發(fā)布時間：2018-06-12 07:49

  本文選題：聲發(fā)射 + 疲勞裂紋��； 參考：《昆明理工大學》2014年碩士論文

【摘要】：滾動軸承旋轉機械中最易損壞的部件之一,其運行狀態(tài)嚴重影響整個設備的運行。因此,滾動軸承的狀態(tài)監(jiān)測與故障診斷對于工程人員有重要意義。現(xiàn)有檢測手段,如振動信號分析、油液分析、溫度診斷分析等并不能有效地診斷出關鍵部位軸承的早期故障。 起重機是廣泛應用的八大特種設備之一。由于數(shù)量巨大,結構復雜,非正常的使用使得安全事故日益頻發(fā)。起重機的常規(guī)無損檢測方法既耗時費力,又需要起重機停止作業(yè),最重要的是無法及時發(fā)現(xiàn)關鍵部位的微弱結構缺陷,導致即便起重機檢測合格仍然有較大的安全隱患。故亟需解決起重機安全檢測的有效性,并對起重機中的結構缺陷源進行粗略定位,再結合其他無損檢測手段對可能的缺陷位置進行仔細檢測。另外,聲發(fā)射對于活性缺陷極其敏感,可以對運行中的起重機進行整體性檢測。 本文以聲發(fā)射檢測技術為手段,對健康滾動軸承和起重機主梁進行了理論和試驗研究,著重對滾動軸承和起重機主梁早期疲勞缺陷和聲發(fā)射檢測的有效性進行了詳細研究。研究工作主要包括以下四個方面： 1)滾動軸承聲發(fā)射信號的特征參數(shù)及信息熵分析。采用了峭度、有效值、峰值和信息熵等對運行中健康滾動軸承聲發(fā)射信號的特征進行了分析,并與振動信號進行了對比分析,得出了聲發(fā)射信號的參數(shù)及信息熵趨勢能更好地反應當前軸承疲勞磨損狀況。 2)通過軸承聲發(fā)射包絡頻譜與振動頻譜對比分析,證明聲發(fā)射信號中故障特征頻率比振動信號更為突出,也說明聲發(fā)射信號比振動信號具有更高的信噪比,能更有效地進行狀態(tài)監(jiān)測和故障診斷。 3)通過后期對實驗采集的起重機主梁聲發(fā)射數(shù)據(jù)波形進行仔細分析,結合聲發(fā)射波形的定義及一些相關的特征參數(shù),從而設定一定的閾值條件,篩選出了可能的裂紋產(chǎn)生及擴展時的聲發(fā)射特征信號。 4)通過對對這些篩選后的聲發(fā)射信號進行峭度、有效值、峰值、波峰系數(shù)、能量等特征參數(shù)分析,結果表明：聲發(fā)射檢測手段可以準確地監(jiān)測起重機主梁這類大型構件中的疲勞裂紋形成及擴展的整個過程。這里還嘗試了缺陷聲發(fā)射源的定位,但是定位精度不是很高。
[Abstract]:One of the most easily damaged parts in rolling bearing rotating machinery, its running state seriously affects the operation of the whole equipment. Therefore, the condition monitoring and fault diagnosis of rolling bearings is of great significance to engineers. The existing detection methods, such as vibration signal analysis, oil analysis, temperature diagnosis and so on, can not effectively diagnose the early failure of bearings in key parts. Crane is one of the eight widely used special equipment. Due to the large number, complex structure and abnormal use, safety accidents are more and more frequent. The conventional nondestructive testing method of crane is time-consuming and laborious, and it also needs the crane to stop its operation. The most important thing is that the weak structural defects of the key parts can not be found in time, which leads to a great potential safety hazard even if the crane is qualified for inspection. Therefore, it is urgent to solve the effectiveness of crane safety inspection, and to roughly locate the structural defect source in the crane, and then combine other non-destructive testing means to carefully detect the possible defect position. In addition, acoustic emission (AE) is very sensitive to active defects and can be used to detect the whole of crane in operation. In this paper, the healthy rolling bearings and crane main beams are studied theoretically and experimentally by means of acoustic emission detection technology. The effectiveness of early fatigue defects and acoustic emission detection of rolling bearings and crane main beams is studied in detail. The research work mainly includes the following four aspects: 1) characteristic parameters and information entropy analysis of acoustic emission signal of rolling bearing. Using kurtosis, effective value, peak value and information entropy, the characteristics of acoustic emission signals of healthy rolling bearings in operation are analyzed and compared with vibration signals. It is concluded that the parameters and information entropy trend of acoustic emission signals can better reflect the current fatigue wear of bearings. 2) by comparing and analyzing the spectrum of acoustic emission envelope and vibration spectrum of bearings, It is proved that the fault characteristic frequency is more prominent than the vibration signal in the acoustic emission signal, and it also shows that the acoustic emission signal has a higher signal-to-noise ratio than the vibration signal. It can more effectively carry out the condition monitoring and fault diagnosis. 3) through the careful analysis of the acoustic emission data waveform of the crane main beam collected in the later stage, combining the definition of the acoustic emission waveform and some relevant characteristic parameters, By setting certain threshold conditions, the acoustic emission characteristic signals of possible crack generation and propagation are screened. 4) by means of kurtosis, effective value, peak value and peak coefficient of these filtered acoustic emission signals, The analysis of energy and other characteristic parameters shows that the acoustic emission detection method can accurately monitor the whole process of fatigue crack formation and propagation in large components such as crane girder. The localization of defective acoustic emission sources is also tried, but the positioning accuracy is not very high.
【學位授予單位】：昆明理工大學
【學位級別】：碩士
【學位授予年份】：2014
【分類號】：TH133.33;TH165.3

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