【摘要】 提出一种基于时间序列数据挖掘的故障预报新方法。把故障前兆因子作为一种暂态,根据旋转机械轴承振动的实验数据建立时间序列．利用时延嵌入的方法重构状态空间,在状态空间中使用遗传算法搜寻最优暂态束．组成暂态集。用暂态集对旋转机械轴承振动的测试数据进行分析．判断是否为故障前兆因子．从而实现故障预报。更多还原

【Abstract】 A new fault prediction method based on time series data mining is proposed.Fault symptoms are regarded as a sort of temporal patterns hidden in the time series formed by rotating machinery bear- ing vibration data.The time series is embedded into a reconstructed phase space with time-delay.In this phase space,genetic algorithms are used to search optimal temporal pattern clusters.The optimal col- lection is comprised of temporal pattern clusters and then is used to test the other bearing vibration data of the rotating machinery.Once the symptom is detected,the fault is forecasted.更多还原