Multi-scale and multi-pooling sparse filtering: A simple and effective representation learning method for intelligent fault diagnosis

Abstract Representation learning (RL) has gained increasingly considerable attention in intelligent fault diagnosis due to its great capability of automatically learning useful features. Most existing studies focus on developing various variants about RL through modifying loss function of original versions, whereas it is a challenging task. Using the promising sparse filtering as the basic module, this paper presents a simple and effective RL method called multi-scale and multi-pooling sparse filtering (MSMPSF). Instead of modifying loss function of sparse filtering, we simply introduce two fusion mechanisms into sparse filtering, i.e., multi-scale fusion and multi-pooling fusion. In detail, the former aims to learn different local features from the collected signals under multiple scales. The latter tries to fuse various local features using multiple poolings. With these two mechanisms, MSMPSF is capable of capturing complementary fault information hidden in raw signals of several scales and obtaining richly informative feature representations, hence it can perform better. The proposed method is evaluated through experiments on three datasets about gear and bearing. Extensive comparison results confirm that both of two mechanisms facilitate a significant improvement on diagnosis performance. Furthermore, our method receives very reliable and competitive results in terms of diagnosis accuracy and stability in comparison with existing related works.