本文選題：滾動軸承 + 故障檢測��； 參考：《河南科技大學》2015年碩士論文

【摘要】：滾動軸承是機械設備中最廣泛應用的一種零部件,其運行狀態(tài)可直接決定了整臺機器的工作狀態(tài),傳統(tǒng)的利用單一傳感器識別滾動軸承故障的方式,能夠采集到的故障信息有限,工作環(huán)境中的噪聲干擾又比較大,常常會將微弱的故障信號淹沒,甚至造成誤判或錯判,現(xiàn)階段研究單一方法的比較多,綜合研究的少。加速度和聲發(fā)射傳感器檢測屬于不同的技術檢測手段,兩者之間具有一定相關性和互補性,將它們有機地結合起來,對分析檢測滾動軸承故障很有效果;因此,本文利用信息融合技術把加速度和聲發(fā)射兩種檢測方法融合起來,來研究診斷滾動軸承的故障。本文系統(tǒng)全面地對滾動軸承故障的加速度信號和聲發(fā)射信號的采集、特征提取和特征模型建立的過程進行了探討,提出了基于BP神經(jīng)網(wǎng)絡的多信息融合方法,將該技術應用到滾動軸承故障的診斷中,提高了滾動軸承故障診斷的正確率。首先,根據(jù)實驗對象選取了參數(shù)合適的加速度傳感器和聲發(fā)射傳感器,并對傳感器進行了校驗,以確定傳感器在正常的工作狀態(tài),以博峰軸承試驗臺為基礎,搭建了加速度和聲發(fā)射采集系統(tǒng)進行數(shù)據(jù)采集;然后分析了滾動軸承出現(xiàn)故障時候的振動機理,找出了滾動軸承發(fā)生不同故障時候相對應的理論特征頻率,對安裝在試驗臺上的加速度和聲發(fā)射傳感器所采集的信號進行了時域、頻域分析,并運用希爾伯特振動分解的方法對加速度信號進行降噪,運用小波降噪的方法對聲發(fā)射信號進行濾波降噪,對降噪后的信號包絡解調(diào),獲取相對應信號的包絡譜圖,通過與故障軸承理論特征頻率作對比,診斷軸承的故障類型;最后利用BP神經(jīng)網(wǎng)絡建立了基于多傳感器的信息融合系統(tǒng),并設計計算了故障信號的特征向量,經(jīng)過歸一化處理之后送入網(wǎng)絡進行訓練,直到達到所要求的誤差范圍以內(nèi),實現(xiàn)了對滾動軸承故障的診斷。本文對傳感器技術、濾波降噪、包絡解調(diào)以及神經(jīng)網(wǎng)絡的信息融合技術在滾動軸承檢測方法的應用進行了積極的研究與探索,結合硬件平臺對滾動軸承故障多信息融合監(jiān)測進行了實驗驗證,實驗數(shù)據(jù)表明:單一利用加速度傳感器診斷的準確率是78%,利用聲發(fā)射傳感器判別的準確率是90%,而信息融合后的準確率提高到了94.1%,由此表明,通過多傳感器的信息融合技術對滾動軸承進行故障診斷,可以提高故障診斷的正確率。
[Abstract]:Rolling bearing is one of the most widely used parts in mechanical equipment. Its running state can directly determine the working state of the whole machine.The limited fault information can be collected, and the noise interference in the working environment is relatively large, which often submerges the weak fault signals, and even results in misjudgment or misjudgment. At present, there are more single methods and less comprehensive research.Acceleration and acoustic emission sensor detection belong to different technical detection methods, and they have certain correlation and complementarity. It is very effective to analyze and detect rolling bearing faults by combining them organically.In this paper, the acceleration and acoustic emission detection methods are combined by using information fusion technology to study the fault diagnosis of rolling bearings.In this paper, the acquisition of acceleration signal and acoustic emission signal of rolling bearing fault, the process of feature extraction and the establishment of feature model are systematically discussed, and the method of multi-information fusion based on BP neural network is put forward.This technique is applied to the fault diagnosis of rolling bearing, and the correct rate of fault diagnosis of rolling bearing is improved.Firstly, the acceleration sensor and acoustic emission sensor with suitable parameters are selected according to the experimental object, and the sensor is calibrated to determine the normal working state of the sensor, which is based on the Bofeng bearing test bed.The acceleration and acoustic emission acquisition system is built to collect data, and then the vibration mechanism of rolling bearing is analyzed, and the corresponding theoretical characteristic frequency of rolling bearing when different fault occurs is found out.The signals collected by acceleration and acoustic emission sensors installed on the test bench are analyzed in time domain and frequency domain, and the acceleration signal is de-noised by Hilbert vibration decomposition method.The wavelet denoising method is used to filter the acoustic emission signal, demodulate the signal envelope, obtain the envelope spectrum of the corresponding signal, and diagnose the fault type of the bearing by comparing with the characteristic frequency of the fault bearing theory.Finally, the information fusion system based on multi-sensor is established by using BP neural network, and the eigenvector of the fault signal is designed and calculated. After normalized processing, it is sent to the network for training until it reaches the required error range.The fault diagnosis of rolling bearing is realized.In this paper, the application of sensor technology, filtering and noise reduction, envelope demodulation and neural network information fusion in rolling bearing detection is studied and explored.Combined with hardware platform, the multi-information fusion monitoring of rolling bearing fault is experimentally verified.The experimental data show that the diagnostic accuracy of single acceleration sensor is 78 and that of acoustic emission sensor is 90. The accuracy of information fusion is improved to 94. 1.The fault diagnosis of rolling bearing can be improved by multi-sensor information fusion technology.
【學位授予單位】：河南科技大學
【學位級別】：碩士
【學位授予年份】：2015
【分類號】：TH133.33;TH165.3

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本文編號：1756087