Experimental Study on Acoustic Emission Characteristics of High-cycle Fatigue Cracks in Blades

doi:10.12382/bgxb.2024.0537

Abstract

Abstract:

In view of the problem that there is no effective online monitoring means in the process of blade high-cycle fatigue crack damage,an acoustic emission technology is adopted to study the online monitoring test of blade high-cycle fatigue crack acoustic emission under the action of fixed amplitude load,and explore the evolution law of blade crack initiation and propagation signal characteristics.The acoustic emission parameters and frequency spectrum characteristics of high-cycle fatigue crack are studied,and a crack activity coefficient index (CAI) is proposed.The high-cycle fatigue cracks in blades are divided into three stages,i.e.,crack initiation,metastable propagation,and rapid propagation.At the end of crack initiation and the rapid propagation stage,the crack activity coefficient significantly increases,which can achieve early warning of cracks.The generalized S transform is used to analyze the frequency spectra of acoustic emission signals at different damage stages,The results show that the signal energy is concentrated at 90-160kHz during crack initiation,the signal energy is mainly concentrated at 80-180kHz during the crack propagation,and a distinct energy peak at 120kHz appears during rapid crack propagation,The signal energy is the highest during rapid crack propagation,the energy is higher during crack initiation,and the energy is the lowest during crack metastable propagation.

Key words: blade crack, high-cycle fatigue, acoustic emission, crack activity coefficient

CLC Number:

TG115.5+7

LIU Qiang, YU Yang, YANG Ping. Experimental Study on Acoustic Emission Characteristics of High-cycle Fatigue Cracks in Blades[J]. Acta Armamentarii, 2025, 46(6): 240537-.

Figures/Tables 15

Fig.1 Schematic diagram of crack initiation and propagation

Fig.2 Blade specimen

Table 1 Blade dimensions

参数	数值
总长度/mm	147
自由端长度/mm	120
叶片宽度/mm	30
叶片厚度/mm	1

Table 2 Blade material parameters

材料	弹性模量/GPa	密度/(kg·m^-3)	泊松比	屈服强度/MPa
45号钢	210×10⁹	7 850	0.3	355

Fig.3 Simulated results of blade natural frequency

Fig.4 Arrangement of blade vibration test

Fig.5 Schematic diagram of the definition of acoustic emission simplified waveform parameters

Table 3 Test conditions of the blades

试件序号	特征频率/Hz	总试验时间/×10⁴s	总循环次数/×10⁶
1	63.44	9.00	5.7
2	45.21	14.40	6.5
3	60.12	8.28	5.0

Fig.6 Blade experimental acoustic emission parameters

Fig.7 Cumulative count

Fig.8 CAI curve

Fig.9 Acoustic emission waveforms of different damage states

Fig.10 Time-frequency diagram of blade crack acoustic emission signal

Fig.11 Test results

Fig.12 Schematic diagram of fatigue crack region division

References 22

[1]	WANG X W, HOU J, GUO H, et al. A Miner’s rule based fatigue life prediction model for combined high and low cycle fatigue considering loading interaction effect[J]. Fatigue & Fracture of Engineering Materials & Structures, 2023, 64:1-16.
[2]	孙聪. 民用航空发动机叶片损伤原位检测与评价技术[D]. 哈尔滨: 哈尔滨工业大学, 2022.
	SUN C. In-situ detection and evaluation technology of civil aero-engine blade damage[D]. Harbin: Harbin Institute of Technology, 2022. (in Chinese)
[3]	KONG Y, BENNETT C J, HYDE C J. A review of non-destructive testing techniques for the in-situ investigation of fretting fatigue cracks[J]. Materials & Design, 2020, 10:90-93.
[4]	CHEW H B. Cohesive zone laws for fatigue crack growth:Numerical field projection of the micromechanical damage process in an elasto-plastic medium[J]. International Journal of Solids and Structures, 2014, 51(6):1410-1420.
[5]	李练兵, 肖亚泽, 张萍, 等. 基于CWT-RES34的风电机组叶片裂纹状态评估[J]. 噪声与振动控制, 2024, 44(2):143-148,293.
	LI L B, XIAO Y Z, ZHANG P, et al. State estimation of cracks of wind turbine blades based on CWT-RES34[J]. Noise and Vibration Control, 2024, 44(2):143-148,293. (in Chinese)
[6]	ZHAO L Z, YOU R Z, REN L. Inverse finite element method and support vector regression for automated crack detection with OFDR-Distributed fiber optic sensors[J]. Measurement, 2024, 234:114916(1-17).
[7]	宋水舟, 任会兰, 宁建国. 混合型加载下钢纤维混凝土损伤过程的声发射参数分析[J]. 兵工学报, 2022, 43(8):1881-1891.
	SONG S Z, REN H L, NING J G. Acoustic emission parameters in the damage process of steel fiber reinforced concrete under mixed loading[J]. Acta Armamentarii, 2022, 43(8):1881-1891. (in Chinese)
[8]	黄振峰, 刘永坚, 毛汉颖, 等. 基于K熵和关联维数的金属疲劳损伤过程的声发射信号特征分析[J]. 振动与冲击, 2017, 36(15):210-214.
	HUANG Z F, LIU Y J, MAO H Y, et al. Feature analysis for acoustic emission signals during metal fatigue damage based on Kolmogorov entropy and correlation dimension[J]. Journal of Vibration and Shock, 2017, 36(15):210-214. (in Chinese)
[9]	HA M Y, KIM J H, KIM S W. Crack detection in upsetting of aluminum alloy using acoustic emission monitoring technology[J]. The International Journal of Advanced Manufacturing Technology, 2023, 124:2823-2834.
[10]	张治衡. 基于声发射技术的航空发动机叶片损伤监测预示方法研究[D]. 北京: 北京化工大学, 2019.
	ZHANG Z H. Study on the monitoring of aviation engine blade damage based on acoustic emission technology[D]. Beijing: Beijing University of Chemical Technology, 2019. (in Chinese)
[11]	廖力达, 向旭宏, 舒王咏, 等. 基于声发射Ib值分析的渗铝321钢损伤特性研究[J]. 仪器仪表学报, 2024, 45(1):211-220.
	LIAO L D, XIANG X H, SHU W Y, et al. Study on damage characteristics of aluminized 321 steel based on acoustic emission Ib-value analysis[J]. Chinese Journal of Scientific Instrument, 2024, 45(1):211-220.(in Chinese)
[12]	门进杰, 李东杰, 兰涛, 等. 2205双相不锈钢疲劳损伤声发射研究[J]. 结构工程师, 2023, 39(2):142-153.
	MEN J J, LI D J, LAN T, et al. Acoustic emission study on fatigue damage of 2205 duplex stainless steel[J]. Structural Engineers, 2023, 39(2):142-153.(in Chinese)
[13]	SHI S R, WU G Y, CHEN H, et al. Acoustic emission monitoring of fatigue crack growth in hadfield steel[J]. Sensors (Basel,Switzerland), 2023, 23(14):1-18.
[14]	ZHANG Z H, YANG G A, HU K. Prediction of fatigue crack growth in gas turbine engine blades using acoustic emission[J]. Sensors, 2018, 18(5):1311-1321.
[15]	YAN L, FAN J K. In-situ SEM study of fatigue crack initiation and propagation behavior in 2524 aluminum alloy[J]. Materials & Design, 2016, 110:92-601.
[16]	MADHAV B, ALI A, ALEXANDER B, et al. Acoustic emission monitoring for necking in sheet metal forming[J]. Journal of Materials Processing Technology, 2022, 310:117758(1-15).
[17]	JAEWOONG P, SUNG J K, YOUNG D L, et al. Real-time monitoring of stress corrosion cracking in 304 L stainless steel pipe using acoustic emission[J]. Journal of Nuclear Materials, 2022, 571:1881-1891.
[18]	CHAI M Y, ZHANG Z X, DUAN Q, et al. Assessment of fatigue crack growth in 316LN stainless steel based on acoustic emission entropy[J]. International Journal of Fatigue, 2018, 109:1-15.
[19]	CHAI M Y, ZHANG Z X, DUAN Q. A new qualitative acoustic emission parameter based on Shannon’s entropy for damage monitoring[J]. Mechanical Systems and Signal Processing, 2018, 100:6-17.
[20]	KESHTGAR A, SAUERBRUNN C M, MODARRES M. Structural reliability prediction using acoustic emission-based modeling of fatigue crack growth[J]. Applied Sciences, 2018, 8(8):12-25.
[21]	ZHAO K, YANG D X, GONG C. Evaluation of internal microcrack evolution in red sandstone based on time-frequency domain characteristics of acoustic emission signals[J]. Construction and Building Materials, 2020, 260:1204315(1-17).
[22]	HE K F, XIAO S W, LI X J. Time-frequency characteristics of acoustic emission signal for monitoring of welding structural state using Stockwell transform[J]. The Journal of the Acoustical Society of America, 2019, 145(1):4-19.