基于小波分析與神經(jīng)網(wǎng)絡(luò)滾動(dòng)軸承故障診斷方法的研究
發(fā)布時(shí)間：2018-09-12 06:48
【摘要】：滾動(dòng)軸承是旋轉(zhuǎn)機(jī)械中重要的零部件之一,但由于加工工藝、工作環(huán)境等原因造成損壞率高、壽命的隨機(jī)性較大。旋轉(zhuǎn)機(jī)械故障種類(lèi)繁多,但由滾動(dòng)軸承的故障引起的大約占三分之一,所以掌握滾動(dòng)軸承的工作狀態(tài)以及故障的形成和發(fā)展,是目前機(jī)械故障診斷領(lǐng)域中所研究的重要課題之一 本論文通過(guò)分析滾動(dòng)軸承振動(dòng)機(jī)理、失效原因和信號(hào)特征,對(duì)軸承振動(dòng)信號(hào)的采集方法進(jìn)行了改進(jìn),采用無(wú)線傳感器網(wǎng)絡(luò)技術(shù)降低故障診斷系統(tǒng)的復(fù)雜性、提升診斷系統(tǒng)的效率。利用滾動(dòng)軸承振動(dòng)信號(hào)實(shí)現(xiàn)其故障檢測(cè)與診斷,目前主要有機(jī)理分析和智能診斷兩條途徑。機(jī)理分析常用方法有隨機(jī)共振和小波分析等；智能診斷常用方法有神經(jīng)網(wǎng)絡(luò)和支持向量機(jī)等。但以上各方法在實(shí)際應(yīng)用中均存在其不足之處,從而影響到軸承故障檢測(cè)與診斷的效果。為此,本文認(rèn)為非常有必要立足于不斷發(fā)展的新理論和新方法,緊緊圍繞滾動(dòng)軸承故障機(jī)理分析與智能診斷現(xiàn)有方法存在的問(wèn)題與不足展開(kāi)研究與探討。 (1)針對(duì)傳統(tǒng)有線傳感器網(wǎng)絡(luò)信息采集靈活性差、故障率高的問(wèn)題,本文在分析滾動(dòng)軸承振動(dòng)機(jī)理、失效原因和振動(dòng)信號(hào)特征的基礎(chǔ)上,設(shè)計(jì)了滾動(dòng)軸承振動(dòng)信號(hào)無(wú)線采集網(wǎng)絡(luò),以802.15.4和ZigBee協(xié)議為標(biāo)準(zhǔn),采用250kbps(?)勺傳輸速率和無(wú)線部署的方式,降低系統(tǒng)復(fù)雜性和故障率,為后續(xù)軸承故障診斷方法提供基礎(chǔ)原理性的支持。 (2)針對(duì)噪聲較強(qiáng)有用信號(hào)較弱環(huán)境下的軸承故障問(wèn)題,研究了一種基于遺傳免疫優(yōu)化粒子群算法的隨機(jī)共振方法。該方法不僅實(shí)現(xiàn)了強(qiáng)噪聲背景下的微弱信號(hào)提取,而且解決了基本隨機(jī)共振理論只能處理微弱的小參數(shù)信號(hào)、不能處理軸承振動(dòng)這類(lèi)大參數(shù)信號(hào)問(wèn)題。通過(guò)展開(kāi)深入的研究,提出了一種基于遺傳免疫的粒子群優(yōu)化算法,并將其應(yīng)用于隨機(jī)共振的關(guān)鍵參數(shù)尋優(yōu)過(guò)程,由此進(jìn)一步提出了基于遺傳免疫粒子群優(yōu)化的自適應(yīng)隨機(jī)共振算法,并采用軸承故障實(shí)驗(yàn)數(shù)據(jù)進(jìn)行了分析與驗(yàn)證。 (3)針對(duì)小波理論實(shí)際應(yīng)用過(guò)程中存在難以構(gòu)造理想小波基函數(shù)的問(wèn)題,研究了基于第二代小波變換的滾動(dòng)軸承故障診斷方法。該方法利用第二代小波變換將滾動(dòng)軸承故障振動(dòng)信號(hào)分解到不同尺度,提取出共振頻帶,然后利用Hilbert變換進(jìn)行解調(diào),再對(duì)解調(diào)后的信號(hào)進(jìn)行頻譜分析得到小波包絡(luò)譜,從包絡(luò)譜上獲取軸承故障特征信息。通過(guò)軸承實(shí)驗(yàn)數(shù)據(jù)應(yīng)用與分析表明,該方法準(zhǔn)確地提取了滾動(dòng)軸承不同損傷程度故障的特征頻率,實(shí)現(xiàn)了軸承故障的定量診斷。 (4)針對(duì)神經(jīng)網(wǎng)絡(luò)本身性能難以繼續(xù)提高的問(wèn)題,研究了基于第二代小波變換與神經(jīng)網(wǎng)絡(luò)的滾動(dòng)軸承智能診斷方法。本文從提高神經(jīng)網(wǎng)絡(luò)輸入端的信號(hào)質(zhì)量入手,利用第二代小波變換與特征評(píng)估方法,提出了一種基于第二代小波與神經(jīng)網(wǎng)絡(luò)相結(jié)合的滾動(dòng)軸承智能診斷模型,并將該模型應(yīng)用于實(shí)驗(yàn)分析與工程實(shí)踐中。結(jié)果表明從第二代小波分解后信號(hào)中提取的聯(lián)合特征能夠揭示更多的故障信息；特征評(píng)估方法能夠針對(duì)診斷對(duì)象的健康狀態(tài)分類(lèi)選擇其相應(yīng)的敏感特征,大大提高了BP神經(jīng)網(wǎng)絡(luò)分類(lèi)的準(zhǔn)確率,驗(yàn)證了本文所建立的智能診斷模型的有效性。 (5)針對(duì)滾動(dòng)軸承故障屬于典型小樣本的特征,研究了基于參數(shù)優(yōu)化支持向量機(jī)的滾動(dòng)軸承智能診斷方法�；局С窒蛄繖C(jī)方法存在模型參數(shù)不易合理選取而影響到算法性能的問(wèn)題,本文在詳細(xì)分析各參數(shù)對(duì)分類(lèi)模型的影響的基礎(chǔ)上,建立了參數(shù)優(yōu)化模型,并采用遺傳免疫粒子群算法作為優(yōu)化方法,建立了基于遺傳免疫粒子群和支持向量機(jī)的智能診斷模型,最后將該模型用于軸承故障診斷中。結(jié)果表明,該算法不但實(shí)現(xiàn)了對(duì)SVM分類(lèi)模型參數(shù)的自動(dòng)優(yōu)化,提高了SVM分類(lèi)模型的故障診斷精度,而且對(duì)分散程度較大、聚類(lèi)性較差的故障樣本分類(lèi)有較強(qiáng)的適用性。 通過(guò)論文上述內(nèi)容研究,優(yōu)化了目前的應(yīng)用于滾動(dòng)軸承不同故障條件下的診斷算法,并進(jìn)行了實(shí)驗(yàn)驗(yàn)證,為旋轉(zhuǎn)機(jī)械的故障診斷提供了新方向。
[Abstract]:Rolling bearing is one of the most important parts in rotating machinery, but because of the processing technology, working environment and other reasons, the damage rate is high, and the life of the randomness is large. Exhibition is one of the most important topics in the field of mechanical fault diagnosis.
In this paper, by analyzing the vibration mechanism, failure reasons and signal characteristics of rolling bearings, the acquisition method of bearing vibration signals is improved. The wireless sensor network technology is used to reduce the complexity of fault diagnosis system and improve the efficiency of diagnosis system. There are two ways of mechanism analysis and intelligent diagnosis.The common methods of mechanism analysis are stochastic resonance and wavelet analysis,and the common methods of intelligent diagnosis are neural network and support vector machine.But these methods have their shortcomings in practical application,which affect the effect of bearing fault detection and diagnosis. It is often necessary to study and discuss the problems and shortcomings of the existing methods of fault mechanism analysis and intelligent diagnosis of rolling bearings based on the new theories and methods which are constantly developing.
(1) Aiming at the problem of poor flexibility and high failure rate in traditional wired sensor networks, this paper designs a wireless vibration signal acquisition network for rolling bearings based on the analysis of vibration mechanism, failure reasons and vibration signal characteristics of rolling bearings. The network adopts 250kbps (?) spoon transmission rate and wireless transmission rate with 802.15.4 and ZigBee protocol as the standard. The deployment method can reduce the complexity and failure rate of the system and provide the basic principle support for subsequent bearing fault diagnosis methods.
(2) A stochastic resonance (SR) method based on genetic immune optimization particle swarm optimization (GAIMO-PSO) is proposed to solve the bearing faults in the environment of strong noise and weak useful signals. A genetic immune particle swarm optimization algorithm based on genetic immune is proposed and applied to the optimization process of the key parameters of stochastic resonance. Data are analyzed and verified.
(3) Aiming at the problem that it is difficult to construct an ideal wavelet basis function in the practical application of wavelet theory, a fault diagnosis method of rolling bearing based on the second generation wavelet transform is studied. After demodulation, wavelet envelope spectrum is obtained by spectrum analysis of demodulated signal, and the characteristic information of bearing fault is obtained from envelope spectrum. The application and analysis of bearing experimental data show that this method can accurately extract the characteristic frequency of rolling bearing fault with different degree of damage and realize the quantitative diagnosis of bearing fault.
(4) Aiming at the problem that it is difficult to improve the performance of neural network, an intelligent diagnosis method of rolling bearing based on second generation wavelet transform and neural network is studied. The results show that the combined features extracted from the second generation wavelet decomposition signal can reveal more fault information, and the feature evaluation method can select the corresponding sensitive features according to the health status classification of the diagnosis object. It greatly improves the accuracy of BP neural network classification and verifies the validity of the intelligent diagnosis model.
(5) Aiming at the characteristics that rolling bearing faults belong to typical small samples, the intelligent diagnosis method of rolling bearing based on parameter optimization support vector machine is studied. An intelligent diagnosis model based on genetic immune particle swarm optimization and support vector machine is established. Finally, the model is applied to bearing fault diagnosis. The results show that the algorithm not only optimizes the parameters of SVM classification model automatically, but also improves the SVM score. The fault diagnosis accuracy of the classification model is high, and it has a strong applicability to the classification of fault samples with large dispersion and poor clustering.
Based on the above research, the current diagnosis algorithm for different fault conditions of rolling bearings is optimized and verified by experiments, which provides a new direction for fault diagnosis of rotating machinery.
【學(xué)位授予單位】：東北林業(yè)大學(xué)
【學(xué)位級(jí)別】：博士
【學(xué)位授予年份】：2013
【分類(lèi)號(hào)】：TH133.33;TH165.3
[Abstract]:Rolling bearing is one of the most important parts in rotating machinery, but because of the processing technology, working environment and other reasons, the damage rate is high, and the life of the randomness is large. Exhibition is one of the most important topics in the field of mechanical fault diagnosis.
In this paper, by analyzing the vibration mechanism, failure reasons and signal characteristics of rolling bearings, the acquisition method of bearing vibration signals is improved. The wireless sensor network technology is used to reduce the complexity of fault diagnosis system and improve the efficiency of diagnosis system. There are two ways of mechanism analysis and intelligent diagnosis.The common methods of mechanism analysis are stochastic resonance and wavelet analysis,and the common methods of intelligent diagnosis are neural network and support vector machine.But these methods have their shortcomings in practical application,which affect the effect of bearing fault detection and diagnosis. It is often necessary to study and discuss the problems and shortcomings of the existing methods of fault mechanism analysis and intelligent diagnosis of rolling bearings based on the new theories and methods which are constantly developing.
(1) Aiming at the problem of poor flexibility and high failure rate in traditional wired sensor networks, this paper designs a wireless vibration signal acquisition network for rolling bearings based on the analysis of vibration mechanism, failure reasons and vibration signal characteristics of rolling bearings. The network adopts 250kbps (?) spoon transmission rate and wireless transmission rate with 802.15.4 and ZigBee protocol as the standard. The deployment method can reduce the complexity and failure rate of the system and provide the basic principle support for subsequent bearing fault diagnosis methods.
(2) A stochastic resonance (SR) method based on genetic immune optimization particle swarm optimization (GAIMO-PSO) is proposed to solve the bearing faults in the environment of strong noise and weak useful signals. A genetic immune particle swarm optimization algorithm based on genetic immune is proposed and applied to the optimization process of the key parameters of stochastic resonance. Data are analyzed and verified.
(3) Aiming at the problem that it is difficult to construct an ideal wavelet basis function in the practical application of wavelet theory, a fault diagnosis method of rolling bearing based on the second generation wavelet transform is studied. After demodulation, wavelet envelope spectrum is obtained by spectrum analysis of demodulated signal, and the characteristic information of bearing fault is obtained from envelope spectrum. The application and analysis of bearing experimental data show that this method can accurately extract the characteristic frequency of rolling bearing fault with different degree of damage and realize the quantitative diagnosis of bearing fault.
(4) Aiming at the problem that it is difficult to improve the performance of neural network, an intelligent diagnosis method of rolling bearing based on second generation wavelet transform and neural network is studied. The results show that the combined features extracted from the second generation wavelet decomposition signal can reveal more fault information, and the feature evaluation method can select the corresponding sensitive features according to the health status classification of the diagnosis object. It greatly improves the accuracy of BP neural network classification and verifies the validity of the intelligent diagnosis model.
(5) Aiming at the characteristics that rolling bearing faults belong to typical small samples, the intelligent diagnosis method of rolling bearing based on parameter optimization support vector machine is studied. An intelligent diagnosis model based on genetic immune particle swarm optimization and support vector machine is established. Finally, the model is applied to bearing fault diagnosis. The results show that the algorithm not only optimizes the parameters of SVM classification model automatically, but also improves the SVM score. The fault diagnosis accuracy of the classification model is high, and it has a strong applicability to the classification of fault samples with large dispersion and poor clustering.
Based on the above research, the current diagnosis algorithm for different fault conditions of rolling bearings is optimized and verified by experiments, which provides a new direction for fault diagnosis of rotating machinery.
【學(xué)位授予單位】：東北林業(yè)大學(xué)
【學(xué)位級(jí)別】：博士
【學(xué)位授予年份】：2013
【分類(lèi)號(hào)】：TH133.33;TH165.3
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