【摘要】：隨著全球新能源技術(shù)的迅猛發(fā)展以及風(fēng)電比重的不斷提高,風(fēng)機(jī)故障診斷與狀態(tài)監(jiān)測(cè)方法研究的重要性及緊迫性也越來(lái)越顯著。本文以風(fēng)機(jī)齒輪箱故障診斷與狀態(tài)監(jiān)測(cè)方法為研究重點(diǎn),以提高風(fēng)機(jī)齒輪箱的可靠性為目的,分別對(duì)風(fēng)機(jī)齒輪箱齒輪、軸承以及油溫進(jìn)行研究。全文圍繞集成學(xué)習(xí)與人工蜂群算法以及改進(jìn)的人工蜂群算法的結(jié)合,對(duì)風(fēng)機(jī)齒輪箱故障診斷和狀態(tài)監(jiān)測(cè)進(jìn)行研究,并結(jié)合研究方法對(duì)風(fēng)機(jī)故障診斷與狀態(tài)監(jiān)測(cè)系統(tǒng)進(jìn)行軟硬件設(shè)計(jì),主要做了以下四個(gè)方面的工作。(1)研究了風(fēng)機(jī)齒輪箱故障形成機(jī)理,利用在實(shí)驗(yàn)平臺(tái)上采集的齒輪、軸承振動(dòng)信號(hào),首先通過(guò)小波包變換方法對(duì)振動(dòng)信號(hào)降噪,對(duì)降噪后信號(hào)提取時(shí)域特征值,然后對(duì)時(shí)域信號(hào)進(jìn)行快速傅立葉變換得頻域信號(hào),再提取頻域特征值,最后將時(shí)域特征值和頻域特征值歸一化,為下面章節(jié)進(jìn)行故障診斷打好基礎(chǔ)。(2)對(duì)風(fēng)機(jī)齒輪箱中的齒輪點(diǎn)蝕、齒輪斷齒、軸承內(nèi)圈損壞以及軸承外圈損壞進(jìn)行診斷,提出基于人工蜂群算法的選擇性神經(jīng)網(wǎng)絡(luò)集成算法(ABCSEN)的故障診斷方法,首先利用UCI數(shù)據(jù)集驗(yàn)證了本文提出的ABCSEN在診斷精度和效率上都優(yōu)于GASEN和Bagging,然后利用齒輪箱歷史故障數(shù)據(jù)訓(xùn)練ABCSEN得到故障診斷模型,最后利用新的故障數(shù)據(jù)測(cè)試故障診斷模型,結(jié)果顯示模型診斷效果較好。(3)以風(fēng)機(jī)齒輪箱油溫?cái)?shù)據(jù)為基礎(chǔ),研究齒輪箱狀態(tài)監(jiān)測(cè)的方法,首先提出改進(jìn)的ABCSEN,即基于動(dòng)態(tài)柯西蜂群算法的選擇性神經(jīng)網(wǎng)絡(luò)集成算法(MCABCSEN),然后利用測(cè)試函數(shù)驗(yàn)證了改進(jìn)算法性能的優(yōu)越性,最后利用南方某風(fēng)場(chǎng)7#風(fēng)機(jī)齒輪箱油溫?cái)?shù)據(jù)以及人為擬合的齒輪箱故障油溫?cái)?shù)據(jù)對(duì)新算法進(jìn)行訓(xùn)練和測(cè)試,結(jié)果顯示新算法狀態(tài)監(jiān)測(cè)靈敏,能提前預(yù)警故障并及時(shí)提醒工作人員,防止損失進(jìn)一步加大。(4)基于研究的故障診斷和狀態(tài)監(jiān)測(cè)的方法,搭建了風(fēng)機(jī)齒輪箱故障診斷與狀態(tài)監(jiān)測(cè)系統(tǒng),較為詳細(xì)的講解了系統(tǒng)硬件和軟件的設(shè)計(jì),最后對(duì)設(shè)計(jì)方案進(jìn)行了簡(jiǎn)單的實(shí)驗(yàn)驗(yàn)證。
[Abstract]:With the rapid development of global new energy technology and the increasing proportion of wind power, the importance and urgency of fan fault diagnosis and condition monitoring are becoming more and more obvious. In order to improve the reliability of fan gearbox, this paper focuses on the fault diagnosis and condition monitoring method of fan gearbox, and respectively studies the fan gearbox gear, bearing and oil temperature. Based on the combination of integrated learning, artificial bee colony algorithm and improved artificial bee colony algorithm, the fault diagnosis and condition monitoring of fan gearbox are studied in this paper. Combined with the research method, the software and hardware design of fan fault diagnosis and condition monitoring system is carried out, mainly in the following four aspects. (1) the mechanism of fan gearbox fault formation is studied, and the gears collected on the experimental platform are used. The vibration signal of bearing is firstly de-noised by wavelet packet transform, then the time domain eigenvalue is extracted from the signal after denoising, then the frequency domain signal is obtained by fast Fourier transform of the time domain signal, and then the frequency domain characteristic value is extracted. Finally, the time-domain and frequency-domain eigenvalues are normalized to provide a good basis for fault diagnosis in the following chapters. (2) the pitting corrosion of the gears in the fan gearbox, gear tooth breaking, bearing inner ring damage and bearing outer ring damage are diagnosed. A fault diagnosis method of selective neural network ensemble algorithm (ABCSEN) based on artificial bee colony algorithm is proposed. Firstly, the UCI data set is used to verify that the ABCSEN presented in this paper is superior to GASEN and Bagging, in accuracy and efficiency. Then the ABCSEN is trained with the historical fault data of the gearbox to obtain the fault diagnosis model. Finally, the new fault diagnosis model is tested with the new fault data. The results show that the model has good diagnostic effect. (3) based on the oil temperature data of fan gear box, the method of monitoring the condition of gear box is studied. The improved ABCSEN, (selective neural network ensemble algorithm (MCABCSEN), based on dynamic Cauchy swarm algorithm) is proposed firstly. Then the superiority of the improved algorithm is verified by using the test function. Finally, the new algorithm is trained and tested by using the oil temperature data of the gear box of a certain wind field in the south and the fault oil temperature data of the gear box fitted artificially. The results show that the new algorithm is sensitive to state monitoring. It can warn the fault in advance and warn the staff in time to prevent the loss from increasing further. (4) based on the method of fault diagnosis and condition monitoring, a fault diagnosis and condition monitoring system for fan gearbox is built. The design of system hardware and software is explained in detail, and the design scheme is verified by experiments.
【學(xué)位授予單位】：上海電機(jī)學(xué)院
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2017
【分類號(hào)】：TM315

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