基于宽频振动的燃气轮机叶片断裂故障识别特征与辨识方法

doi:10.7527/S1000-6893.2024.30098

Abstract

Abstract:

Aiming at the technical difficulty of timely and effective warning and identification of gas turbine rotor blade fracture faults， this paper conducts a three-pronged research. Starting from the blade fracture fault mechanism， we analyse the modulation effect of the unbalanced rotor on the wake pressure， establish the blade excitation force model and the forced vibration response model of the casing under the action of the excitation force， and solve the wide-frequency vibration response of the dynamic blade fracture excitation. On this basis， a blade fracture fault sensitive characteristic is constructed， and a fault alarm model based on the Optimized K-Nearest Neighbor （O-KNN） is proposed to realize the timely alarm analysis of the blade fracture fault. Furthermore， the blade fracture fault localization parameters are proposed， and the accurate localization of the fracture fault is realized by comparing the blade characteristic parameters at all stages. In summary， a broadband vibration characteristic driven gas turbine blade fracture fault identification method is constructed， and a test case on a certain type of gas turbine verifies the effectiveness of the fault sensitive characteristics and the identification method.

Key words: gas turbine, blade fracture, fault diagnosis, casing vibration, optimized K-nearest neighbor

CLC Number:

V263.6

Minghui HU, Shaopeng LIU, Hao WANG, Chenyang LI, Weimin WANG, Zhinong JIANG. Characterization and identification of gas turbine blade fracture faults based on broadband vibration[J]. Acta Aeronautica et Astronautica Sinica, 2024, 45(20): 230098.

Figures/Tables 23

Fig.1

Fig.2

Fig.3

Fig.4

Table 1

Parameter settings for simulation

参数类型	名称	数值
几何参数	转子叶片数 $b$	8
	静子叶片数 $s$	13
	机匣测点位置 $α s$ /（°）	60
	断裂叶片位置 $g$	2
	叶片断裂程度 $d$ /%	20
	初始不平衡量 $e 1$ /m	0.005
	断裂不平衡量 $e 2$ /m	0.005 5
材料参数	转子叶片长度 $l b$ /m	0.5
	静子叶片长度 $l s$ /m	0.5
	机匣密度 $ρ$ /（kg·m^-3）	$7.85 × 107$
	气流密度 $ρ 0$ /（kg·m^-3）	$1.29$
	弹性模量 $E$ /（N·m^-2）	2.1×10¹⁰
	机匣厚度 $δ$ /m	0.005
	机匣阻尼系数 $ζ n$	0.01
求解设置	转子转频 $f r$ /Hz	50
	采样频率 $F s$ /Hz	51 200
	采样时间 $t$ /s	2
	模态阶数 $n$	8

Table 1

Fig.5

Fig.6

Fig.7

Fig.8

Fig.9

Fig.10

Fig.11

Fig.12

Fig.13

Fig.14

Fig.15

Fig.16

Fig.17

Fig.18

Table 2

Parameter values for blade fracture fault localization at each stage

级数	$d i$	$V i$	$Y i$
第1级	6.529	0.030	6.530
第2级	1.034	0.059	1.035
第3级	2.22	0.174	2.220
第4级	0.461	0.166	-0.461
第5级	0.078	0.056	0.078
第6级	5.493	0.664	5.494
第7级	6.33	0.064	6.331
第8/9级	26.794	0.056	26.795

Table 2

Fig.19

Fig.20

Fig.21

References 26

1	MADHAV S， ROY M. Failure analysis of compressor blades of aero-engine［J］. Journal of Failure Analysis and Prevention， 2022， 22（3）： 968-982.
2	马艳红，梁智超，王桂华，等. 航空发动机叶片丢失问题研究综述［J］. 航空动力学报， 2016， 31（3）： 513-526.
	MA Y H， LIANG Z C， WANG G H， et al. Review on the blade loss of aero-engine［J］. Journal of Aerospace Power， 2016， 31（3）： 513-526 （in Chinese）.
3	洪亮，臧朝平，李全坤，等. 模拟转子叶片丢失后外传载荷影响特性研究［J］. 推进技术， 2023， 44（10）： 181-190.
	HONG L， ZANG C P， LI Q K， et al. Effects of external load on simulated rotor blade off event［J］. Journal of Propulsion Technology， 2023， 44（10）： 181-190 （in Chinese）.
4	洪杰，郝勇，张博，等. 叶片丢失激励下整机力学行为及其动力特性［J］. 航空发动机， 2014， 40（2）： 19-23.
	HONG J， HAO Y， ZHANG B， et al. Mechanical behaviors and dynamic characteristics of turbofan engine due to fan blade off［J］. Aeroengine， 2014， 40（2）： 19-23 （in Chinese）.
5	洪杰，栗天壤，王永锋，等. 叶片丢失激励下航空发动机柔性转子系统的动力学响应［J］. 航空动力学报， 2018， 33（2）： 257-264.
	HONG J， LI T R， WANG Y F， et al. Dynamic response of the aero-engine flexible rotor system under the blade-off［J］. Journal of Aerospace Power， 2018， 33（2）： 257-264 （in Chinese）.
6	SINHA S K. Rotordynamic analysis of asymmetric turbofan rotor due to fan blade-loss event with contact-impact rub loads［J］. Journal of Sound and Vibration， 2013， 332（9）： 2253-2283.
7	WANG N F， LIU C， JIANG D X. Prediction of transient vibration response of dual-rotor-blade-casing system with blade off［J］. Proceedings of the Institution of Mechanical Engineers， Part G： Journal of Aerospace Engineering， 2019， 233（14）： 5164-5176.
8	WANG N F， LIU C， JIANG D X， et al. Casing vibration response prediction of dual-rotor-blade-casing system with blade-casing rubbing［J］. Mechanical Systems and Signal Processing， 2019， 118： 61-77.
9	XIE J S， LIU J， CHEN J L， et al. Blade damage monitoring method base on frequency domain statistical index of shaft’s random vibration［J］. Mechanical Systems and Signal Processing， 2022， 165： 108351.
10	FORBES G L， RANDALL R B. Estimation of turbine blade natural frequencies from casing pressure and vibration measurements［J］. Mechanical Systems and Signal Processing， 2013， 36（2）： 549-561.
11	江志农，王钟，胡明辉，等. 燃气轮机动叶片断裂故障振动特征及其识别方法研究［J］. 机电工程， 2021， 38（8）： 935-943.
	JIANG Z N， WANG Z， HU M H， et al. Vibration feature and identification methods of gas turbine rotor blade fracture fault［J］. Journal of Mechanical & Electrical Engineering， 2021， 38（8）： 935-943 （in Chinese）.
12	江志农，党伟，胡明辉，等. 基于OCSVM的燃气轮机叶片断裂故障诊断方法［J］. 机械设计与制造， 2022（12）： 1-5， 10.
	JIANG Z N， DANG W， HU M H， et al. Gas turbine fault diagnosis method of blade fracture based on OCSVM［J］. Machinery Design & Manufacture， 2022（12）： 1-5， 10 （in Chinese）.
13	党伟，胡明辉，江志农，等. 燃气轮机压气机动叶片断裂故障振动特征及其诊断方法［J］. 振动与冲击， 2021， 40（10）： 7-19.
	DANG W， HU M H， JIANG Z N， et al. Vibration features and diagnosis methods for rotor blade fracture in a gas turbine’s compressor［J］. Journal of Vibration and Shock， 2021， 40（10）： 7-19 （in Chinese）.
14	FENG K， XIAO Y， LI Z Z， et al. Gas turbine blade fracturing fault diagnosis based on broadband casing vibration［J］. Measurement， 2023， 214： 112718.
15	LISKA J， VASICEK V， JAKL J. A novel method of impeller blade monitoring using shaft vibration signal processing［J］. Sensors， 2022， 22（13）： 4932.
16	GUBRAN A A， SINHA J K. Shaft instantaneous angular speed for blade vibration in rotating machine［J］. Mechanical Systems and Signal Processing， 2014， 44（1-2）： 47-59.
17	ABDELRHMAN A M， LEE G L， LEONG M L， et al. Early rotor blade fault detection in multi-stage rotor system based on wavelet analysis［C］∥46th Annual Review of Progress in Quantitative Nondestructive Evaluation. New York：ASME，2019.
18	ZHANG J Q， CHEN Y G， LI N， et al. A denoising method of micro-turbine acoustic pressure signal based on CEEMDAN and improved variable step-size NLMS algorithm［J］. Machines， 2022， 10（6）： 444.
19	CAO J H， YANG Z B， TIAN S H， et al. Time delay-based spectrum reconstruction for nonuniform and sub-Nyquist sampling in blade tip timing［J］. Mechanical Systems and Signal Processing， 2023， 200： 110552.
20	LIN J， HU Z， CHEN Z S， et al. Sparse reconstruction of blade tip-timing signals for multi-mode blade vibration monitoring［J］. Mechanical Systems and Signal Processing， 2016， 81： 250-258.
21	DONG J N， LI H K， CAO H W， et al. An improved blade tip timing dual-probe method of synchro-resonance frequency identification for blade damage detection［J］. Mechanical Systems and Signal Processing， 2023， 203： 110731.
22	MURRAY W L， KEY N L. Detection of rotor forced response vibrations using stationary pressure transducers in a multistage axial compressor［J］. International Journal of Rotating Machinery， 2015， 2015： 198534.
23	SOEDEL W. Vibrations of shells and plates［M］. New York： Marcel Dekker Inc， 1981.
24	盛兆顺，尹琦岭. 设备状态监测与故障诊断技术及应用［M］. 北京：化学工业出版社， 2003.
	SHENG Z S， YIN Q L. Equipment status monitoring and fault diagnosis technology and application［M］. Beijing： Chemical Industry Press， 2003 （in Chinese）.
25	COVER T， HART P. Nearest neighbor pattern classification［J］. IEEE Transactions on Information Theory， 1967， 13（1）： 21-27.
26	SARMADI H， KARAMODIN A. A novel anomaly detection method based on adaptive Mahalanobis-squared distance and one-class KNN rule for structural health monitoring under environmental effects［J］. Mechanical Systems and Signal Processing， 2020， 140： 106495.

[1]	Minghui HU, Jinji GAO, Zhinong JIANG, Weimin WANG, Limin ZOU, Tao ZHOU, Yunfeng FAN, Yue WANG, Jiaxin FENG, Chenyang LI. Research progress on vibration monitoring and fault diagnosis for aero-engine [J]. Acta Aeronautica et Astronautica Sinica, 2024, 45(4): 630194-630194.
[2]	Zhanjun HUANG, Xin DONG, Muyu LU, Ruitao ZHANG, Zhaoyang YAN, An ZHANG. Fault diagnosis of inverter of aviation HVDC sysytem based on DRSN and voltage amplitude analysis [J]. Acta Aeronautica et Astronautica Sinica, 2024, 45(3): 328685-328685.
[3]	Tianqing LI, Weimin WANG, Xulong ZHANG, Shuhui WANG, Zhenyu FU. Identification method of rotor blade axial displacement based on blade tip timing [J]. Acta Aeronautica et Astronautica Sinica, 2024, 45(2): 228682-228682.
[4]	Yonggang CHEN, Kangni LIU, Shuai WANG. Fault knowledge graph construction for aviation equipment based on BiGRU⁃Attention improvement [J]. Acta Aeronautica et Astronautica Sinica, 2024, 45(18): 229916-229916.
[5]	Hui LI, Yinchao CHEN, Shaoshan SUN, Zhaoxin LIANG, Gang MAO, Bin QIAO, Yongbo LI. Fault diagnosis method of rotor system based on federated graph network [J]. Acta Aeronautica et Astronautica Sinica, 2024, 45(17): 530611-530611.
[6]	Yuanhao HU, Yibo SONG, Jiahui LIU, Xiaoli ZHAO, Wenxiang DENG, Jian HU, Jianyong YAO. Fault diagnosis of electro-hydraulic proportional servo valves based on attention convolutional capsule networks [J]. Acta Aeronautica et Astronautica Sinica, 2024, 45(15): 630407-630407.
[7]	Jie LI, Wenxin HUANG, Yiming CAI, Siyuan WANG, Yufei GAO, Xuefeng JIANG. Fault diagnosis and fault tolerant control of position sensor based on DFPMM [J]. Acta Aeronautica et Astronautica Sinica, 2024, 45(10): 329307-329307.
[8]	Baohui JIA, Fan JIANG, Yuxin WANG, Du WANG. Fault diagnosis method based on civil aircraft maintenance text data [J]. ACTA AERONAUTICAET ASTRONAUTICA SINICA, 2023, 44(5): 326598-326598.
[9]	Jinrui WANG, Shanshan JI, Zongzhen ZHANG, Zhenyun CHU, Baokun HAN, Huaiqian BAO. Parallel sparse filtering for fault diagnosis under bearing acoustic signal [J]. ACTA AERONAUTICAET ASTRONAUTICA SINICA, 2023, 44(4): 426887-426887.
[10]	Zehao CHEN, Hui CHEN, Yushan GAO, Hang ZHANG. Review and prospect of model-based fault diagnosis technology for liquid rocket engines [J]. Acta Aeronautica et Astronautica Sinica, 2023, 44(23): 629016-629016.
[11]	Gongye YU, Weidong CAI, Minghui HU, Wencai LIU, Bo MA. Intelligent migration diagnosis of mechanical faults driven by hybrid fault mechanism and domain adaptation [J]. ACTA AERONAUTICAET ASTRONAUTICA SINICA, 2023, 44(2): 426800-426800.
[12]	Fangli WANG, Kai LIU, Wei PAN, Mingbo TONG. Application and development of green structure maintenance for civil aircraft [J]. ACTA AERONAUTICAET ASTRONAUTICA SINICA, 2023, 44(11): 25851-025851.
[13]	Chenghao GUO, Jinsong YU, Yue SONG, Qi YIN, Jiaxuan LI. Application of digital twin⁃based aircraft landing gear health management technology [J]. ACTA AERONAUTICAET ASTRONAUTICA SINICA, 2023, 44(11): 227629-227629.
[14]	Wanli ZHAO, Yingqing GUO, Kejie XU, Cansen WANG, Haojie YING, Xinxin TAO. Review of key technologies for fault diagnosis and accommodation for multi⁃electric distributed engine control system [J]. ACTA AERONAUTICAET ASTRONAUTICA SINICA, 2023, 44(10): 27519-027519.
[15]	ZHANG Shuo, LIU Zhiwen. Structured Bayesian sparse representation of aero-engine bearing failures [J]. ACTA AERONAUTICAET ASTRONAUTICA SINICA, 2022, 43(9): 625056-625056.

Characterization and identification of gas turbine blade fracture faults based on broadband vibration

RichHTML

PDF (PC)

Knowledge

Abstract

Cite this article

share this article

Figures/Tables 23

References 26

Related Articles 15

Recommended Articles

Metrics

Comments