本文選題：齒輪箱 + 故障診斷��； 參考：《中北大學(xué)》2012年碩士論文

【摘要】：現(xiàn)代化生產(chǎn)不斷朝著高性能、大規(guī)模化、高自動化方向發(fā)展。齒輪箱在機械設(shè)備中占有很重要的作用，用途比較廣泛，是發(fā)生故障頻率較大的部件，及時發(fā)現(xiàn)其故障，并對故障進行分類識別有重大的意義。但是，在齒輪箱的運行過程中存在著背景噪聲，使得采集到的振動信號常常淹沒在噪聲中，，從而無法識別故障。為了能夠準確的診斷故障，需要對采集到的振動信號進行預(yù)處理，從而提高信號的信噪比。 粒子濾波技術(shù)是一種新型的基于模型的狀態(tài)估計技術(shù)，在深入研究粒子濾波原理的基礎(chǔ)上將其應(yīng)用于齒輪箱振動加速度信號降噪處理中。粒子濾波方法處理降噪過程的前提是需要知道系統(tǒng)的狀態(tài)空間模型。在本文的研究過程中，首先對采集的信號建立時序模型，文中選取的是AR模型。所需動態(tài)方程的系數(shù)確定就是利用建模過程中的模型系數(shù)。模型的階數(shù)使用準則函數(shù)法來確定，利用最小二乘法對參數(shù)進行估計計算。本文在試驗對象是JZQ250型號齒輪箱，根據(jù)實驗環(huán)境及背景，以及齒輪和軸承故障故障形式及信號振動特征，利用加速度傳感器采集振動信號，最后對數(shù)據(jù)進行分析處理。 在上述理論分析的基礎(chǔ)上，對實驗室采集的齒輪箱振動加速度信號進行分析處理，用粒子濾波技術(shù)進行降噪，對結(jié)果比較分析發(fā)現(xiàn)粒子濾波降噪后的數(shù)據(jù)特征值都有所減小。神經(jīng)網(wǎng)絡(luò)是一種自適應(yīng)的模式識別技術(shù)，在故障模式識別中的應(yīng)用非常廣泛，理論研究也較成熟。本文對經(jīng)過粒子濾波降噪的數(shù)據(jù)用BP神經(jīng)網(wǎng)絡(luò)進行診斷，取得了理想效果。
[Abstract]:Modern production continues to develop in the direction of high performance, large scale and high automation. Gearbox plays an important role in mechanical equipment and is widely used. Gearbox is the component with high frequency of fault. It is of great significance to find fault in time and to classify and identify faults. However, there is background noise in the operation of the gearbox, so the vibration signals collected are often submerged in the noise, so the fault can not be identified. In order to diagnose the fault accurately, it is necessary to preprocess the collected vibration signal to improve the signal-to-noise ratio (SNR) of the signal. Particle filter is a new model-based state estimation technique, which is applied to the noise reduction of gear box vibration acceleration signal based on the in-depth study of particle filter principle. The premise of particle filter is to know the state space model of the system. In the research process of this paper, the time series model of the collected signal is established, and the AR model is selected in this paper. The coefficients of the required dynamic equations are determined by using the model coefficients in the modeling process. The order of the model is determined by the criterion function method, and the parameters are estimated by the least square method. The test object is JZQ250 gearbox. According to the experimental environment and background, gear and bearing fault forms and signal vibration characteristics, the acceleration sensor is used to collect vibration signals, and finally the data are analyzed and processed. On the basis of the above theoretical analysis, the vibration acceleration signals of the gearbox collected in the laboratory are analyzed and processed, and the noise reduction is carried out by the particle filter technique. The comparison of the results shows that the eigenvalues of the data after the reduction of the noise by the particle filter are all reduced. Neural network is an adaptive pattern recognition technology, which is widely used in fault pattern recognition, and the theoretical research is also mature. In this paper, BP neural network is used to diagnose the noise reduction data by particle filter, and the ideal results are obtained.
【學(xué)位授予單位】：中北大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2012
【分類號】：TH165.3

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