An Integrated Tool for Resource Scheduling and Health Monitoring

In this proposal, Intelligent Automation, Incorporated (IAI) proposes an Automated Non-intrusive Health Monitoring tool for simultaneous component or structure degradation monitoring (trend analysis), fault detection, and diagnostics in ground systems. This prognostic technique, or health monitoring (HM) tool, can be combined with IAI's internetworking software to provide remote monitoring capability. The innovation will also allow us to detect new fault conditions that have not occurred before. This may include sensor failures and hence, the capability of validity self-checks. Our algorithm uses one major tool: Principal Component Analysis (PCA). PCA is a powerful technique for extracting the features inside the sensor signals. A major advantage of PCA is that supervised learning is unnecessary. Another advantage of PCA is that we can use it for degradation monitoring or reliability assessment. It can also be used for early detection of cracks, fatigue, and corrosion signatures buried in sensor signals. General sensor fusion architecture will be used for fusing different decisions. The proposed method is relevant to the subtopic because our innovation can provide a trend analysis that detects structural degradation due to cracks, corrosion, and fatigue. The purpose is to reduce the likelihood of structural or other failures in ground systems such as engines and gearboxes.Key Words:Non-intrusive health monitoring sensor fusion PCA