Inductive Monitoring System

About this technology

Overview

The Inductive Monitoring System (IMS) software utilizes techniques from the fields of model-based reasoning, machine learning, and data mining to build system monitoring knowledge bases from archived or simulated sensor data. Unlike some other machine learning techniques, IMS does not require examples of anomalous (failure) behavior. IMS automatically analyzes nominal system data to form general classes of expected system sensor values. This process enables the software to inductively learn and model nominal system behavior. The generated data classes are then used to build a monitoring knowledge base. In real-time, IMS performs monitoring functions, determining and displaying the degree of deviation from nominal performance. IMS trend analyses can detect conditions that may indicate a failure or required system maintenance. The development of the IMS was motivated by the difficulty of producing detailed diagnostic models of some system components due to complexity or unavailability of design information. Previous and current IMS applications include the Hybrid Combustion Facility (HCF) advanced rocket fuel test facility, and the RASCAL UH-60 Blackhawk helicopter.