智慧系統實驗室

Smart Machine & AI Tech. 1 

Real-time Monitoring for Fault Diagnosis.

    A set of smart fault-diagnosis technology with features production, extraction, and selection is developed via applying data-mapping strategy and K-Nearest Neighbor (K-NN) algorithm. By designing a nonlinear chaotic mathematics model consisting of a main system and a data-feed system with appropriate parameter settings, the raw vibration signals from time domain can be mathematically mapped into chaotic domain for potential features production, where those tiny differences between each of data set can be clearly observed in the chaotic domain. Further, through appropriate key-features extraction approaches-Euclidean Feature Values (EFV), Squared Euclidean Feature Values (SEFV), and the projections in the x ,y and z directions, the related scatter plots of those features can be employed for key-features selections. In the final step, the classification results as well as their corresponding accuracy rates are investigated via K-NN algorithm. The different fault-states of industrial commonly-used rotating machine, rolling-bearing system, are illustrated for performance evaluations.