基于改进马田系统的复杂系统健康状态评估

doi:10.3969/j.issn.1001-506X.2020.04.30

Abstract

Abstract:

Health status assessment is a key part of prognostics and health management for complex systems. Aiming at the comprehensive assessment problems of the system health state, a complex system health evaluation approach is proposed, which is based on the improved Mahalanobis-Taguchi system (IMTS). Firstly, the time series of multiple characteristic parameters is monitored, which is put into IMTS to filter the features and calculate the weighted Mahalanobis distance (WMD). Then, the health index (HI) model is constructed based on WMD, and the Box-Cox transformation and three-sigma rules are used to determine the HI threshold. Finally, with the PHM08 data provided by national aeronautics and space administration (NASA), the health status of the aeroengine is evaluated. The results indicate that the proposed method can perform timely and effective comprehensive health assessment of complex systems.

Key words: improved Mahalanobis-Taguchi system (IMTS), health index (HI), Box-Cox transformation, health status assessment

CLC Number:

TP18

Zhaiming PENG, Longsheng CHENG, Jun ZHAN, Qifeng YAO. Health status assessment for complex system based on improved Mahalanobis-Taguchi system[J]. Systems Engineering and Electronics, 2020, 42(4): 960-968.

Figures/Tables 12

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References 16

1	CHEN L J, CHANG S N. An approach of fault diagnosis for electronic system of aircraft based on trapezoid fuzzy fault tree[C]//Proc.of the 2nd International Conference on Artificial Intelligence, Management Science and Electronic Commerce, 2011: 3716-3719.
2	EDZEL L , DUSTIN B , HOSSEIN D , et al. Wind turbine performance assessment using multi-regime modeling approach[J]. Renewable Energy, 2012, 45 (3): 86- 95.
3	MIAO Q , WANG D , PECHT M . A probabilistic description scheme for rotating machinery health evaluation[J]. Journal of Mechanical Science and Technology, 2010, 24 (12): 2421- 2430. doi: 10.1007/s12206-010-0908-0
4	MU T N, YU H M, ZHANG X Y. Research on equipment health assessment based on grey system theory[C]//Proc.of the Prognostics and System Health Management Conference, 2012: 1-4.
5	王玉静, 姜义成, 康守强, 等. 基于优化集合EMD的滚动轴承故障位置及性能退化程度诊断方法[J]. 仪器仪表学报, 2013, 34 (8): 1834- 1840. doi: 10.3969/j.issn.0254-3087.2013.08.021
	WANG Y J , JIANG Y C , KANG S Q , et al. Diagnosis method of fault location and performance degradation degree of rolling bearing based on optimal ensemble EMD[J]. Chinese Journal of Scientific Instrument, 2013, 34 (8): 1834- 1840. doi: 10.3969/j.issn.0254-3087.2013.08.021
6	洪骥宇, 王华伟, 倪晓梅. 基于降噪自编码器的航空发动机性能退化评估[J]. 航空动力学报, 2018, 33 (8): 2041- 2048.
	HONG J Y , WANG H W , NI X M . Assessment of performance degradation for aero-engine based on denoising auto-encoder[J]. Journal of Aerospace Power, 2018, 33 (8): 2041- 2048.
7	TAGUCHI G , CHOWDHURY S , WU Y . The Mahalanobis-Taguchi system[M]. New York: McGraw-Hill, 2001.
8	TAGUCHI G , JUGULUM R . The Mahalanobis-Taguchi strategy: a pattern technology system[M]. New York: John Wiley & Sons, 2002.
9	SOYLEMEZOGLU A , JAGANNATHAN S , SAYGIN C . Mahalanobis-Taguchi system (MTS) as a prognostics tool for rolling element bearing failures[J]. Journal of Manufacturing Science and Engineering, 2010, 132 (5): 051014. doi: 10.1115/1.4002545
10	SOYLEMEZOGLU A , JAGANNATHAN S . Mahalanobis-Taguchi system as a multi-sensor based decision making prognostics tool for centrifugal pump failures[J]. IEEE Trans.on Reliability, 2011, 60 (4): 864- 878. doi: 10.1109/TR.2011.2170255
11	剡昌锋, 朱涛, 吴黎晓, 等. 基于马田系统的滚动轴承初始故障检测和状态监测[J]. 振动与冲击, 2017, 36 (12): 155- 162.
	YAN C F , ZHU T , WU L X , et al. Incipient fault detection and condition monitoring of rolling bearings by using the Mahalanobis-Taguchi system[J]. Journal of Vibration and Shock, 2017, 36 (12): 155- 162.
12	陈俊洵, 程龙生, 余慧, 等. 基于EMD-SVD与马田系统的复杂系统健康状态评估[J]. 系统工程与电子技术, 2017, 39 (7): 1542- 1548.
	CHEN J X , CHENG L S , YU H , et al. Health status assessment for complex systems based on EMD-SVD and Mahalanobis-Taguchi system[J]. Systems Engineering and Electronics, 2017, 39 (7): 1542- 1548.
13	ROUSSEEUW P J , VAN D K . A fast algorithm for the minimum covariance determinant estimator[J]. Technometrics, 1999, 41 (3): 212- 223. doi: 10.1080/00401706.1999.10485670
14	常志朋, 程龙生. 基于马田系统和模糊积分的多属性决策方法[J]. 管理工程学报, 2015, 29 (3): 107- 115.
	CHANG Z P , CHENG L S . Multi-attribute decision making method based on Mahalanobis-Taguchi System and fuzzy integral[J]. Journal of Industrial Engineering and Engineering Management, 2015, 39 (3): 107- 115.
15	SU C T , HSIAO Y H . Multiclass MTS for simultaneous feature selection and classification[J]. IEEE Trans.on Knowledge and Data Engineering, 2009, 21 (2): 192- 205. doi: 10.1109/TKDE.2008.128
16	QIU H , LEE J , LIN J , et al. Robust performance degradation assessment methods for enhanced rolling element bearing prognostics[J]. Advanced Engineering Informatics, 2003, 17 (3): 127- 140.

序号	符号	描述	单位
1	T2	风扇入口温度	°R
2	T24	低压压缩机出口温度	°R
3	T30	高压压缩机入口温度	°R
4	T50	低压涡轮出口温度	°R
5	P2	风扇入口的压力	Pa
6	P15	外涵道总压力	Pa
7	P30	高压压缩机出口压力	Pa
8	Nf	风扇物理转速	rpm
9	Nc	核心机物理转速	rpm
10	epr	发动机压比(P50/P2)	-
11	Ps30	高压压缩机出口静压	Pa
12	Phi	燃油流量与Ps30比值	pps/Pa
13	NRf	风扇换算转速	rpm
14	NRc	核心机换算转速	rpm
15	BPR	涵道比	-
16	farB	燃烧室燃气比	-
17	htBleed	引气焓值	-
18	Nf_dmd	设定风扇转速	rpm
19	PCNfr_dmd	设定核心机换算转速	rpm
20	W31	高压涡轮冷却引气流量	lbm/s
21	W32	低压涡轮冷却引气流量	lbm/s

循环次数	T2	T24	…	W31	W32
1	518.67	641.82	…	39.06	23.419
2	518.67	642.15	…	39	23.423 6
⋮	⋮	⋮	⋱	⋮	⋮
192	518.67	643.54	…	38.48	22.964 9

Health status assessment for complex system based on improved Mahalanobis-Taguchi system

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