發(fā)布時間：2018-10-05 13:29

【摘要】：隨著全球新能源技術(shù)的迅猛發(fā)展以及風電比重的不斷提高,風機故障診斷與狀態(tài)監(jiān)測方法研究的重要性及緊迫性也越來越顯著。本文以風機齒輪箱故障診斷與狀態(tài)監(jiān)測方法為研究重點,以提高風機齒輪箱的可靠性為目的,分別對風機齒輪箱齒輪、軸承以及油溫進行研究。全文圍繞集成學習與人工蜂群算法以及改進的人工蜂群算法的結(jié)合,對風機齒輪箱故障診斷和狀態(tài)監(jiān)測進行研究,并結(jié)合研究方法對風機故障診斷與狀態(tài)監(jiān)測系統(tǒng)進行軟硬件設(shè)計,主要做了以下四個方面的工作。(1)研究了風機齒輪箱故障形成機理,利用在實驗平臺上采集的齒輪、軸承振動信號,首先通過小波包變換方法對振動信號降噪,對降噪后信號提取時域特征值,然后對時域信號進行快速傅立葉變換得頻域信號,再提取頻域特征值,最后將時域特征值和頻域特征值歸一化,為下面章節(jié)進行故障診斷打好基礎(chǔ)。(2)對風機齒輪箱中的齒輪點蝕、齒輪斷齒、軸承內(nèi)圈損壞以及軸承外圈損壞進行診斷,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)訓練ABCSEN得到故障診斷模型,最后利用新的故障數(shù)據(jù)測試故障診斷模型,結(jié)果顯示模型診斷效果較好。(3)以風機齒輪箱油溫數(shù)據(jù)為基礎(chǔ),研究齒輪箱狀態(tài)監(jiān)測的方法,首先提出改進的ABCSEN,即基于動態(tài)柯西蜂群算法的選擇性神經(jīng)網(wǎng)絡(luò)集成算法(MCABCSEN),然后利用測試函數(shù)驗證了改進算法性能的優(yōu)越性,最后利用南方某風場7#風機齒輪箱油溫數(shù)據(jù)以及人為擬合的齒輪箱故障油溫數(shù)據(jù)對新算法進行訓練和測試,結(jié)果顯示新算法狀態(tài)監(jiān)測靈敏,能提前預警故障并及時提醒工作人員,防止損失進一步加大。(4)基于研究的故障診斷和狀態(tài)監(jiān)測的方法,搭建了風機齒輪箱故障診斷與狀態(tài)監(jiān)測系統(tǒng),較為詳細的講解了系統(tǒng)硬件和軟件的設(shè)計,最后對設(shè)計方案進行了簡單的實驗驗證。
[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.
【學位授予單位】：上海電機學院
【學位級別】：碩士
【學位授予年份】：2017
【分類號】：TM315

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