Identifying sensor drifts and diverse varying operational conditions using variational autoencoders for continual training

Abstract

Existing machine learning systems require historical data to perform analytics to detect faults in a machine and are unable to detect new types of faults/changes occurring in real time. These systems further fail to identify operation changes due to sensor drift and forget past events that have occurred. Present application provides systems and methods for identifying and classifying sensor drifts and diverse varying operational conditions from continually received sensor data using continual training of variational autoencoders (VAE) following drift specific characteristics, wherein sensor drift is compensated based on identified changes in sensors and degradation in machine(s). Rehearsal technique is performed by either VAE based generative models trained in previous iterations that are configured to generate a dataset corresponding to a current iteration, or discriminative instances of original dataset in previous iterations that are configured to generate a dataset corresponding to a current iteration, thus preventing from catastrophic forgetting.