渦輪鉆具滾動軸承故障診斷系統(tǒng)的研究
發(fā)布時間：2018-11-05 14:33
【摘要】：在旋轉(zhuǎn)機械中,軸承的失效可能會引起系統(tǒng)故障。到目前為止,有很多基于振動的方法來監(jiān)測軸承狀態(tài),在這些方法中都很少考慮到軸承振動的自身特性。在學(xué)位論文中,對正常軸承系統(tǒng)進行了研究分析,對軸承振動的不同類型有了全新的認識。在研究過程中,將軸承的滾動體與滾道之間的接觸設(shè)定為非線性彈簧,將系統(tǒng)轉(zhuǎn)換成一個2-自由度模型。通過研究分析確定了內(nèi)圈的振動特性主要取決于軸承的內(nèi)間隙。軸承故障產(chǎn)生的周期行為會影響到它的混沌行為,并在龐加萊映射中得以體現(xiàn)。同時,混沌的監(jiān)測指標(biāo)如李雅普諾夫指數(shù)、關(guān)聯(lián)維數(shù)以及歸一化信息熵都會發(fā)生變化。為了比較故障診斷技術(shù)中監(jiān)測指標(biāo)的靈敏度和魯棒性,進行了全面的實驗分析。實驗結(jié)果也充分說明了關(guān)聯(lián)維數(shù)、歸一化信息熵和小波近似最大系數(shù)是軸承故障監(jiān)測的可靠指標(biāo)。在論文中,提出了模糊神經(jīng)診斷系統(tǒng)。為了提高軸承故障診斷的可靠性,在新的診斷系統(tǒng)中,對上述的監(jiān)測指標(biāo)進行整合。同時,基于自適應(yīng)模糊神經(jīng)推理系統(tǒng),提出了軸承預(yù)測方案,結(jié)合事先約定好的邏輯準(zhǔn)則,通過理論與實驗研究,證實了這種預(yù)測方案可以用來評估軸承的下一步工作狀態(tài)。并通過實驗驗證了該預(yù)測方案在變轉(zhuǎn)速和變載荷工況下的有效性和可靠性。通過本學(xué)位論文的研究,主要成果總結(jié)如下:1)證實了軸承運動平衡點的個數(shù)取決于軸承內(nèi)間隙。小間隙軸承存在著周期運動,并具有唯一一個平衡點;而對于大間隙的軸承來說,在每一時幀都會有3個平衡點,而且會將相空間劃分為一個不穩(wěn)定的區(qū)域和兩個穩(wěn)定的區(qū)域。對于高轉(zhuǎn)速的工況,軸承的內(nèi)圈會發(fā)生跳動,從一個穩(wěn)定的區(qū)域跳動到另一個區(qū)域,并存在著混沌行為。2)實驗與數(shù)值模擬仿真的結(jié)果證實了正常間隙的滾動軸承和推力球軸承在變轉(zhuǎn)速的工況下存在著寬頻混沌振動。此外,對于正常的軸承系統(tǒng)來說,隨著脈沖的倍增和混沌振動的干擾,軸承會出現(xiàn)故障。實驗結(jié)果與數(shù)值模擬仿真結(jié)果的一致性也證實了軸承故障會嚴重的影響到混沌監(jiān)測指標(biāo):李雅普諾夫指數(shù)、關(guān)聯(lián)維數(shù)和歸一化信息熵。3)基于神經(jīng)網(wǎng)絡(luò)提出軸承故障的診斷系統(tǒng)。將上述監(jiān)測指標(biāo)作為診斷層而輸入,而輸出的結(jié)果就對應(yīng)于軸承的相關(guān)工作狀態(tài)或故障模式。對比分析指出,自適應(yīng)模糊神經(jīng)推理系統(tǒng)能更有效的映射出軸承的工作狀態(tài)。4)針對渦輪鉆具滾動軸承系統(tǒng),研究了神經(jīng)網(wǎng)絡(luò)兩種可行的方法(回歸神經(jīng)網(wǎng)絡(luò)和自適應(yīng)模糊神經(jīng)推理系統(tǒng)),并評估了它們對軸承系統(tǒng)工作狀態(tài)預(yù)判性能的高低。研究表明,一旦被軸承振動數(shù)據(jù)訓(xùn)練后的自適應(yīng)模糊神經(jīng)推理系統(tǒng),神經(jīng)網(wǎng)絡(luò)可以準(zhǔn)確的獲取軸承故障擴散的信息。這種被訓(xùn)練過的方法可以有效的用于對軸承未來的工作狀態(tài)進行預(yù)測,而且適用于變轉(zhuǎn)速變載荷的工況中。同時,結(jié)合所提出的軸承預(yù)判模型,對測試軸承的305個案例進行未來工作狀態(tài)的評估,準(zhǔn)確率達到98%以上。
[Abstract]:Failure of a bearing may cause a system failure in a rotating machine. So far, there are a number of vibration-based methods to monitor the bearing state, in which little consideration is given to the self-characteristics of the bearing vibration. In this paper, the normal bearing system is studied and analyzed, and the different types of bearing vibration have a new understanding. During the study, the contact between the rolling body of the bearing and the raceway is set as a non-linear spring, and the system is converted into a 2-degree-of-freedom model. Through the research and analysis, it is determined that the vibration characteristics of the inner ring mainly depend on the internal clearance of the bearing. The periodic behavior of bearing fault can affect its dynamic behavior, and it can be embodied in the Poincare map. At the same time, the monitoring indexes such as Lyapunov exponent, correlation dimension number and normalized information entropy change. In order to compare the sensitivity and robustness of the monitoring indexes in the fault diagnosis technology, a comprehensive experimental analysis was carried out. The experimental results also show that the correlation dimension number, normalized information entropy and wavelet approximate maximum coefficient are reliable indexes of bearing fault monitoring. In this paper, a fuzzy neural diagnostic system is proposed. In order to improve the reliability of bearing fault diagnosis, in the new diagnosis system, the above-mentioned monitoring indexes are integrated. At the same time, based on the adaptive fuzzy neural reasoning system, a bearing prediction scheme is put forward, combining with the pre-agreed logical criterion, and through theoretical and experimental research, it is confirmed that this kind of prediction scheme can be used to evaluate the next working state of the bearing. The validity and reliability of the prediction scheme under varying rotating speed and variable load condition are verified by experiments. Through the study of this degree thesis, the main results are summarized as follows: 1) The number of the bearing motion balance points is confirmed to be dependent on the bearing inner clearance. The small-gap bearing has a periodic motion and has a unique balance point; and for large-gap bearings, there are three equilibrium points at each time frame, and the phase space is divided into an unstable region and two stable regions. for high rotational speeds, the inner ring of the bearing will bounce and jump from a stable region to the other, The results of experiment and numerical simulation show that the rolling bearing and thrust ball bearing of normal gap have broadband vibration under the condition of variable speed. In addition, for normal bearing systems, the bearings will fail with the multiplication of the pulses and the interference of the backlash. The consistency between the experimental results and the numerical simulation results also confirms that the bearing fault can seriously affect the fault monitoring index: Lyapunov exponent, correlation dimension number and normalized information entropy. 3) Fault diagnosis system based on neural network is proposed. The above-mentioned monitoring index is input as a diagnostic layer, and the result of the output corresponds to the relevant operating state or failure mode of the bearing. According to the comparative analysis, the adaptive fuzzy neural reasoning system can map out the working state of the bearing more effectively. 4) According to the rolling bearing system of the drilling tool, two feasible methods of neural network (regression neural network and adaptive fuzzy neural inference system) are studied. The pre-judging performance of bearing system working state is evaluated and evaluated. The research shows that, once the adaptive fuzzy neural reasoning system after the bearing vibration data is trained, the neural network can accurately acquire the information about the bearing fault diffusion. The trained method can be used for predicting the working state of the bearing in the future, and is suitable for working conditions of variable speed variable load. At the same time, in combination with the proposed bearing prejudging model, 305 cases of the test bearing are evaluated in the future, and the accuracy is above 98%.
【學(xué)位授予單位】：西南石油大學(xué)
【學(xué)位級別】：博士
【學(xué)位授予年份】：2016
【分類號】：TE921.2;TH133.33
本文編號：2312375
[Abstract]:Failure of a bearing may cause a system failure in a rotating machine. So far, there are a number of vibration-based methods to monitor the bearing state, in which little consideration is given to the self-characteristics of the bearing vibration. In this paper, the normal bearing system is studied and analyzed, and the different types of bearing vibration have a new understanding. During the study, the contact between the rolling body of the bearing and the raceway is set as a non-linear spring, and the system is converted into a 2-degree-of-freedom model. Through the research and analysis, it is determined that the vibration characteristics of the inner ring mainly depend on the internal clearance of the bearing. The periodic behavior of bearing fault can affect its dynamic behavior, and it can be embodied in the Poincare map. At the same time, the monitoring indexes such as Lyapunov exponent, correlation dimension number and normalized information entropy change. In order to compare the sensitivity and robustness of the monitoring indexes in the fault diagnosis technology, a comprehensive experimental analysis was carried out. The experimental results also show that the correlation dimension number, normalized information entropy and wavelet approximate maximum coefficient are reliable indexes of bearing fault monitoring. In this paper, a fuzzy neural diagnostic system is proposed. In order to improve the reliability of bearing fault diagnosis, in the new diagnosis system, the above-mentioned monitoring indexes are integrated. At the same time, based on the adaptive fuzzy neural reasoning system, a bearing prediction scheme is put forward, combining with the pre-agreed logical criterion, and through theoretical and experimental research, it is confirmed that this kind of prediction scheme can be used to evaluate the next working state of the bearing. The validity and reliability of the prediction scheme under varying rotating speed and variable load condition are verified by experiments. Through the study of this degree thesis, the main results are summarized as follows: 1) The number of the bearing motion balance points is confirmed to be dependent on the bearing inner clearance. The small-gap bearing has a periodic motion and has a unique balance point; and for large-gap bearings, there are three equilibrium points at each time frame, and the phase space is divided into an unstable region and two stable regions. for high rotational speeds, the inner ring of the bearing will bounce and jump from a stable region to the other, The results of experiment and numerical simulation show that the rolling bearing and thrust ball bearing of normal gap have broadband vibration under the condition of variable speed. In addition, for normal bearing systems, the bearings will fail with the multiplication of the pulses and the interference of the backlash. The consistency between the experimental results and the numerical simulation results also confirms that the bearing fault can seriously affect the fault monitoring index: Lyapunov exponent, correlation dimension number and normalized information entropy. 3) Fault diagnosis system based on neural network is proposed. The above-mentioned monitoring index is input as a diagnostic layer, and the result of the output corresponds to the relevant operating state or failure mode of the bearing. According to the comparative analysis, the adaptive fuzzy neural reasoning system can map out the working state of the bearing more effectively. 4) According to the rolling bearing system of the drilling tool, two feasible methods of neural network (regression neural network and adaptive fuzzy neural inference system) are studied. The pre-judging performance of bearing system working state is evaluated and evaluated. The research shows that, once the adaptive fuzzy neural reasoning system after the bearing vibration data is trained, the neural network can accurately acquire the information about the bearing fault diffusion. The trained method can be used for predicting the working state of the bearing in the future, and is suitable for working conditions of variable speed variable load. At the same time, in combination with the proposed bearing prejudging model, 305 cases of the test bearing are evaluated in the future, and the accuracy is above 98%.
【學(xué)位授予單位】：西南石油大學(xué)
【學(xué)位級別】：博士
【學(xué)位授予年份】：2016
【分類號】：TE921.2;TH133.33
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