Abstract: Health monitoring of complex industrial assets remains the most critical task for avoiding downtimes, improving system reliability, safety and maximizing utilization. Recent advances in time-series synthetic data generation have several inherent limitations for realistic applications. A method and system have been provided for generating mixed variable type multivariate temporal synthetic data. The system provides a framework for condition and constraint knowledge-driven synthetic data generation of real-world industrial mixed-data type multivariate time-series data. The framework consists of a generative time-series model, which is trained adversarially and jointly through a learned latent embedding space with both supervised and unsupervised losses. The system addresses the key desideratum in diverse time dependent data fields where data availability, data accuracy, precision, timeliness, and completeness are of prior importance in improving the performance of the deep learning models.

Abstract: Existing privacy-preserving techniques suffer from inherent drawbacks to retain characteristics of observed, and original industrial time series data for utility in the downstream tasks such as process modelling, control, optimization and etc. The embodiments herein provide a method and system for privacy preserving generative mechanism for data-disclosure of the industrial multivariate mixed-variable time series data. The system fuses an industrial time series data with a random gaussian noise to preserve the privacy of the industrial time series data and trades-off the privacy with the utility of synthetic-private data. Further, the system presents the privacy-preserving synthetic industrial data generative mechanism for data disclosure with minimal risk of AI technique and strong privacy guarantees.

Abstract: Recent advances and techniques in missing data imputation suffer from inherent limitations of preserving the relationship among the input feature attributes and the target variable and temporal relations between observations spanning across timeframes because of which it is also challenging to reconcile missing data for any downstream tasks. Present disclosure provides system and method that implement for congeniality-preserving Generative Adversarial Networks (cpGAN) for imputing low-dimensional incomplete multivariate industrial time-series data. The method minimizes the rubric based on the information theory for Machine Learning (ML) between the empirical probability distributions of the reconcile data and the non-linear original data to preserve the temporal dependencies and retain the input feature-attributes and target-variable relationship and probability distributions of the original data.