Robust visual monitoring of machine condition with sparse coding and self-organizing map

Abstract

A direct way to recognize the machine condition is to map the monitored data into a machine condition space. In this paper, via combining Sparse Coding and Self-Organizing Map, a new model (SC-SOM) is proposed for robust visual monitoring of machine condition. Following the model, a Machine Condition Map (MCM) representing the machine condition space is formulated offline with the historical signals; then, during the online monitoring, the machine condition can be determined by mapping the monitoring signals onto the MCM. The application of the SC-SOM model for bearing condition monitoring verifies that the bearing condition can be correctly determined even with some disturbances. Furthermore, novel bearing conditions can also be detected with this model. © 2010 Springer-Verlag.