Monte Carlo simulation framework that produces anomaly-free training data to support ML-based prognostic surveillance

A non-transitory computer-readable storage medium storing instructions that when executed by a computer cause the computer to perform a method for producing anomaly-free training data to facilitate machine learning (ML)-based prognostic surveillance operations, the method comprising:

• receiving a dataset comprising time-series signals obtained from a monitored system during operation of the monitored system;
• dividing the dataset into a plurality of subsets;
• identifying subsets that contain anomalies from target subsets of the dataset by:
• training one or more inferential models using combinations of subsets of the plurality of subsets, and
• using the one or more trained inferential models to detect anomalies in target subsets of the dataset;
• and
• removing one or more subsets of the target subsets having the detected anomalies from the dataset, wherein the dividing the dataset into subsets and identifying the subsets that contain anomalies is an iterative process, which starts with fewer larger subsets and progresses to a larger number of smaller subsets.

A system that produces anomaly-free training data to facilitate machine learning (ML)-based prognostic surveillance operations, comprising:

• a computing system with one or more processors and one or more associated memories;
• and
• an execution mechanism that executes on the computing system, wherein during operation, the execution mechanism:
• receives a dataset comprising time-series signals obtained from a monitored system during operation of the monitored system,
• divides the dataset into a plurality of subsets,
• identifies subsets that contain anomalies from target subsets of the dataset, wherein while identifying subsets that contain anomalies, the execution mechanism trains one or more inferential models using combinations of subsets of the plurality of subsets, and uses the one or more trained inferential models to detect anomalies in target subsets of the dataset, and
• removes one or more subsets of the target subsets having the detected anomalies from the dataset, wherein while dividing the dataset into subsets and identifying the subsets that contain anomalies, the execution mechanism performs an iterative process, which starts with fewer larger subsets and progresses to a larger number of smaller subsets.