Feature fusion and degradation using self-organizing map

Successful prognostics is based on effective feature exaction and correct feature selection processes. Feature map is one of the widely used performance assessment and degradation detection methods. By continuously tracking the trajectories, degradation detection and prognostics in feature space can be conducted. The challenge is how to construct a feature space that can consistently exemplify the degradation pattern. In this paper, a Self Organizing Map (SOM) neural network based method is proposed to address the problem of the construction of feature space and degradation detection. Roller bearing run-to-failure tests are conducted to generate full life cycle degradation data, by which the proposed method is validated. The results demonstrate that SOM-based performance assessment and degradation detection approach provides a means of enhancing the condition monitoring of the roller bearing. It is able to provide a comprehensible indication of current operation state. Degradation detection can be fulfilled by monitoring the trajectories. Robust and quantitative performance assessment is achieved by the Minimum Quantization Error (MQE) calculation. It provides solid foundation for the development of the rolling bearing prognostic method.