DTW based quantitative fault diagnosis of gas path component in turbofan
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摘要: 提出了一种利用动态过程信息对气路故障进行定量诊断的新方法.采用动态时间规整(DTW, Dynamic Time Warping)技术,对动态测试样本同已知的故障模式进行相似性度量,充分利用发动机的过渡态特征信息,实现了发动机气路部件故障的定量诊断.结果表明:这种方法的漏报率和误报率低,具有很高的诊断准确性,对测量噪声和其它不确定性具有很好的鲁棒性.Abstract: A quantitative fault diagnosis method was presented based on pattern recognition principles for gas path component in turbofan. The method consists of a preprocessing step for multivariate dynamic data, where the magnitude dependent information is standardized, and a similarity assessment step via DTW (dynamic time warping). DTW is a flexible pattern matching method used in the area of speech recognition. The method was designed to classify faults independently of inconsistencies in the operating processes of turbofan ground test-runs. Quantitative diagnosis was fulfilled by the subdivision of fault pattern database according to the fault degree of gas path component. Case studies show that the method has a low false alarm rate and a high capability in isolating faults, and moreover, it is also robust with measuring noise and other uncertainties.
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Key words:
- aircraft engines /
- fault diagnosis /
- dynamic process /
- dynamic time warping
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