盲源分離理論在振動篩軸承故障診斷中的應用
發(fā)布時間：2018-12-18 11:32
【摘要】：隨著我國經濟建設和科學研究事業(yè)的進一步發(fā)展，篩分機械設備所涉及的領域與應用變得越來越廣泛，對于有原材料生產以及應用的領域，都可以看到篩分機械設備，而在這些篩分機械設備中，最常見和常用的設備就是振動篩。在煤炭工業(yè)部門、水利水電部門、交通工業(yè)部門、化工部門甚至在環(huán)衛(wèi)部門都已經應用到了振動篩。可以看出振動篩在各個行業(yè)部門起著至關重要的作用。而振動篩的軸承部分對于振動篩的正常工作有著重要的作用，其工況不僅影響該機器設備本身的安全穩(wěn)定運行，而且還會對后續(xù)生產造成直接影響，，故障嚴重時會造成重大經濟損失，甚至造成機毀人亡的事故，因此對軸承進行故障檢驗技術與分析技術顯得更加迫切。 故障診斷技術是一門新發(fā)展的科學領域，還沒有形成較為完整的科學體系。因此對研究的目的、內容范疇的理解，往往與工程應用背景，乃至工程技術人員的專業(yè)不同而有很大的差異，所以對現有的故障理論方法還有一些不足之處與難題，而最關鍵也是最困難的問題之一就是故障特征信號的特征提取�？梢赃@么說，特征提取是當前故障診斷方面中的一個瓶頸問題，它對于故障診斷的準確性和早期預報的可靠性有著很大的關系。而盲源分離理論為振動信號的處理、故障診斷的識別提供了積極地方法。 但是正如其他算法一樣，它也有自身的限制，其一就是觀測數必須大于振動源數，如果不能滿足這一前提條件，那么分離最終會造成失敗。針對這一限制，本文提出了基于集合平均經驗模態(tài)分解的盲源分離算法(EEMD-BSS)，該算法能很好的克制這一限制，使得在觀測數小于振動源數的情況下也能較好的分離出故障數據，從而達到分離的目的。 最后本文分別使用傳統(tǒng)的盲源分離算法和改進的EEMD-BSS算法對軸承的內外圈實驗故障數據進行了多通道與單通道的故障特征的分離，都較好的完成了分離任務，說明算法的有效性。
[Abstract]:With the further development of economic construction and scientific research in our country, the fields and applications of screening machinery and equipment have become more and more extensive. For the fields where raw materials are produced and applied, we can see screening machinery and equipment. In these screening mechanical equipment, the most common and commonly used equipment is vibrating screen. Shakers have been used in coal, hydropower, transportation, chemicals and even sanitation. It can be seen that the vibrating screen plays a vital role in all sectors of the industry. The bearing part of the vibrating screen plays an important role in the normal operation of the vibrating screen. Its working conditions not only affect the safe and stable operation of the machine itself, but also have a direct impact on the subsequent production. When the fault is serious, it will cause great economic loss, even cause the accident of machine destruction and death, so it is more urgent to carry on the fault inspection and analysis technology to the bearing. Fault diagnosis technology is a newly developed field of science and has not yet formed a relatively complete scientific system. Therefore, the understanding of the purpose and content category of the research is often different from the engineering application background and even the engineering technicians' specialty, so there are still some deficiencies and difficulties in the existing fault theory and methods. One of the most critical and difficult problems is feature extraction of fault feature signals. It can be said that feature extraction is a bottleneck problem in fault diagnosis at present. It has a great relationship with the accuracy of fault diagnosis and the reliability of early prediction. Blind source separation theory provides an active method for vibration signal processing and fault diagnosis. However, like other algorithms, it has its own limitations. One is that the number of observations must be greater than the number of vibration sources. If this precondition is not satisfied, separation will eventually lead to failure. In order to overcome this limitation, a blind source separation algorithm (EEMD-BSS) based on set average empirical mode decomposition (EMD) is proposed in this paper. The fault data can be separated better when the number of observations is less than the number of vibration sources, so as to achieve the purpose of separation. Finally, the traditional blind source separation algorithm and the improved EEMD-BSS algorithm are used to separate the multi-channel and single-channel fault data of the bearing's inner and outer ring experiment respectively. The effectiveness of the algorithm is illustrated.
【學位授予單位】：西安建筑科技大學
【學位級別】：碩士
【學位授予年份】：2012
【分類號】：TH165.3
本文編號：2385789
[Abstract]:With the further development of economic construction and scientific research in our country, the fields and applications of screening machinery and equipment have become more and more extensive. For the fields where raw materials are produced and applied, we can see screening machinery and equipment. In these screening mechanical equipment, the most common and commonly used equipment is vibrating screen. Shakers have been used in coal, hydropower, transportation, chemicals and even sanitation. It can be seen that the vibrating screen plays a vital role in all sectors of the industry. The bearing part of the vibrating screen plays an important role in the normal operation of the vibrating screen. Its working conditions not only affect the safe and stable operation of the machine itself, but also have a direct impact on the subsequent production. When the fault is serious, it will cause great economic loss, even cause the accident of machine destruction and death, so it is more urgent to carry on the fault inspection and analysis technology to the bearing. Fault diagnosis technology is a newly developed field of science and has not yet formed a relatively complete scientific system. Therefore, the understanding of the purpose and content category of the research is often different from the engineering application background and even the engineering technicians' specialty, so there are still some deficiencies and difficulties in the existing fault theory and methods. One of the most critical and difficult problems is feature extraction of fault feature signals. It can be said that feature extraction is a bottleneck problem in fault diagnosis at present. It has a great relationship with the accuracy of fault diagnosis and the reliability of early prediction. Blind source separation theory provides an active method for vibration signal processing and fault diagnosis. However, like other algorithms, it has its own limitations. One is that the number of observations must be greater than the number of vibration sources. If this precondition is not satisfied, separation will eventually lead to failure. In order to overcome this limitation, a blind source separation algorithm (EEMD-BSS) based on set average empirical mode decomposition (EMD) is proposed in this paper. The fault data can be separated better when the number of observations is less than the number of vibration sources, so as to achieve the purpose of separation. Finally, the traditional blind source separation algorithm and the improved EEMD-BSS algorithm are used to separate the multi-channel and single-channel fault data of the bearing's inner and outer ring experiment respectively. The effectiveness of the algorithm is illustrated.
【學位授予單位】：西安建筑科技大學
【學位級別】：碩士
【學位授予年份】：2012
【分類號】：TH165.3
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