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

Authors:
Haining Liu;Yanming Li;Nan Li;Chengliang Liu
Affiliations:
School of Mechanical Engineering, Shanghai Jiao Tong University, Shanghai, China;School of Mechanical Engineering, Shanghai Jiao Tong University, Shanghai, China;School of Mechanical Engineering, Shanghai Jiao Tong University, Shanghai, China;School of Mechanical Engineering, Shanghai Jiao Tong University, Shanghai, China
Venue:
ICIRA'10 Proceedings of the Third international conference on Intelligent robotics and applications - Volume Part I
Year:
2010

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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 (SCSOM) 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.