Ensembled Multi-classification Generative Adversarial Network for Condition Monitoring in Streaming Data with Emerging New Classes

Condition monitoring (CM) process can be viewed as the problem of streaming classification with emerging new classes (SENC). There are four fundamental challenges we are faced with: (1) timely detection of emerging new classes; (2) model update to adapt to new classes; (3) pattern recognition of these already known classes with high accuracy and (4) distinguishing between different new classes. Although intelligent surrogate models like deep learning have achieved remarkable success in the field of CM, SENC still remains a thorny challenge that has seldom been studied. This paper presents an approach to address the challenge of SENC using an ensembled multi-classification generative adversarial network (EMC-GAN). The proposed method includes a novel deep network architecture called MC-GAN that integrates the tasks of novelty detection and multi-classification into a single framework. To address issues of model stability, an efficient history-state ensemble (HSE) method that does not require additional training costs to generate multiple base models is introduced. Experimental validation is conducted on four simulated SENC tasks using benchmark data, and the results have shown the effectiveness of the proposed approach.