A comprehensive survey of sparse regularization: Fundamental, state-of-the-art methodologies and applications on fault diagnosis

Abstract

Sparse regularization has been attracting much attention in industrial applications over the past few decades. By exploiting the latent data structure in low-dimensional subspaces, a significant amount of research achievements have been realized in signal/image processing, pattern recognition and system identification, etc. However, very few systematic review or comprehensive survey are reported for sparse regularization including fundamentals, state-of-the-art methodologies, and applications on fault diagnosis. To fill this gap, this article conducts an in-depth review of the state-of-the-art technologies of sparse regularization, and the R&D of sparse regularization applied to fault diagnosis will also be summarized. Specifically, we discuss the rationales of cause formulation, algorithm idea, algorithm merits, algorithm demerits and computing techniques for each category. The availability and practicability of several representative models of sparse regularization are investigated with real-world experimental datasets. Finally, benefiting from theoretical developments of the sparse regularization, open/upcoming challenges, instructive perspectives, as well as possible future trends of the sparse regularization for prognostic and health management (PHM) are discussed.