【摘要】：風機在工業(yè)生產中發(fā)揮著重要的作用，當風機發(fā)生故障時，不僅對整個生產線產生直接影響，而且會造成重大的經濟損失甚至是機毀人亡的事故。為保證設備的安全運行,降低機組維修費用和提高設備利用率，設計出一種自動獲取知識且能進行高速推理的故障診斷方法，已經成為風機故障診斷研究的一個主要方向。 本論文針對某煉鋼廠風機的故障診斷與狀態(tài)監(jiān)測進行研究，采用PDM2000數據采集分析儀對故障風機進行振動信號采集，獲得特征頻率，根據設備振動診斷技術的頻譜分析方法，分析討論風機故障的故障征兆,得到風機故障的診斷結果。同時又采用BP神經網絡分析方法，對風機的故障做進一步的診斷分析。 本文根據BP神經網絡的結構形式及算法，選用三種方法對BP神經網絡的算法進行改進；并通過實測數據運算及三種改進算法的相互比較，從而選出運算速度比較快、判斷比較準確的Levenberg-Marquardt算法對所建立的BP神經網絡進行訓練分析。將采集的現場風機的特征數據，通過Matlab軟件進行訓練；并通過已訓練完成的BP神經網絡對其進行測試，從而判斷得出風機目前也存在轉子不平衡、轉子碰摩及輕微轉子不對中等故障，，其診斷結果與現場實測分析結果相吻合。 本文通過實測的風機振動數據分析結果與理論計算結果進行比較分析,證明本文提出的采用BP神經網絡改進算法對風機故障進行診斷具有一定的實用性和可行性。
[Abstract]:Fan plays an important role in industrial production. When the fan breaks down, it will not only have a direct impact on the whole production line, but also cause great economic losses and even fatal accidents. In order to ensure the safe operation of the equipment, reduce the maintenance cost of the unit and improve the utilization rate of the equipment, a fault diagnosis method which can automatically acquire knowledge and carry out high-speed reasoning has become a main research direction of fan fault diagnosis. In this paper, the fault diagnosis and condition monitoring of fan in a steelmaking plant is studied. The vibration signal of the fan is collected by PDM2000 data acquisition analyzer, and the characteristic frequency is obtained. According to the frequency spectrum analysis method of the equipment vibration diagnosis technology, the frequency spectrum of the fault fan is obtained. The fault symptom of fan fault is analyzed and the diagnosis result of fan fault is obtained. At the same time, BP neural network analysis method is used to diagnose fan fault further. According to the structure and algorithm of BP neural network, three methods are selected to improve the algorithm of BP neural network. A more accurate Levenberg-Marquardt algorithm is used to train and analyze the BP neural network. The characteristic data of the field fan are trained by Matlab software, and tested by BP neural network which has been trained, and it is judged that the fan also has rotor unbalance at present. The results of diagnosis are in good agreement with the measured results. In this paper, the analysis results of the measured fan vibration data and the theoretical calculation results are compared and analyzed. It is proved that the improved BP neural network algorithm proposed in this paper is practical and feasible for fan fault diagnosis.
【學位授予單位】：遼寧科技大學
【學位級別】：碩士
【學位授予年份】：2014
【分類號】：TH165.3

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本文編號：2145908