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標題: Application of artificial neural network to determine the status of spindle bearing
作者: 陳志騏
Zhi-Chi Chen
關鍵字: artificial neural network;bearing;類神經網路;軸承
引用: 1.[TIMKEN, 2003] TIMKEN, Tapered Roller Bearing Damage Analysis, 2003 2.[Subrahmanyam, 1997] M. Subrahmanyam and C. Sujatha, Using neural networks for the diagnosis of localized defects in ball bearings, Tribology International Vol. 30, No. 10, pp. 739–752, 1997 3.[Blödt et al, 2008] Martin Blödt, Pierre Granjon, Bertrand Raison and Gilles Rostaing , Models for Bearing Damage Detection in Induction, Motors Using Stator Current Monitoring IEEE TRANSACTIONS ON INDUSTRIAL ELECTRONICS, VOL. 55, NO. 4, APRIL 2008 4.[Gao, 2008] Robert X. Gao,Neural Networks for Machine Condition Monitoring and Fault Diagnosis,Department of Mechanical and Industrial Engineering, University of Massachusetts, 2008 5.[廖天志&許原昇,2004] 廖天志&許原昇,工具機主軸之鬆拉刀機構設計探討,精密機械研究發展中心,2004年5月25日,機械資訊月刊 568 期 6.[朱效賢,2005] 朱效賢,包絡譜分析於軸承故障診斷之探討暨工程應用,中央大學論文,2005年9月30日 7.[黃興杰,2010] 黃興杰,工具機主軸振動與檢測,2010年04月22日 8.[彭善謙,2004] 彭善謙,綜合振動信號於馬達故障診斷,中原大學論文,2004 9.[黃興杰,2010] 黃興杰,工具機主軸振動與檢測,財團法人精密機械研究發展中心,2010年04月22日,迴轉機械之振噪檢測研討會 10.[趙安民,2004] 趙安民, 馬達故障診斷之模糊類神經網路,中原大學論文,2004年7月
The machining performance of the spindle of a milling machine is highly related to its operation condition. Therefore it is important to monitor the operation status of the spindle. While the most important factor affecting spindle operation is the condition of its bearings, this research proposes a diagnostic method to improve accuracy for identifying bearing fault status of the milling spindles via measuring rotation signals of operations and establishing a decision model. The developed method is testified against several spindles from market and shows it can be used to identify the condition of spindle bearings.
In the study, the artificial neural network (ANN) is employed to build the decision model. The failure mode and effect and the feature corresponding to the failure of spindle bearings are analyzed at first. The items and signals to be measured are then designed accordingly. These signals are then collected from a lot of spindles, with some of them being damaged. Conditions and operation signals of bearings, dissembled from these spindles, are also measured. These measured signals are used to train the ANN for building the relationship among the bearing conditions and the measured signals. Two approaches, two-step and one-step approaches, are further conducted to compare the accuracy of the models. The result shows that the accuracies of the two approaches were 85% and 81% respectively but the one-step approach is more practical as it can be employed to industrial application directly. The major contribution of this research is the approach to build up an ANN model that can be used to identify the status of spindle bearings with fairly good accuracy. The model can be further employed for predictive monitoring of the spindle of a machine tool such that failure of the spindle can be forecasted to prevent against the loss of production due to spindle failure.

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