变转速工况下松动故障自适应时频模态分解  

Bearing looseness fault diagnosis based on adaptive time–frequency modal decomposition under variable operating conditions

作　　者：单振 汤佳琛 王重秋 杨建华[2] 郝晨航 李尚袁 SHAN Zhen;TANG Jiachen;WANG Zhongqiu;YANG Jianhua;HAO Chenhang;LI Shangyuan(School of Computer Science and Technology,China University of Mining and Technology,Xuzhou 221116,China;School of Mechanical and Electrical Engineering,China University of Mining and Technology,Xuzhou 221116,China;Xuzhou Heavy Machinery Co.,Ltd.,Xuzhou 221004,China)

机构地区：[1]中国矿业大学计算机科学与技术学院,徐州221116 [2]中国矿业大学机电工程学院,徐州221116 [3]徐州重型机械有限公司,徐州221004

出　　处：《工程科学学报》2025年第1期130-141,共12页Chinese Journal of Engineering

基　　金：国家自然科学基金资助项目(12072362)。

摘　　要：松动故障广泛存在于机械设备之中,而在变转速工况下的松动故障诊断仍存在一定挑战.为实现变转速工况下的松动故障诊断,本文提出了一种自适应时频模态分解方法.为提高该方法的多工况自适应能力,针对时频模态分解窗宽参数进行了优化选取,研究了窗宽参数与分解输出的非线性关联特征,实现了不同噪声下的自适应时频模态分解.为验证该方法的有效性,针对支承松动故障进行了实验验证,同时在某工程设备上进行了旋转部件松动故障实验验证.采用自适应时频模态分解算法对实验验证数据进行处理,实现了非平稳特征的模态分解.通过定义和计算各阶次能量占比,完成了振动信号的故障特征分析,实现了松动故障的特征提取与诊断.结果表明,所提方法能够实现非平稳信号的模态分解,对于松动故障具备有效的诊断能力.Looseness faults are widely present in mechanical equipment,and diagnosing these faults under variable speed conditions remains challenging.To address this problem,we propose a time-frequency modal decomposition method optimized for variable speeds.To enhance the adaptability of this method across different operating conditions,we have optimized the parameter window width,obtaining an adaptive time-frequency modal decomposition technique.We conducted experimental validations on both bearing seat looseness faults and rotating component looseness faults.In the bearing seat looseness fault experiment,we analyzed time-domain waveforms,time-frequency plots,and the outputs of the adaptive time-frequency modal decomposition.This comprehensive analysis allowed us to accurately diagnose the looseness fault.In the rotating component looseness fault experiment,we processed variable speed bearing vibration signals.The proposed method effectively extracted fault features from these signals.Experimental results confirmed the effectiveness of our proposed method.Both bearing seat looseness and rotating part looseness faults were diagnosed using the adaptive time-frequency modal decomposition algorithm.Our experiments demonstrated that the proposed method can achieve modal decomposition of nonstationary signals,showcasing its effective diagnostic capabilities for looseness faults.Overall,this research highlights the effectiveness of the adaptive time-frequency modal decomposition method in diagnosing looseness faults under variable speeds and strong noise backgrounds.The method is applicable to different types of looseness faults and exhibits adaptability by optimizing parameters to cope with different noise intensities.It is important to note that this method requires instantaneous speed information from the device,which may impose some limitations on its applicability.However,in practical engineering,the technical challenge of measuring speed is relatively low.Many types of existing large-scale equipment are already equipped w

关 键 词：变转速工况 模态分解 松动故障 强噪声 自适应 

分 类 号：TH133.3[机械工程—机械制造及自动化]