基于LVQ神经网络的行星轮系故障退化状态识别
首发时间:2013-07-02
摘要:为确保设备安全可靠的运行,减少事故的发生,降低由设备故障或失效造成的经济损失,故障预测与健康管理技术已经受到了越来越多的重视。其中退化状态识别作为故障预测的基础和起点,是PHM的关键环节之一,直接关系到设备故障预测的可靠性,并影响相应维护策略的制定。本文针对行星轮系的退化状态识别问题,首先对多种时频域预测特征进行评估处理,然后引入学习矢量量化神经网络,利用选择出的预测特征直接应用于行星轮系的退化状态识别,最后以行星轮系缺齿故障数据对该方法进行验证。实验结果表明本文算法能够较好的识别出多级退化状态,可为进一步的故障预测提供有益参考。
关键词: 机械电子工程 退化状态识别 预测特征 神经网络 学习矢量量化
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Fault Degradation State Recognition for Planetary Gear Set Based on LVQ Neural Network
Abstract:In order to ensure the safety and reliable operation of equipment, reduce accidents and economic loss caused by the mechanical fault or failure, prediction and health management (PHM) technology has attracted more and more attention. As the basis and starting point of fault prediction, degradation state recognition is one of the key steps of PHM, which directly affect the reliability of the equipment failure prediction and the selection of corresponding maintenance strategy. As to the degradation state recognition problem of planetary gear set, firstly, select the proper prognosis feature by evaluating various time-frequency features. Secondly, utilize the learning vector quantization neural network to recognize degradation state of planetary gear set. Finally, validate the effectively of presented method with pre-planted chipped fault experiment of planetary gear set. The results show that the proposed algorithm recognizes the multi-level degradation state effectively, and provide a useful reference for subsequent fault prediction.
Keywords: mechatronics engineering degradation state recognition prognosis feature neural network learning vector quantization
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