Antinoise Bearing Fault Diagnosis Using Time-Reassigned Multisynchrosqueezing Transform and Complex Sparse Learning Dictionary

IF 5.9 2区 工程技术 Q1 ENGINEERING, ELECTRICAL & ELECTRONIC
Wu Deng;Hongbin Li;Huimin Zhao
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Abstract

Bearings are core components of rotating machinery, and their operating status monitoring and fault diagnosis are crucial for equipment health management. Accurately identifying fault impulse signatures in bearing signals under complex operating conditions is a key challenge in bearing fault diagnosis. Therefore, this article proposes a bearing fault time–frequency diagnosis method [time-frequency complex sparse coding K-SVD (TFCSCK)] based on the time-reassigned multisynchrosqueezing transform (TMSST) and the improved k-singular value decomposition (KSVD) algorithm [complex KSVD (CKSVD)]. First, TMSST is used to obtain a high-resolution time–frequency representation (TFR) to enhance the accuracy of impulse signature localization. Second, to address the problem that the traditional KSVD algorithm only works in the real domain and ignores time–frequency phase information, a CKSVD algorithm is proposed. This algorithm utilizes complex sparse coding and dictionary updating to preserve the time–frequency phase characteristics, improving the robustness of feature extraction under complex interference. Third, a transient component time–frequency mask decomposition (TFTCD) algorithm is proposed. This algorithm preserves the time-domain waveform details of the fault impulse through mask-weighted separation and inverse transform reconstruction. Finally, the effectiveness of the proposed method is verified using numerical simulations and real fault signals. The experimental results show that TFCSCK improves the accuracy of inner race fault frequency extraction by 2.53% compared to TMSST and KSVD on the inner race data of the TYS1-8 platform. Based on measured data from aircraft engine bearings, even when both TMSST and KSVD fail, TFCSCK still extracts high-speed rotational frequency and inner race fault frequency.
基于时间重分配多同步压缩变换和复稀疏学习字典的轴承故障抗噪诊断
轴承是旋转机械的核心部件,其运行状态监测和故障诊断对设备健康管理至关重要。准确识别复杂工况下轴承信号中的故障脉冲特征是轴承故障诊断的关键问题。因此,本文提出了一种基于时间重分配多同步压缩变换(TMSST)和改进的k-奇异值分解(KSVD)算法[复KSVD (CKSVD)]的轴承故障时频诊断方法[时频复稀疏编码K-SVD (TFCSCK)]。首先,利用TMSST获得高分辨率时频表示(TFR),提高脉冲特征定位的精度;其次,针对传统的KSVD算法只适用于实域而忽略时频相位信息的问题,提出了一种CKSVD算法。该算法利用复杂稀疏编码和字典更新来保持时频相位特征,提高了复杂干扰下特征提取的鲁棒性。第三,提出了一种瞬态分量时频掩码分解算法。该算法通过掩模加权分离和逆变换重构,保留了故障脉冲的时域波形细节。最后,通过数值仿真和实际故障信号验证了该方法的有效性。实验结果表明,与TMSST和KSVD相比,TFCSCK在TYS1-8平台内圈数据上的内圈故障频率提取精度提高了2.53%。基于飞机发动机轴承实测数据,TFCSCK在TMSST和KSVD均失效的情况下,仍能提取高速旋转频率和内圈故障频率。
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来源期刊
IEEE Transactions on Instrumentation and Measurement
IEEE Transactions on Instrumentation and Measurement 工程技术-工程:电子与电气
CiteScore
9.00
自引率
23.20%
发文量
1294
审稿时长
3.9 months
期刊介绍: Papers are sought that address innovative solutions to the development and use of electrical and electronic instruments and equipment to measure, monitor and/or record physical phenomena for the purpose of advancing measurement science, methods, functionality and applications. The scope of these papers may encompass: (1) theory, methodology, and practice of measurement; (2) design, development and evaluation of instrumentation and measurement systems and components used in generating, acquiring, conditioning and processing signals; (3) analysis, representation, display, and preservation of the information obtained from a set of measurements; and (4) scientific and technical support to establishment and maintenance of technical standards in the field of Instrumentation and Measurement.
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