滾動軸承故障特征提取與診斷方法研究
發(fā)布時間：2019-01-03 10:49
【摘要】：隨著科學(xué)技術(shù)的迅猛發(fā)展和現(xiàn)代化大生產(chǎn)的日益普及,旋轉(zhuǎn)機(jī)械不斷朝著大型化、復(fù)雜化、高速化和自動化方向發(fā)展,這對設(shè)備的運行安全提出了更高的要求。滾動軸承作為旋轉(zhuǎn)機(jī)械中應(yīng)用最為廣泛的部件之一,直接決定著整個機(jī)械系統(tǒng)能否正�？煽窟\行,深入開展?jié)L動軸承故障診斷和狀態(tài)檢測技術(shù)的研究,對有效避免生產(chǎn)中重大事故的發(fā)生,具有重要的學(xué)術(shù)意義和工程應(yīng)用價值。本文在詳細(xì)論述滾動軸承故障信號降噪、特征提取、復(fù)合與智能故障診斷研究現(xiàn)狀的基礎(chǔ)上,從振動信號分析與處理方法著手,針對滾動軸承故障特征提取與診斷中所涉及的幾個關(guān)鍵問題進(jìn)行了深入研究,在滾動軸承故障特征提取、微弱故障診斷、復(fù)合故障特征分離、故障模式智能識別和運行狀態(tài)檢測方面取得了一些研究成果,論文的創(chuàng)新點及主要工作如下：(1)傳感器采集的滾動軸承故障振動信號頻率成分比較復(fù)雜,在無關(guān)頻率成分及噪聲的干擾下,軸承故障特征常常難以準(zhǔn)確提取。針對此問題,本文基于倒譜預(yù)白化和奇異值分解重構(gòu)提出了一種故障特征提取方法。該方法通過倒譜預(yù)白化處理軸承故障信號,消除了信號中離散頻率成分和諧波分量的干擾；然后進(jìn)行奇異值分解,并基于奇異值最大差分譜重構(gòu)信號,有效濾除了信號中的干擾噪聲。實驗證明,該方法能準(zhǔn)確提取滾動軸承的故障特征。(2)在惡劣的工作環(huán)境下,滾動軸承振動信號中�；祀s有強烈的背景噪聲,尤其是故障特征較為微弱時,極易被噪聲所掩蓋,軸承故障難以診斷。因此,本文基于自適應(yīng)多尺度自互補Top-Hat變換提出了一種軸承微弱故障診斷方法。形態(tài)學(xué)自互補Top-Hat變換濾波器處理軸承故障信號時,能夠抑制信號中的強背景噪聲,并有效增強軸承的故障沖擊特征。同時,為達(dá)到兼顧抗噪性和信號細(xì)節(jié)保持性的目的,構(gòu)建了多尺度形態(tài)學(xué)濾波器,通過比較不同尺度下濾波信號的故障特征能量比,自適應(yīng)確定了最優(yōu)結(jié)構(gòu)元素的尺度。(3)滾動軸承出現(xiàn)復(fù)合故障時,在單通道振動信號中軸承不同元件的故障特征彼此混雜,難以分離。為解決此問題,本文基于改進(jìn)諧波小波包分解提出了一種軸承復(fù)合故障特征分離方法。該方法可以根據(jù)需要對信號頻帶進(jìn)行任意劃分,克服了傳統(tǒng)諧波小波包分解后子信號個數(shù)及帶寬范圍受二進(jìn)制劃分的缺陷,通過計算子信號中各單一故障信號的權(quán)重因子,重構(gòu)分離出軸承各單一故障信號,有效實現(xiàn)了滾動軸承復(fù)合故障特征的分離。(4)針對以故障模式識別與運行狀態(tài)檢測為主要內(nèi)容的滾動軸承智能診斷問題,本文采用Hermitian小波對軸承信號進(jìn)行連續(xù)小波變換,再結(jié)合樣本熵理論,提出以時間-小波能量譜樣本熵作為特征參數(shù),對軸承智能診斷進(jìn)行研究。該方法將時間-小波能量譜樣本熵作為軸承不同工況下樣本信號的特征向量,通過支持向量機(jī)分類算法實現(xiàn)了軸承不同故障模式的智能識別。之后將時間-小波能量譜樣本熵用于滾動軸承運行狀態(tài)檢測,計算全壽命周期實驗數(shù)據(jù)的時間-小波能量譜樣本熵,按照時間順序排列,繪制出了軸承運行狀態(tài)曲線,通過判斷曲線走勢可有效診斷出軸承早期故障的發(fā)生。
[Abstract]:With the rapid development of science and technology and the increasing popularity of modern production, the rotating machinery has been developing in the direction of large-scale, complicated, high-speed and automatic. As one of the most widely used parts in the rotating machinery, the rolling bearing directly determines whether the whole mechanical system can operate normally and reliably, and the research of the fault diagnosis and the state detection technology of the rolling bearing is carried out, and the occurrence of a major accident in the production can be effectively avoided, and has important academic significance and engineering application value. In this paper, on the basis of the present situation of noise reduction, feature extraction, compound and intelligent fault diagnosis of rolling bearing fault signal, this paper proceeds from the analysis and processing method of vibration signal, and studies the key problems involved in the feature extraction and diagnosis of rolling bearing. Some research achievements have been made in the fault feature extraction, weak fault diagnosis, compound fault feature separation, fault mode intelligent identification and operation state detection of rolling bearing, and the innovation point and main work of the paper are as follows: (1) The frequency component of the fault vibration signal of the rolling bearing collected by the sensor is more complex, and the fault characteristics of the bearing are often difficult to be extracted accurately under the interference of independent frequency components and noise. In this paper, a fault feature extraction method is proposed based on inverse spectrum pre-whitening and singular value decomposition reconstruction. The method eliminates the interference of the discrete frequency component and the harmonic component in the signal through the cepstrum pre-whitening treatment bearing fault signal, then performs singular value decomposition, and reconstructs the signal based on the singular value maximum difference spectrum, and effectively filters out the interference noise in the signal. The experimental results show that the method can accurately extract the fault features of the rolling bearing. (2) In the severe working environment, the vibration signal of the rolling bearing is often mixed with strong background noise, especially when the fault characteristic is weak, it is very easy to be covered by the noise, and the bearing fault is difficult to diagnose. Therefore, based on the self-adaptive multi-scale self-complementary Top-Hat transformation, a method of bearing weak fault diagnosis is proposed. When the morphology self-complementary Top-Hat transform filter is used to process the bearing fault signal, the strong background noise in the signal can be suppressed, and the fault impact characteristic of the bearing can be effectively enhanced. At the same time, the multi-scale morphological filter is constructed for the purpose of achieving both anti-noise and signal detail retention, and the scale of the optimal structural element is determined by comparing the fault characteristic energy ratio of the filtered signal at different scales. (3) The fault features of different components of bearing in single-channel vibration signal are mixed with each other, and it is difficult to separate. In order to solve this problem, a method for separating a bearing composite fault feature based on improved harmonic wavelet packet decomposition is presented in this paper. According to the method, the signal frequency band can be arbitrarily divided according to needs, the defects that the number of the sub-signals and the bandwidth range of the traditional harmonic wavelet packet decomposition are subjected to binary division are overcome, the weight factors of each single fault signal in the sub-signal are calculated, and the single fault signals of the bearing are reconstructed and separated, and the separation of the composite fault characteristic of the rolling bearing is effectively realized. (4) According to the intelligent diagnosis of rolling bearing with fault pattern recognition and operation state detection as the main content, this paper uses Hermitian wavelet to perform continuous wavelet transform on the bearing signal, and then combines the sample entropy theory, and puts forward the time-wavelet energy spectrum sample entropy as the characteristic parameter. The intelligent diagnosis of bearing is studied. The method takes the time-small-wave energy spectrum sample entropy as the characteristic vector of the sample signal under different working conditions of the bearing, and realizes the intelligent identification of different fault modes of the bearing by supporting the vector machine classification algorithm. then, the time-small-wave energy spectrum sample entropy is used for detecting the running state of the rolling bearing, the time-small-wave energy spectrum sample entropy of the whole life cycle experimental data is calculated, and the running state curve of the bearing is drawn according to the time sequence, and the occurrence of the early fault of the bearing can be effectively diagnosed by judging the trend of the curve.
【學(xué)位授予單位】：華北電力大學(xué)(北京)
【學(xué)位級別】：博士
【學(xué)位授予年份】：2016
【分類號】：TH133.33
[Abstract]:With the rapid development of science and technology and the increasing popularity of modern production, the rotating machinery has been developing in the direction of large-scale, complicated, high-speed and automatic. As one of the most widely used parts in the rotating machinery, the rolling bearing directly determines whether the whole mechanical system can operate normally and reliably, and the research of the fault diagnosis and the state detection technology of the rolling bearing is carried out, and the occurrence of a major accident in the production can be effectively avoided, and has important academic significance and engineering application value. In this paper, on the basis of the present situation of noise reduction, feature extraction, compound and intelligent fault diagnosis of rolling bearing fault signal, this paper proceeds from the analysis and processing method of vibration signal, and studies the key problems involved in the feature extraction and diagnosis of rolling bearing. Some research achievements have been made in the fault feature extraction, weak fault diagnosis, compound fault feature separation, fault mode intelligent identification and operation state detection of rolling bearing, and the innovation point and main work of the paper are as follows: (1) The frequency component of the fault vibration signal of the rolling bearing collected by the sensor is more complex, and the fault characteristics of the bearing are often difficult to be extracted accurately under the interference of independent frequency components and noise. In this paper, a fault feature extraction method is proposed based on inverse spectrum pre-whitening and singular value decomposition reconstruction. The method eliminates the interference of the discrete frequency component and the harmonic component in the signal through the cepstrum pre-whitening treatment bearing fault signal, then performs singular value decomposition, and reconstructs the signal based on the singular value maximum difference spectrum, and effectively filters out the interference noise in the signal. The experimental results show that the method can accurately extract the fault features of the rolling bearing. (2) In the severe working environment, the vibration signal of the rolling bearing is often mixed with strong background noise, especially when the fault characteristic is weak, it is very easy to be covered by the noise, and the bearing fault is difficult to diagnose. Therefore, based on the self-adaptive multi-scale self-complementary Top-Hat transformation, a method of bearing weak fault diagnosis is proposed. When the morphology self-complementary Top-Hat transform filter is used to process the bearing fault signal, the strong background noise in the signal can be suppressed, and the fault impact characteristic of the bearing can be effectively enhanced. At the same time, the multi-scale morphological filter is constructed for the purpose of achieving both anti-noise and signal detail retention, and the scale of the optimal structural element is determined by comparing the fault characteristic energy ratio of the filtered signal at different scales. (3) The fault features of different components of bearing in single-channel vibration signal are mixed with each other, and it is difficult to separate. In order to solve this problem, a method for separating a bearing composite fault feature based on improved harmonic wavelet packet decomposition is presented in this paper. According to the method, the signal frequency band can be arbitrarily divided according to needs, the defects that the number of the sub-signals and the bandwidth range of the traditional harmonic wavelet packet decomposition are subjected to binary division are overcome, the weight factors of each single fault signal in the sub-signal are calculated, and the single fault signals of the bearing are reconstructed and separated, and the separation of the composite fault characteristic of the rolling bearing is effectively realized. (4) According to the intelligent diagnosis of rolling bearing with fault pattern recognition and operation state detection as the main content, this paper uses Hermitian wavelet to perform continuous wavelet transform on the bearing signal, and then combines the sample entropy theory, and puts forward the time-wavelet energy spectrum sample entropy as the characteristic parameter. The intelligent diagnosis of bearing is studied. The method takes the time-small-wave energy spectrum sample entropy as the characteristic vector of the sample signal under different working conditions of the bearing, and realizes the intelligent identification of different fault modes of the bearing by supporting the vector machine classification algorithm. then, the time-small-wave energy spectrum sample entropy is used for detecting the running state of the rolling bearing, the time-small-wave energy spectrum sample entropy of the whole life cycle experimental data is calculated, and the running state curve of the bearing is drawn according to the time sequence, and the occurrence of the early fault of the bearing can be effectively diagnosed by judging the trend of the curve.
【學(xué)位授予單位】：華北電力大學(xué)(北京)
【學(xué)位級別】：博士
【學(xué)位授予年份】：2016
【分類號】：TH133.33
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