本文選題：渦旋壓縮機 + 閾值��； 參考：《蘭州理工大學》2016年碩士論文

【摘要】：近年來隨著渦旋壓縮機應用領域的不斷擴展,不同的工作環(huán)境,引發(fā)渦旋壓縮機故障的因素變得愈加復雜,渦旋壓縮機的故障診斷也變得更有必要。渦旋壓縮機的振動測試實驗平臺的搭建和對其振動噪聲的分析顯得尤為重要。但由于渦旋壓縮機工作環(huán)境較為復雜,測試系統(tǒng)較難搭建等因素的影響,難以滿足實時監(jiān)測、診斷的要求。此外渦旋壓縮機的應用時間較短,故障數據庫不健全,且振動源較多,殼體信號為非平穩(wěn)性和非線性,因此故障特征難以準確地區(qū)分且故障診斷過程較為復雜。本文以渦旋壓縮機振動測試實驗平臺為基礎,將信息熵和熵距相結合實現對非平穩(wěn)信號進行故障判別。具體實施過程如下:(1)從振動信號分析的思路出發(fā),結合信息論中熵和歐氏距離理論,提出一種基于信息熵和熵距的故障診斷方法。(2)搭建改進已有實驗平臺,安裝傳感器,更換硬件,調試軟件進行試驗。采集信號,建立機體正常運行的特征標準和四種典型故障的特征標準。(3)使用實驗平臺分別模擬四種故障,利用Matlab信號處理工具箱的強大功能,對采集到的故障信號進行基于閾值的小波包去噪,分解和重構,提取信息熵作為故障特征。(4)將提取的故障特征,與典型故障對比,使用熵距計算方法得到熵距,綜合考慮信息熵和熵距對測試信號進行故障診斷。實驗結果表明:該方法對轉子不平衡和軸承故障的故障診斷具有很高的準確度,對其余故障也能很好的區(qū)分。信息熵能反映故障類型及故障嚴重程度,而熵距曲線圖則進一步提高診斷的準確性。同時其也能夠表示復合故障的部分特征,因而給復合故障診斷提供一種新的思路,也為渦旋壓縮機的結構設計、制造加工和安裝檢測提供一定的幫助。
[Abstract]:In recent years, with the continuous expansion of the application field of scroll compressor, the factors causing the scroll compressor fault become more and more complex in different working environment, and the fault diagnosis of scroll compressor becomes more necessary. It is very important to build the experimental platform and analyze the vibration and noise of scroll compressor. However, due to the complex working environment of the scroll compressor and the difficulty of setting up the test system, it is difficult to meet the requirements of real-time monitoring and diagnosis. In addition, the application time of scroll compressor is short, the fault database is not perfect, and there are many vibration sources, the shell signal is non-stationary and nonlinear, so it is difficult to distinguish the fault features accurately and the fault diagnosis process is more complicated. In this paper, based on the vibration test platform of scroll compressor, the information entropy and entropy distance are combined to realize the fault identification of non-stationary signals. The concrete implementation process is as follows: (1) starting from the idea of vibration signal analysis and combining the theory of entropy and Euclidean distance in information theory, a fault diagnosis method based on information entropy and entropy distance. Replace hardware and debug software for test. Collect the signal, establish the characteristic standard of the normal operation of the body and the characteristic standard of four kinds of typical faults. Use the experiment platform to simulate the four kinds of faults, and utilize the powerful function of the Matlab signal processing toolbox. The acquired fault signal is de-noised, decomposed and reconstructed based on threshold wavelet packet, and the information entropy is extracted as the fault feature. The extracted fault feature is compared with the typical fault, and the entropy distance is obtained by using entropy distance calculation method. The information entropy and entropy distance are considered comprehensively to diagnose the fault of test signal. The experimental results show that the method has a high accuracy for the fault diagnosis of rotor unbalance and bearing fault, and can distinguish the other faults well. Information entropy can reflect fault type and fault severity, while entropy distance curve can further improve the accuracy of diagnosis. At the same time, it can also express some characteristics of complex faults, which provides a new idea for complex fault diagnosis, and also provides some help for the structure design, manufacturing and processing of scroll compressor and installation and detection.
【學位授予單位】：蘭州理工大學
【學位級別】：碩士
【學位授予年份】：2016
【分類號】：TH45

【參考文獻】

相關期刊論文 前10條

1 肖根福;劉國平;王俊亭;宋紅滾;;基于動網格的渦旋壓縮機內部流場數值模擬[J];機床與液壓;2013年01期

2 劉濤;黃成東;;基于信息熵的渦旋壓縮機故障診斷研究[J];壓縮機技術;2012年01期

3 吳炳勝;蘇茹茹;吳繼民;李文選;;基于小波包分解與重構的齒輪泵故障分析[J];煤礦機械;2012年02期

4 李超;余洋;趙Z，

本文編號：1948028