This article considers the problem of extracting periodic envelope features from observation signals modulated by subcritically damped mechanical systems. Due to insufficient envelope structure descriptions, popular two-stage blind deconvolution techniques incur a significant performance degradation in envelope feature deconvolution tasks. Leveraging sparse learning framework, a nonnegative bounded convolutional sparse learning model (NBconvSLM), is then proposed to address it in this article, and meanwhile, a nonconvex multiblock alternating direction method of multiplier (ADMM) solver is developed to rapidly achieve satisfying deconvolutional solutions. The main highlight of NBconvSLM is that nonnegative sparse regularizer is exploited to effectively describe envelope feature structure, and bounded regularizer is further introduced to sufficiently mitigate deconvolutional envelope amplitude enlargement problem. Regularizer’ roles are verified through a set of numerical experiments. Meanwhile, algorithmic performance and superiority are profoundly evaluated with respect to state-of-the-art deconvolutional techniques. Engineering application to generator bearing fault detection further corroborates algorithmic effectiveness in latent periodic feature recognition.

中文翻译：

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包络特征反卷积的非负有界卷积稀疏学习方法

本文考虑从亚临界阻尼机械系统调制的观测信号中提取周期包络特征的问题。由于包络结构描述不充分，流行的两阶段盲反卷积技术在包络特征反卷积任务中导致性能显着下降。利用稀疏学习框架，本文提出了一种非负有界卷积稀疏学习模型（NBconvSLM）来解决这个问题，同时，开发了一种非凸多块交替方向乘法器（ADMM）求解器，以快速获得令人满意的反卷积解。NBconvSLM 的主要亮点是利用非负稀疏正则化器来有效描述包络特征结构，并进一步引入有界正则化器以充分缓解反卷积包络幅度放大问题。正则化器的作用通过一组数值实验得到验证。同时，对最先进的反卷积技术的算法性能和优越性进行了深刻的评估。发电机轴承故障检测的工程应用进一步证实了潜在周期性特征识别的算法有效性。