Fault diagnosis of rotating machinery based on self-learning fuzzy spiking neural network
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Abstract:
For fuzziness classific boundry of fault diagnosis of rotating machinery and traditional neural network algorithms difficulted to solve contradiction between application problems example scale and netwok scale,a methord of self-learning fuzzy spiking neural network is put forward. The methord overcomes unavailability of cluster analysis on classific boundry of fault diagnosis of rotating machinery by species encoding of pulse sequence and unsupervised learning. The method shows that it effectively solves boundary fuzziness problem on fault diagnosis of rotating machinery,and greatly improves efficiency of fault diagnosis.
