基于盲源分離和多尺度熵（MSE）的滾動軸承故障診斷
發(fā)布時間：2018-07-25 13:12
【摘要】：滾動軸承是許多機(jī)械設(shè)備的重要部件之一,其能否正常運(yùn)行關(guān)系到機(jī)械設(shè)備的正常與否。傳統(tǒng)滾動軸承故障診斷方法常常忽略傳感器采集的振動信號是多個源信號混合的事實,直接采用適用于平穩(wěn)信號分析的傅里葉變換對非平穩(wěn)振動信號進(jìn)行處理,難以全面、準(zhǔn)確地分析源信號所包含的故障類型。針對傳統(tǒng)滾動軸承故障診斷技術(shù)的不足,本文提出基于盲源分離和多尺度熵的滾動軸承故障診斷方法。滾動軸承作為一種精密元件,當(dāng)軸承某一部件出現(xiàn)異常時,軸承其他部件往往會產(chǎn)生連鎖反應(yīng),傳感器采集到的振動數(shù)據(jù)往往是多個部件異常振動的疊加。為了更加精準(zhǔn)地識別各個異常情況,本文提出基于盲源分離的單通道振動信號分離方法,該方法利用極點對稱模態(tài)分解將欠定盲源分離問題轉(zhuǎn)換為正定盲源分離問題,然后采用基于時頻分析的盲源分離方法分離源信號。仿真結(jié)果表明,該方法分離出的源信號與實際源信號相關(guān)系數(shù)分別達(dá)到0.9771、0.9784、0.9660,能夠以較高的分離精度將單個多源混合信號逐一分離出來。針對分離信號的特征提取,提出采用經(jīng)驗?zāi)B(tài)分解和多尺度熵方法來提取分離信號的特征量。經(jīng)驗?zāi)B(tài)分解方法在使用過程中,常常受到端點效應(yīng)的影響。針對經(jīng)驗?zāi)B(tài)分解方法端點效應(yīng)問題,提出基于波形平均的端點效應(yīng)抑制方法,根據(jù)信號自身特性來延拓信號,具有較好的自適應(yīng)性,能夠較好地抑制經(jīng)驗?zāi)B(tài)分解端點效應(yīng)。為了有效識別故障類型,采用BP神經(jīng)網(wǎng)絡(luò)對故障進(jìn)行辨識。實驗結(jié)果表明,本文提出的滾動軸承故障診斷方法對軸承內(nèi)圈故障、外圈故障以及正常狀態(tài)的識別率分別達(dá)到97%、86%、90%,在一定程度上能夠有效識別滾動軸承的故障類型。本文采用C#和MATLAB混合編程技術(shù),開發(fā)了一套滾動軸承離線故障診斷分析軟件。該軟件通過對實際滾動軸承振動信號的分析,進(jìn)一步驗證了本文方法在實際應(yīng)用中的有效性。
[Abstract]:Rolling bearing is one of the important parts of many mechanical equipments. The traditional fault diagnosis method of rolling bearing often ignores the fact that the vibration signal collected by the sensor is a mixture of multiple sources, so it is difficult to deal with the non-stationary vibration signal directly by Fourier transform, which is suitable for the stationary signal analysis. Accurately analyze the type of fault contained in the source signal. Aiming at the shortcomings of traditional rolling bearing fault diagnosis techniques, a fault diagnosis method for rolling bearings based on blind source separation and multi-scale entropy is proposed in this paper. As a kind of precision element, when one part of the bearing is abnormal, the other parts of the bearing often produce chain reaction, and the vibration data collected by the sensor are often the superposition of the abnormal vibration of several parts. In order to identify anomalies more accurately, a single channel vibration signal separation method based on blind source separation is proposed in this paper. In this method, the problem of under-determined blind source separation is transformed into a positive definite blind source separation problem by pole symmetric mode decomposition. Then the blind source separation method based on time frequency analysis is used to separate the source signal. The simulation results show that the correlation coefficient between the source signal and the actual source signal obtained by this method is 0.9771n0.9784n0.9660, and the single multi-source mixed signal can be separated one by one with higher separation accuracy. An empirical mode decomposition (EMD) and multi-scale entropy method is proposed to extract the characteristic quantity of the separation signal. The empirical mode decomposition (EMD) method is often affected by the endpoint effect. To solve the endpoint effect problem of empirical mode decomposition method, a waveform averaging based endpoint effect suppression method is proposed, which extends the signal according to its own characteristics. It is self-adaptive and can suppress the end-point effect of empirical mode decomposition. In order to identify the fault type effectively, BP neural network is used to identify the fault. The experimental results show that the fault diagnosis method of rolling bearing presented in this paper can identify the inner ring fault, outer ring fault and normal state of bearing, respectively, and the recognition rate is 97 / 86 / 90, which can effectively identify the fault type of rolling bearing to a certain extent. In this paper, a software for off-line fault diagnosis and analysis of rolling bearing is developed by using C # and MATLAB mixed programming technology. The software further verifies the effectiveness of this method in practical application by analyzing the vibration signals of actual rolling bearings.
【學(xué)位授予單位】：西南科技大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2017
【分類號】：TH133.33
[Abstract]:Rolling bearing is one of the important parts of many mechanical equipments. The traditional fault diagnosis method of rolling bearing often ignores the fact that the vibration signal collected by the sensor is a mixture of multiple sources, so it is difficult to deal with the non-stationary vibration signal directly by Fourier transform, which is suitable for the stationary signal analysis. Accurately analyze the type of fault contained in the source signal. Aiming at the shortcomings of traditional rolling bearing fault diagnosis techniques, a fault diagnosis method for rolling bearings based on blind source separation and multi-scale entropy is proposed in this paper. As a kind of precision element, when one part of the bearing is abnormal, the other parts of the bearing often produce chain reaction, and the vibration data collected by the sensor are often the superposition of the abnormal vibration of several parts. In order to identify anomalies more accurately, a single channel vibration signal separation method based on blind source separation is proposed in this paper. In this method, the problem of under-determined blind source separation is transformed into a positive definite blind source separation problem by pole symmetric mode decomposition. Then the blind source separation method based on time frequency analysis is used to separate the source signal. The simulation results show that the correlation coefficient between the source signal and the actual source signal obtained by this method is 0.9771n0.9784n0.9660, and the single multi-source mixed signal can be separated one by one with higher separation accuracy. An empirical mode decomposition (EMD) and multi-scale entropy method is proposed to extract the characteristic quantity of the separation signal. The empirical mode decomposition (EMD) method is often affected by the endpoint effect. To solve the endpoint effect problem of empirical mode decomposition method, a waveform averaging based endpoint effect suppression method is proposed, which extends the signal according to its own characteristics. It is self-adaptive and can suppress the end-point effect of empirical mode decomposition. In order to identify the fault type effectively, BP neural network is used to identify the fault. The experimental results show that the fault diagnosis method of rolling bearing presented in this paper can identify the inner ring fault, outer ring fault and normal state of bearing, respectively, and the recognition rate is 97 / 86 / 90, which can effectively identify the fault type of rolling bearing to a certain extent. In this paper, a software for off-line fault diagnosis and analysis of rolling bearing is developed by using C # and MATLAB mixed programming technology. The software further verifies the effectiveness of this method in practical application by analyzing the vibration signals of actual rolling bearings.
【學(xué)位授予單位】：西南科技大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2017
【分類號】：TH133.33
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