本文關(guān)鍵詞：復(fù)雜機(jī)電設(shè)備微弱特征提取與早期故障診斷方法研究　出處：《北京工業(yè)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： 微弱信號(hào)檢測(cè) 早期故障診斷 差分振子 隨機(jī)共振 混沌振子

【摘要】：在機(jī)電設(shè)備故障診斷中,由于背景噪聲、振動(dòng)信號(hào)傳播路徑等因素的影響,所測(cè)信號(hào)中有效故障信息往往被強(qiáng)大的背景噪聲所淹沒。尤其是在機(jī)電設(shè)備早期故障中,微弱特征信號(hào)完全被噪淹沒,從而無法獲得設(shè)備運(yùn)行狀態(tài)信息。本文以機(jī)電設(shè)備為對(duì)象,研究了機(jī)電設(shè)備微弱特征提取方法和早期故障診斷實(shí)用技術(shù)。 (1)闡述了差分振子的數(shù)學(xué)模型和微弱信號(hào)檢測(cè)基本原理,探討了系統(tǒng)參數(shù)對(duì)差分振子檢測(cè)性能的影響及其選取原則,并首次分析了差分振子檢測(cè)微弱信號(hào)的敏感特性和容錯(cuò)特性。指出若增大系統(tǒng)參數(shù),差分振子的收斂速度將會(huì)增加,檢測(cè)敏感性增強(qiáng),容錯(cuò)特性降低;反之,檢測(cè)的容錯(cuò)性增強(qiáng),敏感性降低。利用該方法分析了軋機(jī)齒輪和滾動(dòng)軸承的故障早期數(shù)據(jù),成功地提取出了設(shè)備早期故障特征,驗(yàn)證了差分振子對(duì)設(shè)備早期故障微弱特征提取的有效性。 (2)首次提出了利用差分振子相圖大小表征微弱信號(hào)相對(duì)幅值的機(jī)理和方法,給出了差分振子相圖收斂狀態(tài)的自動(dòng)辨識(shí)方法。理論分析和仿真實(shí)驗(yàn)均證明了在相同的系統(tǒng)參數(shù)下,差分振子相圖大小與信號(hào)幅值存在單調(diào)遞增的線性比例關(guān)系,故可利用相圖大小表征信號(hào)幅值的相對(duì)大小。利用該方法成功揭示了高線軋機(jī)軸承故障發(fā)生和發(fā)展的劣化過程,驗(yàn)證了方法的有效性。 (3)首次分析了差分振子系統(tǒng)參數(shù)對(duì)檢測(cè)帶寬的影響,提出了基于差分振子陣列的復(fù)合頻率信號(hào)檢測(cè)方法,實(shí)現(xiàn)了復(fù)合頻率信號(hào)無噪聲頻譜復(fù)現(xiàn)。指出小系統(tǒng)參數(shù)對(duì)應(yīng)較大的檢測(cè)帶寬,而大系統(tǒng)參數(shù)對(duì)應(yīng)較窄的檢測(cè)帶寬,進(jìn)而利用差分振子相圖所表征的頻率信息和幅值信息,實(shí)現(xiàn)了復(fù)雜信號(hào)的頻譜復(fù)現(xiàn)。 (4)首次將差分振子與隨機(jī)共振理論有機(jī)融合,實(shí)現(xiàn)了微弱信號(hào)的精確檢測(cè)。針對(duì)隨機(jī)共振頻譜中的奇倍頻虛假頻率現(xiàn)象,利用差分振子的選頻特性對(duì)隨機(jī)共振產(chǎn)生的譜峰進(jìn)行檢測(cè)以去除虛假頻率,實(shí)現(xiàn)微弱信號(hào)的精確檢測(cè)。利用該方法提取了棒材軋機(jī)故障早期數(shù)據(jù)中的微弱特征,可望實(shí)現(xiàn)設(shè)備早期故障診斷。 (5)針對(duì)目測(cè)觀察混沌振子相變的主觀性問題,提出利用Hu氏不變矩定量描述和刻畫混沌振子相圖的斂散性和對(duì)稱性,實(shí)現(xiàn)了混沌振子檢測(cè)微弱信號(hào)時(shí)臨界閾值和相圖狀態(tài)的自動(dòng)辨識(shí)。高線軋機(jī)軸承點(diǎn)蝕故障的實(shí)例分析證明了該方法的有效性。 上述研究成果在復(fù)雜機(jī)電設(shè)備微弱特征提取中取得了較好的效果,在早期故障診斷領(lǐng)域具有廣泛的應(yīng)用前景。
[Abstract]:In the fault diagnosis of electromechanical equipment, due to background noise, vibration signal propagation path and other factors. The effective fault information of the measured signal is often submerged by strong background noise, especially in the early fault of electromechanical equipment, the weak characteristic signal is completely submerged by noise. In this paper, the weak feature extraction method of electromechanical equipment and the practical technique of early fault diagnosis are studied. 1) the mathematical model of differential oscillator and the basic principle of weak signal detection are expounded, and the influence of system parameters on the detection performance of differential oscillator and its selection principle are discussed. The sensitivity and fault tolerance of differential oscillator for weak signal detection are analyzed for the first time. It is pointed out that if the system parameters are increased, the convergence rate of differential oscillator will increase, the sensitivity of detection will increase, and the fault-tolerant characteristic will decrease. On the other hand, the fault tolerance and sensitivity of the detection are enhanced, and the early fault data of rolling bearing and gear are analyzed by this method, and the early fault characteristics of the equipment are extracted successfully. The effectiveness of differential oscillator for weak feature extraction of equipment early fault is verified. (2) the mechanism and method of using differential oscillator phase diagram to characterize the relative amplitude of weak signal are proposed for the first time. An automatic identification method for the convergent state of the differential oscillator phase diagram is presented. The theoretical analysis and simulation results show that the convergent state of the differential oscillator is under the same system parameters. There is a monotone increasing linear relation between the size of the phase diagram and the amplitude of the signal. Therefore, the relative magnitude of the signal amplitude can be characterized by the size of the phase diagram, and the deterioration process of bearing fault occurrence and development in high wire rolling mill is successfully revealed by using this method, and the validity of the method is verified. The influence of the differential oscillator system parameters on the detection bandwidth is analyzed for the first time, and the detection method of the composite frequency signal based on the differential oscillator array is proposed. The noiseless spectrum reproduction of the composite frequency signal is realized. It is pointed out that the small system parameters correspond to a larger detection bandwidth, while the large system parameters correspond to a narrower detection bandwidth. Furthermore, the frequency and amplitude information represented by the differential oscillator phase diagram are used to realize the spectrum reproduction of the complex signal. 4) the differential oscillator and the stochastic resonance theory are combined for the first time to realize the accurate detection of the weak signal, aiming at the false frequency phenomenon of odd frequency doubling in the stochastic resonance spectrum. The spectral peak generated by stochastic resonance is detected by using the frequency selection characteristic of differential oscillator to remove false frequency and to detect the weak signal accurately. The weak features of the early fault data of bar mill are extracted by this method. It is expected to realize early fault diagnosis of equipment. 5) aiming at the subjective problem of visual observation of chaotic oscillator phase transition, Hu's invariant moment is used to quantitatively describe and depict the convergence and divergence and symmetry of chaotic oscillator phase diagram. The critical threshold and phase diagram state of chaotic oscillator for weak signal detection are automatically identified. The effectiveness of this method is proved by an example of pitting fault of bearings in high wire rolling mill. The above research results have achieved good results in the weak feature extraction of complex electromechanical equipment and have a wide application prospect in the field of early fault diagnosis.
【學(xué)位授予單位】：北京工業(yè)大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2011
【分類號(hào)】：TH165.3

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