公开(公告)号：US20230281429A1

公开(公告)日：2023-09-07

申请号：US18093594

申请日：2023-01-05

Applicant: Tata Consultancy Services Limited

Inventor： Soma Bandyopadhyay ,  Sridhar Balakrishnan ,  Shruti Sachan ,  Yasasvy Tadepalli ,  Arpan Pal ,  Anish Datta ,  Karthik Leburi ,  Srinivas Raghu Raman Gadepally

IPC: G06N3/0455 ,  G06F18/21

CPC classification number: G06N3/0455 ,  G06F18/217

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.