中文摘要：

为了有效地检测发动机试车实验中性能参数发生的异常,提出一种基于时间序列数据挖掘的发动机故障检测方法。通过基于形态特征的时间序列特征表示方法,将发动机参数时间序列转化为符号序列,再根据符号语义对发动机参数序列实现稳态特征和过渡态特征识别。同时,根据稳态序列的数据特征,利用基于统计特征的时间序列相似性度量结合最不相似模式发现方法实现发动机的故障检测。数值实验结果表明,与传统方法相比,本文方法能够有效地对发动机性能参数进行故障检测,并且具有较强的鲁棒性。

英文摘要：

To validly detect the anomalies of parameters in the engine test, a fault detection algorithm of engine based on time series data mining is proposed. The parameter time series are transformed into symbolic strings by a representation method based on shape features. The stable states and transition states are extracted from the parameter time series according to symbolic semantics. Meanwhile, the detection algorithm of abnormal pattern from the stable states is realized by similarity measurement between time series based on statistic features, combined with the most unusual pattern discovery method. The results of numerical experiments show that the new method validly detects the fault of engine and has the better robustness than the traditional method.