Cui Lingli, Li Wenjie, Wang Xin, Liu Dongdong
Key Laboratory of Advanced Manufacturing Technology, Beijing University of Technology, Beijing 100124, China.
School of Aeronautics, Northwestern Polytechnical University, Xi'an 710072, China.
ISA Trans. 2025 Feb;157:381-391. doi: 10.1016/j.isatra.2024.12.013. Epub 2024 Dec 10.
Dual-impulse behaviors of rolling bearings have been widely researched for quantitative diagnosis. However, it is challenging to accurately extract entry and exit moments of the fault from noise-contaminated raw signals. To address this issue, a novel quantitative diagnosis method based on digital twin model is proposed to assess the fault severity from the original signal waveform. Specifically, the quantitative diagnostic criterion for bearing faults is derived to reveal the instantaneous response characteristics of dual-impulse behaviors, and then a digital twin model is constructed to characterize the fault characteristics of the measured signal with noise-free twin signals. Subsequently, a recursive parameter optimization strategy based on cosine similarity (RPOS-CS) is proposed to optimize the twin model in real time, and fault parameters of the optimal signal will be applied to evaluate the fault size of the bearing. Finally, kernel density estimation is employed to perform uncertainty analysis on multiple diagnosis results, thereby realizing interval estimation and significantly enhancing the reliability of diagnosis results. Both simulated and experimental signals are utilized to validate the efficacy of the proposed method, and the further comparative analysis shows that it exhibits high diagnostic accuracy and outstanding reliability.
滚动轴承的双脉冲行为已被广泛研究用于定量诊断。然而，从受噪声污染的原始信号中准确提取故障的起始和结束时刻具有挑战性。为了解决这个问题，提出了一种基于数字孪生模型的新型定量诊断方法，以从原始信号波形评估故障严重程度。具体而言，推导了轴承故障的定量诊断准则以揭示双脉冲行为的瞬时响应特性，然后构建数字孪生模型用无噪声的孪生信号表征测量信号的故障特征。随后，提出了一种基于余弦相似度的递归参数优化策略（RPOS-CS）实时优化孪生模型，并将最优信号的故障参数用于评估轴承的故障大小。最后，采用核密度估计对多个诊断结果进行不确定性分析，从而实现区间估计并显著提高诊断结果的可靠性。利用模拟信号和实验信号验证了所提方法的有效性，进一步的对比分析表明该方法具有高诊断精度和出色的可靠性。
