电子测量与仪器学报

王红军,葛恒林,庞建军,岳宇宾,谢龙.基于梅尔倒谱系数和联合分布对齐的刀具磨损识别方法[J].电子测量与仪器学报,2026,40(6):232-244

基于梅尔倒谱系数和联合分布对齐的刀具磨损识别方法

Tool wear identification method based on Mel-frequency cepstralcoefficients and joint distribution alignment

DOI：

英文关键词:Mel-frequency cepstral coefficients joint distribution adaptation transfer learning tool wear identification method

基金项目:北京市自然科学基金(IS24076)项目资助

作者	单位
王红军	1.北京信息科技大学机电工程学院北京100192；2.北京信息科技大学高端装备智能感知与控制北京市国际科技合作基地北京100192；3.北京信息科技大学现代测控技术教育部重点实验室北京100192
葛恒林	1.北京信息科技大学机电工程学院北京100192；2.北京信息科技大学高端装备智能感知与控制北京市国际科技合作基地北京100192；3.北京信息科技大学现代测控技术教育部重点实验室北京100192
庞建军	超同步股份有限公司北京101500
岳宇宾	1.北京信息科技大学机电工程学院北京100192；2.北京信息科技大学高端装备智能感知与控制北京市国际科技合作基地北京100192
谢龙	1.北京信息科技大学机电工程学院北京100192；2.北京信息科技大学高端装备智能感知与控制北京市国际科技合作基地北京100192

Author	Institution
Wang Hongjun	1.Colleage of Mechanical and Electrical Engineering, Beijing Information Science & Technology University, Beijing 100192, China; 2.High-end Equipment Intelligent Perception and Control Beijing International Science & Technology Cooperation Base，Beijing Information Science and Technology University,Beijing 100192, China;3.Key Laboratory of Modern Measurement & Control Technology, Ministry of Education, Beijing Information Science and Technology University, Beijing 100192, China
Ge Henglin	1.Colleage of Mechanical and Electrical Engineering, Beijing Information Science & Technology University, Beijing 100192, China; 2.High-end Equipment Intelligent Perception and Control Beijing International Science & Technology Cooperation Base，Beijing Information Science and Technology University,Beijing 100192, China;3.Key Laboratory of Modern Measurement & Control Technology, Ministry of Education, Beijing Information Science and Technology University, Beijing 100192, China
Pang Jianjun	Beijing Chao Tong Bu Co., Ltd., Beijing 101500, China
Yue Yubin	1.Colleage of Mechanical and Electrical Engineering, Beijing Information Science & Technology University, Beijing 100192, China; 2.High-end Equipment Intelligent Perception and Control Beijing International Science & Technology Cooperation Base，Beijing Information Science and Technology University,Beijing 100192, China
Xie Long	1.Colleage of Mechanical and Electrical Engineering, Beijing Information Science & Technology University, Beijing 100192, China; 2.High-end Equipment Intelligent Perception and Control Beijing International Science & Technology Cooperation Base，Beijing Information Science and Technology University,Beijing 100192, China

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全文下载次数: 11

中文摘要:

针对传统刀具磨损监测方法在多物理场耦合状态及复杂工况下识别精度不足的问题，提出一种基于梅尔倒谱系数（Mel-frequency cepstral coefficients, MFCC）和联合分布对齐（joint distribution alignment, JDA）的跨工况刀具磨损状态识别方法，旨在解决跨工况下刀具磨损监测识别能力弱的难题。首先，采集主轴振动信号，通过梅尔倒谱系数对振动信号进行特征提取，压缩高频信息，保留中低频关键特征信息。其次，构建联合分布对齐迁移学习模型，结合源域标注数据和目标域无标注数据进行联合训练，实现跨工况下的刀具磨损知识迁移。该模型通过交叉熵损失对齐输出分布，结合最大均值差异损失对齐特征分布，并引入自训练伪标签生成机制优化目标域数据的利用效率。在NASA铣削数据集上，该方法在进给量、切深及主轴转速变化的跨工况场景下识别准确率达到93.33%，显著优于基于傅里叶变换和小波变换的传统方法。将该模型应用于超同步的铣削数据集，识别准确率达到了93.25%。所提方法有效解决了跨工况条件下刀具磨损状态识别的难题，为工业现场刀具状态监测提供了可靠的技术支持，具有重要的工程应用价值。

英文摘要:

Aiming at the problem of insufficient recognition accuracy of traditional tool wear monitoring methods under multi-physics coupling conditions and complex working conditions, this paper proposes a cross-working-condition tool wear identification method based on Mel-frequency cepstral coefficients (MFCC) and joint distribution alignment (JDA), designed to address the challenge of weak tool wear monitoring capability across varying operational conditions. First, spindle vibration signals are collected, and MFCC is employed to extract features from these vibration signals, compressing high-frequency information while preserving critical mid-to-low frequency characteristics. Second, a transfer learning model based on joint distribution alignment is constructed, which integrates source domain labeled data with target domain unlabeled data for joint training, thereby achieving knowledge transfer of tool wear patterns across different working conditions. This model aligns output distributions through cross-entropy loss, aligns feature distributions using maximum mean discrepancy (MMD) loss, and incorporates a self-training pseudo-label generation mechanism to optimize the utilization efficiency of target domain data. On the NASA milling dataset, the proposed method achieves a recognition accuracy of 93.33% in cross-working-condition scenarios involving variations in feed rate, cutting depth, and spindle speed, significantly outperforming traditional methods based on Fourier transform and wavelet transform. When applied to the CTB milling test, the model achieved a recognition accuracy of 93.25%. The proposed method effectively resolves the challenge of tool wear state recognition under cross-working-condition environments, providing reliable technical support for tool condition monitoring in industrial applications, and possesses significant engineering application value.

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