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中文摘要：
一台飞机引擎的颤动信号是为 nonstationarity 和颤动的低重覆性表明的差错诊断和条件 monitoring.Considering 的很重要的信息来源，为 fiature 抽取和差错 recognition.In 发现一个相应方法是必要的这份报纸，基于独立部件分析(集成通信适配器)和分离隐藏的 Markov 模型( DHMM )，一条新差错诊断途径说出 ICA-DHMM 是 proposed.In 这个方法，集成通信适配器把来源信号与分开
英文摘要：
The vibration signals of an aeroengine are a very important information source for fault diagnosis and condition monitoring. Considering the nonstationarity and low repeatability of the vibration signals, it is necessary to find a corresponding method for feature extraction and fault recognition. In this paper, based on Independent Component Analysis （ICA） and the Discrete Hidden Markov Model （DHMM）, a new fault diagnosis approach named ICA-DHMM is proposed. In this method, ICA separates the source signals from the mixed vibration signals and then extracts features from them, DHMM works as a classifier to recognize the conditions of the aeroengine. Compared with the DHMM, which use the amplitude spectrum of mixed signals as feature parameters, experimental results show this method has higher diagnosis accuracy.
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