This paper delves into the accurate detection of the early initial degradation point (IDP) in bearings, and proposes, for the first time, a comprehensive adaptive IDP detection framework for bearings under variable operating conditions, along with an outlier data repair strategy. First, this study introduces the adaptive early initial degradation point detection (AEIDPD) method, which incorporates least-squares fitting to compute the slope and intercept of health indicators, and t-tests are used to construct the “sum-of-slopes” indicator. An adaptive IDP threshold construction method that adapts to variable operating conditions is proposed, establishing a strategy for IDP detection based on sum-of-slopes and adaptive thresholds. To enhance the robustness of AEIDPD in variable operating conditions, this paper introduces synchronized wavelet transform to obtain the "synchronous pseudo-speed" signal of bearing vibration, and constructs a condition interference elimination strategy based on velocity and sliding window averaging to mitigate the effects of variable operating conditions. Additionally, the study constructs upper and lower bounds for the root mean square feature of vibration signals using empirical parameters to correct outliers, providing more accurate data to support bearing life predictions. Experimental results demonstrate the effectiveness and robustness of the proposed methods.

中文翻译：

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轴承的自适应早期初始退化点检测和异常值校正


本文深入研究了轴承早期初始退化点（IDP）的准确检测，并首次提出了一种针对可变操作条件下轴承的全面自适应 IDP 检测框架以及异常数据修复策略。首先，本研究引入了自适应早期初始退化点检测（AEIDPD）方法，该方法结合最小二乘拟合来计算健康指标的斜率和截距，并使用t检验来构建“斜率之和”指标。提出了一种适应可变操作条件的自适应IDP阈值构建方法，建立了基于斜率和和自适应阈值的IDP检测策略。为了增强AEIDPD在变工况下的鲁棒性，本文引入同步小波变换来获取轴承振动的“同步伪速度”信号，并构建基于速度和滑动窗口平均的工况干扰消除策略，以减轻工况干扰的影响。可变的操作条件。此外，该研究还利用经验参数构建了振动信号均方根特征的上限和下限，以纠正异常值，从而提供更准确的数据来支持轴承寿命预测。实验结果证明了所提出方法的有效性和鲁棒性。