引用本文: 单振, 汤佳琛, 王重秋, 杨建华, 郝晨航, 李尚袁. 变转速工况下松动故障自适应时频模态分解[J]. 工程科学学报.
Bearing looseness fault diagnosis based on adaptive time-frequency modal decomposition under variable operating conditions[J]. Chinese Journal of Engineering.
 Citation: Bearing looseness fault diagnosis based on adaptive time-frequency modal decomposition under variable operating conditions[J]. Chinese Journal of Engineering.

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

• 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。

Abstract: Looseness faults are widely present in mechanical equipment, and the diagnosis of looseness faults under variable speed conditions still poses certain challenges. To achieve looseness fault diagnosis under variable speed operating conditions and tackle this challenge, an innovative adaptive time-frequency modal decomposition method that effectively handles non-stationary signals and enhances the accurate diagnosis of looseness is proposed in this paper. The research method begins by introducing the basic steps of time-frequency modal decomposition, involving temporal truncation through window functions and obtaining time-frequency distribution using Fourier transform. The concept of fault characteristic orders is introduced, and filtering is performed through time-frequency windows to ultimately obtain time-domain waveforms for each order mode. To enhance the adaptability of the method, we introduce fusion metrics, including signal-to-noise ratio and cross-correlation coefficient, for optimizing the selection of window width parameters. By defining and calculating the energy proportion of each order, fault feature analysis of vibration signals was completed, and feature extraction and diagnosis of looseness faults were achieved. The parameter of the window width for time-frequency modal decomposition is optimized and selected, and the nonlinear correlation characteristics between the window width and the decomposition output are studied to achieve adaptive time-frequency modal decomposition. Experimental validations are conducted on both bearing seat looseness faults and rotating component looseness faults. In the bearing seat looseness fault experiment, a comprehensive analysis of time-domain waveforms, time-frequency plots, and the output of adaptive time-frequency modal decomposition successfully achieves accurate diagnosis of the looseness fault. In the rotating component looseness fault experiment, effective diagnosis of the rotating component looseness fault is achieved through the processing of variable speed bearing vibration signals. Based on the proposed method, the fault features in the experimental signals were effectively extracted. In the experiment on bearing seat looseness faults, a comprehensive analysis of time-domain waveforms, time-frequency plots, and the output of adaptive time-frequency modal decomposition allowed us to successfully capture the characteristic information related to the looseness fault. Experimental results verified the effectiveness of the method, looseness faults caused by bearing seat looseness, and rotating parts looseness faults were both diagnosed by the proposed adaptive time-frequency modal decomposition algorithm. The experimental results show that the proposed method can achieve modal decomposition of non-stationary signals and has effective diagnostic capabilities for looseness faults. Overall, this research demonstrates the effectiveness of the adaptive time-frequency modal decomposition method in diagnosing looseness faults under variable speeds and strong noise backgrounds. The method is not only applicable to different types of looseness faults but also exhibits adaptability by optimizing parameters under varying noise intensities. This innovative approach expands the application potential in the field of signal decomposition, providing a powerful tool for the health monitoring of mechanical equipment.

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