。實(shí)驗(yàn)結(jié)果表明,PCA和PSO優(yōu)化的PNN既保證了較快的訓(xùn)練速度,又獲得了更高的故障分類正確率。
[Abstract]:Ventilator is a kind of typical mechanical equipment, the running state directly affects the economic development and daily production. Bearing is the key component to maintain the steady rotation of ventilator. It is of great significance to study the condition monitoring and fault diagnosis of the bearing. The paper takes fan bearing as the research object and collects vibration signals in seven states: normal, mild inner ring fault, severe inner ring fault, mild rolling body fault, heavy rolling body fault, mild outer ring fault and heavy outer ring fault. The problems of signal feature extraction and fault diagnosis classification are studied. The local mean decomposition (LMD) algorithm is introduced in this paper. By analyzing the simulated signals, it is proved that LMD is superior to EMD and traditional time-frequency analysis method in dealing with non-stationary signals, and the total local mean decomposition (LMD) algorithm is introduced to solve the problem of modal aliasing in LMD. In view of the poor completeness of ELMD decomposition, the improved total local mean decomposition is used to solve not only the problem of modal aliasing, but also the high completeness, and the ICELMD is used to decompose the vibration signals of fan bearings in different states. Energy entropy and kurtosis entropy are extracted as eigenvalues, which lays a foundation for fault identification. Finally, probabilistic neural network (PNN) is used to identify fault types. In view of the complexity of the model layer structure of PNN, the principal component analysis method (PCA) is used to reduce the dimension of input samples, and the particle swarm optimization algorithm (PSO) is used to improve the classification accuracy because the smoothing factor 蟽 of PNN is difficult to determine. Aiming at the disadvantage of PSO algorithm which is easy to fall into local extremum and slow convergence rate, the inertial weight concave function reduction strategy and fitness stability are adopted as the optimization strategy of iterative termination condition respectively. The experimental results show that the PNN optimized by PCA and PSO can not only guarantee faster training speed, but also obtain higher accuracy rate of fault classification.
【學(xué)位授予單位】：中國礦業(yè)大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2017
【分類號(hào)】：TH43;TP183

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本文編號(hào)：1968492