改进的符号时间序列分析方法及其在电机故障诊断中的应用

Improved symbolic time series analysis method and it′s application in motor fault diagnosis

提出了一种改进的基于符号时间序列分析的电机异常探测方法,该方法自适应地将符号序列中出现符号最多的符号区间重新划分为2个新的符号区间,使得数据密集区间可以分配到相对更多的符号,而数据稀疏区间则分配到较少的符号,提高了符号对于信号变化的灵敏度。电机转子断条故障的诊断实验结果表明:该方法较平均划分区间的方法对于电机异常诊断有着更高的灵敏度以及更好的鲁棒性和可靠性。

An improved method of motor fault detection based on symbolic time series analysis is proposed. The method adaptively partition off the region, which region has the most symbols in the symbolic series, into two new regions. The method makes that the regions with more information are assigned more symbols relatively but those with sparse information are assigned fewer symbols, which enhances the sensitive degree of symbols to the signals. The laboratory experiments of fault diagnosis of broken rotor in inductive motor show that, comparing with the uniform partition, the new method is more sensitive and also has stronger robustness and a better reliability.