Nonnegative Bounded Convolutional Sparse Learning Method for Envelope Feature Deconvolution

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.