SBIR/STTR Award attributes
Goal is to develop intelligent system tools that learn normal patterns of life from energy consumption auditing of both cyber and manufacturing devices in manufacturing systems, and use a hybrid machine-learning (ML) and a digital-twin (DT) approach to learn and correlate changed patterns from physical and cyber threats. Unknown anomalies in a manufacturing machine will be detected and characterized by the Energy Consumption Abnormality Detection (ECAD) prototype system based upon the DF&NN Goal-Driven Condition-Based Predictive Maintenance (GCPM) baseline Condition-Based Maintenance (CBM). The DF&NN-QSI team will apply our ISA tools to generate temporally overlapping known and unknown manufacturing system and energy consumption abnormality detection and historical abnormality categorization event tracks. We will apply our Smoking Gun and TEAMS tools to discover correlation relationships in these events and use these to improve cause diagnosis and determine the effectiveness of using energy consumption data to detect cyber and physical attacks. The anomalies form inputs to QSI’s TEAMS® models that capture system-agnostic functional failure-cause and effect dependency relationships. The TEAMS® model facilitates mapping these anomalies to the causal model thereby allowing TEAMS® runtime reasoning engines to perform failure root-cause isolation and corrective/preventive action determination when such anomalies are detected.
