This paper aims to unveil a collaborative sparsity structure for rigorously describing the universal self-similarity property of mechanical feature information, which is an important task in the field of adaptive feature recognition. The self-similarity pattern among all local feature segments is first highlighted by an elaborately designed partition strategy, and then a row-wise group sparsity penalty is enforced under an appropriate dictionary to effectively capture the latent self-similarity features from noisy observations. Incorporating dictionary learning techniques, a collaborative sparsity learning model (CSLM) is further proposed, and meanwhile solved by a nonconvex optimization solver generated from a block proximal gradient descend framework. Moreover, the convergence property and computational complexity of the developed solver are discussed comprehensively. The advantage of this model is to adaptively achieve a satisfying sparse level to concentrate the underlying feature information and simultaneously enforce that all segments share a same active atom set to retain the desired self-similarity pattern. The proposed CSLM is profoundly evaluated through implementing feature detection for wind turbine gearbox, and it shows superior performances to many state-of-the-art feature recognition techniques.

中文翻译：

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通过非凸优化学习协作稀疏结构进行特征识别


本文旨在揭示一种协作稀疏结构，以严格描述机械特征信息的普遍自相似性，这是自适应特征识别领域的一项重要任务。首先通过精心设计的分区策略突出显示所有局部特征片段之间的自相似模式，然后在适当的字典下强制执行行组稀疏惩罚，以有效地从噪声观测中捕获潜在的自相似特征。结合字典学习技术，进一步提出了协作稀疏学习模型（CSLM），同时通过块近端梯度下降框架生成的非凸优化求解器进行求解。此外，还全面讨论了所开发求解器的收敛性和计算复杂度。该模型的优点是自适应地实现令人满意的稀疏水平以集中底层特征信息，并同时强制所有片段共享相同的活动原子集以保留所需的自相似性模式。通过对风力涡轮机齿轮箱进行特征检测，对所提出的 CSLM 进行了深入的评估，并且它显示出优于许多最先进的特征识别技术的性能。