Acoustic Emission Feature Extraction of Single Abrasive Wear Based on Multi-scale Standard Deviation Permutation Entropy

XIA Tian; WAN Linlin; ZHANG Xianyang; CHEN Zejun

doi:10.16490/j.cnki.issn.1001-3660.2026.03.015

PDF(6502 KB)

Surface Technology ›› 2026, Vol. 55 ›› Issue (3) : 183-195. DOI: 10.16490/j.cnki.issn.1001-3660.2026.03.015

Acoustic Emission Feature Extraction of Single Abrasive Wear Based on Multi-scale Standard Deviation Permutation Entropy

XIA Tian, WAN Linlin^*, ZHANG Xianyang, CHEN Zejun

Author information +

History +

Abstract

Grinding serves as an important method for high efficient and precise machining of hard and brittle materials such as engineering ceramics, and the wear state of the grinding wheel directly affects the surface quality of machined workpieces. The acoustic emission signal characteristics can effectively represent the wear state of the grinding wheels. With a single abrasive particle as the research object, a new acoustic emission characteristic parameter used to characterize the wear state of the grinding wheel is proposed in this study. First, a single abrasive particle wear experiment is carried out under dry cutting conditions according to the cut-in plane reverse cutting method. The grinding wheel speed is set as 2 000 r/min, the grinding depth as 20 μm, the feed speed of the worktable as 0.02 m/s, and the workpiece was sintered silicon carbide ceramic block under normal pressure. During the experiment, acoustic emission sensors are used to collect the signals generated by the abrasive particle scratching silicon carbide ceramic under different wear conditions, and a Dino AM4115T handheld electron microscope is used to record the evolution of abrasive particle morphology. The initial exposed height of abrasive particle is 0.774 mm, and the initial wear platform area is 0 mm². The wear volume of the abrasive particle can be calculated by the wear height and wear platform area of the abrasive particle. The wear volume is used to quantify the wear process of the abrasive particle, and its change shows a trend of rapid rise first, then tending to be flat, and finally rapid rise. The wear process can be divided into three stages according to the wear volume of the abrasive particle: initial wear dominated by micro fracture, stable wear dominated by wear and serious wear dominated by macro fracture. Subsequently, an improved Enhanced Whale Optimized Variable Mode Decomposition (EWOA_VMD) method is proposed for the pretreatment of acoustic emission signals. Compared with the traditional Whale Optimized Variable Mode Decomposition (WOA_VMD) method, the entropy of the signal envelope obtained according to the EWOA_VMD method is 6.868 9 lower, the convergence speed of each parameter is significantly improved, the signal-to-noise ratio of processed signals is higher and the denoising effect is better. Finally, the effectiveness of the proposed MSDPE feature is verified. Through calculating the MSDPE value within the range of scale factor 1-6, it is found that when the scale factor is 4, the MSDPE values of different wear states have obvious differences, and the change curve can accurately reflect the time of abrasive wear. The correlation between the commonly used time domain, frequency domain and MSDPE characteristics and the wear volume of the abrasive particle is further analyzed. When the scale factor is 4, the correlation coefficient between the MSDPE and the wear volume of the abrasive particle reaches 0.92, which is significantly higher than other characteristic parameters. The results show that the MSDPE feature is highly sensitive to the wear behavior of the abrasive particle, and can accurately identify the occurrence of wear events. It can be used as an effective characterization parameter for the acoustic emission monitoring of grinding wheel wear, which provides a new solution for the grinding wheel wear state monitoring process.

Key words

single abrasive particle / wear mechanism / acoustic emission / enhanced whale optimization algorithm (EWOA) / feature extraction / multi-scale standard deviation permutation entropy (MSDPE)

Cite this article

EndNote

Ris (Procite)

Bibtex

Download Citations

XIA Tian, WAN Linlin, ZHANG Xianyang, CHEN Zejun. Acoustic Emission Feature Extraction of Single Abrasive Wear Based on Multi-scale Standard Deviation Permutation Entropy[J]. Surface Technology. 2026, 55(3): 183-195

References

[1] KHAN A R, ZIA A, CAMPEÑA F J H, et al. Investigations of Entropy Double & Strong Double Graph of Silicon Carbide[J]. Silicon, 2024, 16(10): 4187-4197.
[2] DAI J B, SU H H, HU H, et al.The Influence of Grain Geometry and Wear Conditions on the Material Removal Mechanism in Silicon Carbide Grinding with Single Grain[J]. Ceramics International, 2017, 43(15): 11973-11980.
[3] ZHOU X Y, YU T Y, WANG G Z, et al.Tool Wear Classification Based on Convolutional Neural Network and Time Series Images during High Precision Turning of Copper[J]. Wear, 2023, 522: 204692.
[4] KIM H M, MANIVANNAN R, MOON D J, et al.Evaluation of Double Sided Lapping Using a Fixed Abrasive Pad for Sapphire Substrates[J]. Wear, 2013, 302(1/2): 1340-1344.
[5] ZHANG Z Y, GUO D M, WANG B, et al.A Novel Approach of High Speed Scratching on Silicon Wafers at Nanoscale Depths of Cut[J]. Scientific Reports, 2015, 5: 16395.
[6] DAI C W, DING W F, XU J H, et al.Influence of Grain Wear on Material Removal Behavior during Grinding Nickel-Based Superalloy with a Single Diamond Grain[J]. International Journal of Machine Tools and Manufacture, 2017, 113: 49-58.
[7] DING H H, YANG J Y, WANG W J, et al.Wear Mechanisms of Abrasive Wheel for Rail Facing Grinding[J]. Wear, 2022, 504: 204421.
[8] 尹国强, 王东, 关云匀, 等. 基于声发射监测的砂轮磨损实验[J]. 东北大学学报(自然科学版), 2022, 43(8): 1127-1133.
YIN G Q, WANG D, GUAN Y Y, et al.Grinding Wheel Wear Experiment Based on Acoustic Emission Monitoring[J]. Journal of Northeastern University (Natural Science), 2022, 43(8): 1127-1133.
[9] 郭维诚, 李蓓智, 杨建国, 等. 磨削过程信号监测与砂轮磨损预测模型构建[J]. 上海交通大学学报, 2019, 53(12): 1475-1481.
GUO W C, LI B Z, YANG J G, et al.Monitoring of Grinding Signals and Development of Wheel Wear Prediction Model[J]. Journal of Shanghai Jiao Tong University, 2019, 53(12): 1475-1481.
[10] SHIVITH K, RAMESHKUMAR K.AE Signature Analysis Using Continuous and Discrete Wavelet Transforms to Predict Grinding Wheel Conditions[J]. IOP Conference Series: Materials Science and Engineering, 2021, 1045(1): 012034.
[11] 陈敬强, 邓朝晖, 卓荣锦, 等. 磨削加工中声发射监测技术研究进展[J]. 宇航材料工艺, 2023, 53(3): 18-26.
CHEN J Q, DENG Z H, ZHUO R J, et al.Research Progress of Acoustic Emission Monitoring Technology in Grinding Process[J]. Aerospace Materials & Technology, 2023, 53(3): 18-26.
[12] DRAGOMIRETSKIY K, ZOSSO D.Variational Mode Decomposition[J]. IEEE Transactions on Signal Processing, 2014, 62(3): 531-544.
[13] 张帅, 丁俊华, 丁雪兴, 等. 基于IA-VMD的浮环密封声发射信号降噪与特征提取[J]. 振动与冲击, 2024, 43(4): 222-229.
ZHANG S, DING J H, DING X X, et al.Noise Reduction and Feature Extraction of Acoustic Emission Signals from Floating Ring Seals Based on IA-VMD[J]. Journal of Vibration and Shock, 2024, 43(4): 222-229.
[14] 张俊, 张建群, 钟敏, 等. 基于PSO-VMD-MCKD方法的风机轴承微弱故障诊断[J]. 振动测试与诊断, 2020, 40(2): 287-296.
ZHANG J, ZHANG J Q, ZHONG M, et al.PSO-VMD-MCKD Based Fault Diagnosis for Incipient Damage in Wind Turbine Rolling Bearing[J]. Journal of Vibration, Measurement & Diagnosis, 2020, 40(2): 287-296.
[15] 杨远鹏, 陈志刚, 余志红, 等. 基于WOA-VMD与PSO-SVM的滚动轴承故障诊断[J]. 制造技术与机床, 2025(2): 23-29.
YANG Y P, CHEN Z G, YU Z H, et al.Fault Diagnosis of Rolling Bearings Based on WOA-VMD and PSO-SVM[J]. Manufacturing Technology & Machine Tool, 2025(2): 23-29.
[16] 陈祥, 杨志强, 田镇, 等. GA-VMD与多尺度排列熵结合的GNSS坐标时序降噪方法[J]. 武汉大学学报(信息科学版), 2023, 48(9): 1425-1434.
CHEN X, YANG Z Q, TIAN Z, et al.Denoising Method for GNSS Time Series Based on GA-VMD and Multi-Scale Permutation Entropy[J]. Geomatics and Information Science of Wuhan University, 2023, 48(9): 1425-1434.
[17] WAN L L, CHEN Z J, ZHANG X Y, et al.A Multi-Sensor Monitoring Methodology for Grinding Wheel Wear Evaluation Based on INFO-SVM[J]. Mechanical Systems and Signal Processing, 2024, 208: 111003.
[18] WAN L L, ZHANG X Y, ZHOU Q M, et al.Acoustic Emission Identification of Wheel Wear States in Engineering Ceramic Grinding Based on Parameter-Adaptive VMD[J]. Ceramics International, 2023, 49(9): 13618-13630.
[19] CARAGUAY S J, BOARON A, WEINGAERTNER W L, et al.Wear Assessment of Microcrystalline and Electrofused Aluminum Oxide Grinding Wheels by Multi-Sensor Monitoring Technique[J]. Journal of Manufacturing Processes, 2022, 80: 141-151.
[20] GUO Y, CHEN B, ZENG H Y, et al.Research on Wear State Identification of Ordered Grinding Wheel for C/SiC Composites Based on DBO-ELM[J]. Wear, 2024, 556: 205529.
[21] 郭力, 李波, 郭君涛. 基于小波分析和GA-SVM的金刚石砂轮磨损的声发射监测研究[J]. 机电工程, 2019, 36(12): 1255-1260.
GUO L, LI B, GUO J T.Diamond Wheel Wear Monitoring with Acoustic Emission Based on Wavelet Analysis and GA-SVM[J]. Mechanical & Electrical Engineering Magazine, 2019, 36(12): 1255-1260.
[22] WANG S, TIAN Y B, HU X T, et al.Identification of Grinding Wheel Wear States Using AE Monitoring and HHT-RF Method[J]. Wear, 2025, 562: 205668.
[23] WANG X Z, SI S B, LI Y B.Multiscale Diversity Entropy: A Novel Dynamical Measure for Fault Diagnosis of Rotating Machinery[J]. IEEE Transactions on Industrial Informatics, 2021, 17(8): 5419-5429.
[24] 谭永敏, 施展, 迟玉伦, 等. 基于Shannon熵与声发射信号的CBN砂轮性能监测方法研究[J]. 机械强度, 2023, 45(2): 321-330.
TAN Y M, SHI Z, CHI Y L, et al.Research on Cbn Grinding Wheel Performance Monitoring Method Based on Shannon Entropy and Acoustic Emission Signal[J]. Journal of Mechanical Strength, 2023, 45(2): 321-330.
[25] 陈芸, 杨嘉睿, 金容鑫, 等. 基于混沌理论砂轮磨钝过程声发射信号试验研究[J]. 噪声与振动控制, 2022, 42(5): 148-153.
CHEN Y, YANG J R, JIN R X, et al.Experimental Study on Acoustic Emission Signals during Glunting Process of Grinding Wheels Based on Chaos Theory[J]. Noise and Vibration Control, 2022, 42(5): 148-153.
[26] ZHANG X L, CAO L Y, CHEN Y, et al.Microseismic Signal Denoising by Combining Variational Mode Decomposition with Permutation Entropy[J]. Applied Geophysics, 2022, 19(1): 65-80.
[27] LI K, HE S P, LI B, et al.A Novel Online Chatter Detection Method in Milling Process Based on Multiscale Entropy and Gradient Tree Boosting[J]. Mechanical Systems and Signal Processing, 2020, 135: 106385.
[28] WU S D, WU P H, WU C W, et al.Bearing Fault Diagnosis Based on Multiscale Permutation Entropy and Support Vector Machine[J]. Entropy, 2012, 14(8): 1343-1356.
[29] 别锋锋, 张瀚阳, 李倩倩, 等. 基于多元变分模态分解的行星齿轮箱故障诊断方法[J]. 机械设计与研究, 2025, 41(2): 364-370.
BIE F F, ZHANG H Y, LI Q Q, et al.Fault Diagnosis Method for Planetary Gearboxes Based on Multivariate Variational Mode Decomposition[J]. Machine Design & Research, 2025, 41(2): 364-370.
[30] 张炎亮, 李营. 基于多尺度排列熵和IWOA-SVM的滚动轴承故障诊断[J]. 电子测量技术, 2023, 46(19): 29-34.
ZHANG Y L, LI Y.Rolling Bearing Fault Diagnosis Based on Multi-Scale Permutation Entropy and IWOA-SVM[J]. Electronic Measurement Technology, 2023, 46(19): 29-34.
[31] MINHAS A S, KANKAR P K, KUMAR N, et al.Bearing Fault Detection and Recognition Methodology Based on Weighted Multiscale Entropy Approach[J]. Mechanical Systems and Signal Processing, 2021, 147: 107073.
[32] MIRJALILI S, LEWIS A.The Whale Optimization Algorithm[J]. Advances in Engineering Software, 2016, 95: 51-67.
[33] KUMAR Y, KAUR A.Variants of Bat Algorithm for Solving Partitional Clustering Problems[J]. Engineering with Computers, 2022, 38(3): 1973-1999.
[34] NADIMI-SHAHRAKI M H, ZAMANI H, MIRJALILI S. Enhanced Whale Optimization Algorithm for Medical Feature Selection: A COVID-19 Case Study[J]. Computers in Biology and Medicine, 2022, 148: 105858.
[35] BANDT C, POMPE B.Permutation Entropy: A Natural Complexity Measure for Time Series[J]. Physical Review Letters, 2002, 88(17): 174102.
[36] 肖扬, 王华庆, 李华, 等. 基于精细复合缩放多尺度加权排列熵的跨域故障诊断方法[J]. 机械工程学报, 2025, 61(16): 28-39.
XIAO Y, WANG H Q, LI H, et al.Cross-Domain Fault Diagnosis Method Based on Refined Composite Zoom Multi-Scale Weighted Permutation Entropy[J]. Journal of Mechanical Engineering, 2025, 61(16): 28-39.
[37] SHARMA S, TIWARI S K.A Novel Feature Extraction Method Based on Weighted Multi-Scale Fluctuation Based Dispersion Entropy and Its Application to the Condition Monitoring of Rotary Machines[J]. Mechanical Systems and Signal Processing, 2022, 171: 108909.
[38] XIAO Z X, MA H F, LU Y Z, et al.Real-Time Milling Tool Breakage Monitoring Based on Multiscale Standard Deviation Diversity Entropy[J]. International Journal of Mechanical Sciences, 2023, 240: 107929.
[39] 刘伟. 基于单颗磨粒切削的氮化硅陶瓷精密磨削仿真与实验研究[D]. 长沙: 湖南大学, 2014.
LIU W.Simulation and Experimental Study on Precision Grinding of Silicon Nitride Ceramics Based on Single Abrasive Cutting[D]. Changsha: Hunan University, 2014.
[40] 贺勇. 单颗金刚石磨粒划擦SiC的实验研究[D]. 泉州: 华侨大学, 2014.
HE Y.Study on SiC Scratching with Single Diamond Grain[D]. Quanzhou: Huaqiao University, 2014.
[41] WU H Y.Wear Characteristics of Single Diamond Grit Scratching on Sapphire with Different Contact Forms[J]. Ceramics International, 2021, 47(7): 8840-8848.
[42] 陈凯, 杨长勇, 高绍武, 等. 单颗CBN磨粒微切削硬质合金YG8磨损研究[J]. 机械制造与自动化, 2021, 50(1): 25-28.
CHEN K, YANG C Y, GAO S W, et al.Study on Wear of Single CBN Grain Micro-Cutting YG8 Cemented Carbide[J]. Machine Building & Automation, 2021, 50(1): 25-28.
[43] ZHU S M, XIA H, PENG B S, et al.Feature Extraction for Early Fault Detection in Rotating Machinery of Nuclear Power Plants Based on Adaptive VMD and Teager Energy Operator[J]. Annals of Nuclear Energy, 2021, 160: 108392.
[44] BEALE C, NIEZRECKI C, INALPOLAT M.An Adaptive Wavelet Packet Denoising Algorithm for Enhanced Active Acoustic Damage Detection from Wind Turbine Blades[J]. Mechanical Systems and Signal Processing, 2020, 142: 106754.