Blind deconvolution has been demonstrated to be an effective tool for rolling element bearing localized defect detection. However, the successful utilization of deconvolution methods largely depends on the selection of several tunable configuration parameters, otherwise it may lead to the incomplete extraction of malfunction information. Additionally, the effectiveness of some multiple-transient-oriented deconvolution approaches strongly relies on the accurate set of the specific prior impulse period, whose application may be severely confined in practice. Coping with the aforementioned problems, an efficient as well as automatic bearing fault detection and diagnosis method based on multipoint optimal minimum entropy deconvolution adjusted (MOMEDA) and Bayesian optimization (BO) is proposed in this article, where the configuration parameters and the potential impulse period can be optimized jointly in a sequential decision-making framework. To alleviate the influence of trend terms and intricate interferences in the raw signal to some extent and promote the deconvolution result, a preprocessing method coined selective coarse enveloping (SCE) is developed based on the kurtosis indicator. In the methodological aspect, the signal after preprocessing is analyzed by the presented BO-based MOMEDA (BO-MOMEDA) method, and the period of the purified signal can be obtained, with the fault type of bearing being identified. Furthermore, both simulation analysis and experimental verifications profoundly corroborate the efficiency and superiority of the proposed method, compared with other contradistinctive methods.

中文翻译：

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基于贝叶斯优化的轴承局部缺陷自动检测的高效反卷积方法

盲解卷积已被证明是滚动轴承局部缺陷检测的有效工具。然而，反卷积方法的成功利用很大程度上取决于几个可调配置参数的选择，否则可能导致故障信息提取不完整。此外，一些面向多重瞬态的反卷积方法的有效性强烈依赖于特定先验脉冲周期的准确设置，其应用在实践中可能受到严重限制。针对上述问题，本文提出一种基于多点最优最小熵反卷积调整（MOMEDA）和贝叶斯优化（BO）的高效自动轴承故障检测与诊断方法，其中配置参数和潜在脉冲周期可以在顺序决策框架中联合优化。为了在一定程度上减轻原始信号中趋势项和复杂干扰的影响，提高反卷积效果，提出了一种基于峰度指标的预处理方法选择性粗包络（SCE）。在方法方面，通过提出的基于BO的MOMEDA（BO-MOMEDA）方法对预处理后的信号进行分析，可以获得净化信号的周期，从而识别轴承的故障类型。此外，与其他对比方法相比，仿真分析和实验验证都深刻证实了该方法的效率和优越性。