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


本文深入探讨了轴承早期初始退化点 （IDP） 的准确检测，并首次提出了一种适用于可变运行条件下轴承的全面自适应 IDP 检测框架，以及异常值数据修复策略。首先，本研究引入了自适应早期初始退化点检测 （AEIDPD） 方法，该方法结合了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“斜率和”指标。该文提出一种适应可变工况的自适应 IDP 阈值构建方法，建立了基于斜率和自适应阈值的 IDP 检测策略。为了增强 AEIDPD 在可变工况下的鲁棒性，该文引入同步小波变换来获得轴承振动的“同步伪速度”信号，并构建了基于速度和滑动窗口平均的条件干扰消除策略，以减轻可变工况的影响。此外，该研究使用经验参数构建了振动信号均方根特征的上限和下限，以校正异常值，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方法的有效性和稳健性。