DTW based quantitative fault diagnosis of gas path component in turbofan

[1] Duyar A,Eldem V,Merrill W, et al. Fault detection and diagnosis in propulsion systems:a fault parameter estimation approach[J]. AIAA Journal of Guidance, Control and Dynamics, 1994,17(1):104~108 [2] 孙 斌,张 津. BPN在涡扇发动机气路故障诊断中的应用[J]. 航空动力学报,1998,13(3):323~326 Sun Bin,Zhang Jin. Application of BPN to quantitative diagnosis of gas path component faults in turbofan[J]. Journal of Aerospace Power, 1998,13(3):323~326(in Chinese) [3] 叶志锋,孙健国. 应用神经网络诊断发动机气路故障的前景[J]. 推进技术,2002,23(1):1~4 Ye Zhifeng,Sun Jianguo. Prospect for neural networks used aeroengine fault diagnosis technology[J]. Journal of Propulsion Technology, 2002,23(1):1~4(in Chinese) [4] 周东华,叶银忠. 现代故障诊断与容错控制[M].北京:清华大学出版社,2000 Zhou Donghua,Ye Yinzhong. Modern fault diagnosis and fault-tolerant control .Beijing:Tsinghua University Press,2000(in Chinese) [5] Kassidas A,Taylor P A,MacGregor J F. Off-line diagnosis of deterministic faults in continuous dynamic multivariable processes using speech recognition methods[J]. Journal of Process Control, 1998,8(5/6):381~393 [6] Keogh E J,Pazzani M J. Scaling up dynamic time warping for data mining applications . In:Proceedings of the 6th International Conference on Knowledge Discovery and Data Mining . Boston, 2000.285~289