基于联合稀疏表示的调相机振动信号数据压缩方法  

A Phase Compensator Vibration Signal Data Compression Method Based on Joint Sparse Representation

作　　者：丁子杨 赵文强 周军 王正伟 李富才[1] DING Ziyang;ZHAO Wenqiang;ZHOU Jun;WANG Zhengwei;LI Fucai(State Key Laboratory of Mechanical System and Vibration,Shanghai Jiao Tong University,Shanghai 200240,China;Electric Power Research Institute,State Grid Qinghai Electric Power Company,Xining 810000,China)

机构地区：[1]上海交通大学机械系统与振动全国重点实验室,上海200240 [2]国网青海省电力公司电力科学研究院,青海西宁810000

出　　处：《西北工程技术学报(中英文)》2025年第1期1-7,共7页Ningxia Engineering Technology

基　　金：国网青海省电力公司科技项目(522807230005)。

摘　　要：随着调相机振动信号数据量的增加,数据存储和实时监测面临越来越大的挑战。调相机故障振动信号通常包含多个频率成分,而不同类型的故障信号其频率特性差异较大,导致信号的稀疏性降低,从而增加了压缩存储的难度。针对这一问题,提出了一种基于联合稀疏表示的调相机振动信号压缩存储方法。该方法结合贪婪迭代算法和K-SVD(K均值奇异值分解)字典学习算法,形成了一种自适应的联合稀疏表示框架,能够在字典原子和测量值的基础上进行有效分析,实现调相机振动信号的高效压缩与存储。实验结果表明,该方法不仅能显著节省存储空间,而且在处理不同故障类型时,原始时域信号与重构信号的皮尔逊相关系数均超过0.9。此外,在噪声环境下,该方法依然保持较高的信号恢复精度,相比传统方法具有更强的鲁棒性和适用性。With the increasing volume of vibration signal data from phase compensators,the challenges of data storage and real-time monitoring are becoming more significant.Phase compensator fault vibration signals typically contain multiple frequency components,and the frequency characteristics of different fault signals can vary considerably,leading to a significant reduction in signal sparsity and increasing the difficulty of compression and storage.To address this issue,this paper proposes a joint sparse representation-based method for the compression and storage of phase compensator vibration signals.The method combines a greedy iterative algorithm and the K-SVD(K-singular value decomposition)dictionary learning algorithm to introduce a framework for adaptive joint sparse representation,which allows for effective analysis of the dictionary atoms and measurements to achieve efficient compression and storage of the phase compensator vibration signals.Experimental results show that the proposed method not only saves storage space but also achieves a Pearson correlation coefficient greater than 0.9 between the original and reconstructed time-domain signals for different fault types.Furthermore,the method maintains high signal recovery accuracy in noisy environments and demonstrates superior robustness and applicability compared to traditional methods.

关 键 词：调相机 K-SVD(K均值奇异值分解)算法 联合稀疏表示 压缩感知 

分 类 号：TH133[机械工程—机械制造及自动化]