本文選題：軸承 + 聲發(fā)射　； 參考：《沈陽工業(yè)大學(xué)》2017年碩士論文

【摘要】：與核能發(fā)電和火力發(fā)電相比,風(fēng)力發(fā)電更綠色環(huán)保、資源充足。隨著風(fēng)力發(fā)電行業(yè)迅猛發(fā)展,風(fēng)機裝機量逐年遞增。如何及時發(fā)現(xiàn)風(fēng)機故障,準確判斷故障類型,保證風(fēng)力發(fā)電機組安全可靠運行成為世界各國主要研究的問題。本文以風(fēng)機軸承為研究對象,搭建風(fēng)機軸承故障監(jiān)測系統(tǒng),利用該系統(tǒng)對軸承運行狀態(tài)進行實時監(jiān)測和故障診斷研究。該系統(tǒng)利用光柵傳感器提取軸承聲發(fā)射信號。當軸承發(fā)生故障時,聲發(fā)射現(xiàn)象會引起軸承表面位移變化;光柵傳感器會將位移變化量轉(zhuǎn)換成電信號;AD9467采集傳感器輸出的電信號;STM32F429對信號進行濾波、細分和故障診斷。故障發(fā)生時,該系統(tǒng)有報警提示功能。此外,該系統(tǒng)還具有數(shù)據(jù)存儲功能和網(wǎng)絡(luò)傳輸功能。光柵傳感器的分辨力決定了軸承故障聲發(fā)射信號識別能力。為提高系統(tǒng)故障診斷的精準性,本文對莫爾條紋信號進行高倍細分。首先,通過智能小波閾值降噪方法對信號進行小波分解和降噪。其次,針對莫爾條紋信號含直流電平、幅值不等、相位不正交等現(xiàn)象,進行莫爾條紋信號補償可有效提高細分精度。再次,本文對基于L-M的BP神經(jīng)網(wǎng)絡(luò)莫爾條紋信號細分方法進行了深入研究。通過增加新的判斷條件來改進L-M算法,根據(jù)本次訓(xùn)練結(jié)果誤差和上一次訓(xùn)練結(jié)果誤差關(guān)系可以得到新的權(quán)值。該方法能夠提高神經(jīng)網(wǎng)絡(luò)訓(xùn)練速度和結(jié)果精度。將其結(jié)果與RBF神經(jīng)網(wǎng)絡(luò)莫爾條紋細分方法所得結(jié)果進行對比,實驗結(jié)果表明,基于改進L-M的BP神經(jīng)網(wǎng)絡(luò)莫爾條紋信號細分方法速度更快、誤差波動范圍更小。然后,對細分后得到的位移值做頻譜分析可得不同頻率對應(yīng)的幅值。最后,通過比較幅值和軸承臨界故障時的幅值可以判斷軸承是否有故障。仿真結(jié)果和實驗測試結(jié)果表明,基于改進L-M的BP神經(jīng)網(wǎng)絡(luò)莫爾條紋信號細分方法可以實現(xiàn)20000細分,分辨力達到1nm,能夠識別納米級軸承裂紋故障的聲發(fā)射信號。表明光柵傳感器通過莫爾條紋信號細分后可以用于提取軸承故障聲發(fā)射信號,通過小波神經(jīng)網(wǎng)絡(luò)故障診斷方法能判斷出軸承裂紋故障。
[Abstract]:Compared with nuclear power generation and thermal power generation, wind power generation is greener and more resources. With the rapid development of the wind power generation industry, the volume of wind turbines is increasing year by year. How to find out the blower fault in time, accurately determine the type of fault and ensure the safe and reliable operation of the wind turbine is the main problem in the world. This paper is based on the wind turbine shaft. As the research object, a fault monitoring system for the bearing of the fan is built, and the system is used to monitor and diagnose the bearing state of the bearing in real time. The system uses a grating sensor to extract the acoustic emission signal of the bearing. When the bearing occurs, the acoustic emission will cause the change of the displacement of the bearing surface, and the grating sensor will change the displacement. The electrical signal is converted into an electrical signal; AD9467 takes the electrical signal output by the sensor; STM32F429 filters, subdivides and diagnoses the signal. When the fault occurs, the system has alarm and prompt function. In addition, the system also has data storage function and network transmission function. The resolution of the grating sensor determines the identification of the acoustic emission signal of the bearing fault. In order to improve the accuracy of the system fault diagnosis, the moire stripe signal is subdivided in high times. First, wavelet decomposition and noise reduction are carried out by the intelligent wavelet threshold denoising method. Secondly, the moire fringe signal compensation can be effectively proposed for the moire fringe signal containing the DC level, the amplitude is unequal and the phase is not orthogonal. Thirdly, the L-M based BP neural network Moire stripe signal subdivision method is deeply studied in this paper. By adding new judgment conditions to improve the L-M algorithm, new weights can be obtained according to the error of this training result and the error relationship of the previous training results. This method can improve the training speed of neural network and the training speed of neural network. The result is compared with the results obtained from the RBF neural network moire fringe subdivision method. The experimental results show that the moire fringe signal subdivision method based on the improved L-M neural network is faster and the range of error fluctuation is smaller. Then, the spectrum analysis of the displacement values obtained after the subdivision can be obtained with the corresponding amplitude of different frequencies. Finally, by comparing the amplitude and the amplitude of the critical fault of the bearing, the fault of the bearing can be judged. The simulation results and the experimental test results show that the 20000 subdivision method based on the improved L-M BP neural network moire fringe signal subdivision can be realized and the resolution can reach 1nm. After subdivision of the moire fringe signal, the light grating sensor can be used to extract the acoustic emission signal of the bearing fault, and the fault of the bearing crack can be judged by the wavelet neural network fault diagnosis method.
【學(xué)位授予單位】：沈陽工業(yè)大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2017
【分類號】：TM614;TP212

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