Meta Monitoring System (MMS)

The Inductive Monitoring System (IMS) software provides a method of building an efficient system health monitoring software module by examining data covering the range of nominal system behavior in advance and using parameters derived from that data for the monitoring task. This software module also has the capability to adapt to the specific system being monitored by augmenting its monitoring database with initially suspect system parameter sets encountered during monitoring operations, which are later verified as nominal. While the system is offline, IMS learns nominal system behavior from archived system data sets collected from the monitored system or from accurate simulations of the system. This training phase automatically builds a model of nominal operations, and stores it in a knowledge base. The basic data structure of the IMS software algorithm is a vector of parameter values. Each vector is an ordered list of parameters collected from the monitored system by a data acquisition process. IMS then processes select data sets by formatting the data into a predefined vector format and building a knowledge base containing clusters of related value ranges for the vector parameters. In real time, IMS then monitors and displays information on the degree of deviation from nominal performance. The values collected from the monitored system for a given vector are compared to the clusters in the knowledge base. If all the values fall into or near the parameter ranges defined by one of these clusters, it is assumed to be nominal data since it matches previously observed nominal behavior. The IMS knowledge base can also be used for offline analysis of archived data.

Multiple strain sensors encoded into one or more optical fibers are affixed to a MMOD shield or structure. The optical fiber(s) is/are connected to a data collection device that records strain data at a frequency sufficient to resolve MMOD impact events. Strain data are processed and presented on a computer display. MMOD impact imparts a transient shock loading to a structure which is manifested as transient strain as the shock wave moves through the structure. MMOD impacts are determined from the time signature of, both, measured strain from multiple sensors on the optical fiber(s) as well as strain resulting from plastic strain induced in the MMOD shield and structure as a consequence of the MMOD impact (for materials exhibiting plastic strain). The array of strain sensors, encoded into one or more optical fibers using Fiber Bragg Grating (FBG) technology, records time varying strain to identify that a strike has occurred and at what time it occurred. Strike location information can be inferred from the residual plastic strain recorded by the multitude of strain sensors in the fiber(s). One or more optical fibers may be used to provide optimal coverage of the area of interest and/or to ensure a sufficient number of strain measurements are provided to accurately characterize the nature of the impact.

The current innovation utilizes heritage flight proven L-band Digital Beamforming Synthetic Aperture Radar (DBSAR) in conjunction with a new P-Band Digital beamforming Polarimetric and Interferometric EcoSAR (ESTO IIP) architecture. The system employs digital beamforming (DBF) and reconfigurable hardware to provide advanced radar capabilities not possible with conventional radar instruments. The SAR is operated without the use of a slewing antenna allowing the single radar system to provide polarimetric imaging, interferometry, and altimetry or scatterometry data types. The SAR is also capable of Sweep-SAR, simultaneous SAR/GNSS-R , and simultaneous active/passive techniques. This system has an increased coverage area and can rapidly image large areas of the surface using the simultaneous left/right imaging. The resulting images maintain their full resolution and allows for faster full coverage mapping

The tool developed through this work merges information from the electric propulsion system design phase with diagnostic tools. Information from the failure mode and effect analysis (FMEA) from the system design phase is embedded within a Bayesian network (BN). Each node in the network can represent either a fault, failure mode, root cause or effect, and the causal relationships between different elements are described through the connecting edges. This novel approach can help Fault Detection and Isolation (FDI), producing a framework capable of isolating the cause of sub-system level fault and degradation. This system: Identifies and quantifies the effects of the identified hazards, the severity and probability of their effects, their root cause, and the likelihood of each cause; Uses a Bayesian framework for fault detection and isolation (FDI); Based on the FDI output, estimates health of the faulty component and predicts the remaining useful life (RUL) by also performing uncertainty quantification (UQ); Identifies potential electric powertrain hazards and performs a functional hazard analysis (FHA) for unmanned aerial vehicles (UAVs)/Urban Air Mobility (UAM) vehicles. Despite being developed for and demonstrated with an application to an electric UAV, the methodology is generalized and can be implemented in other domains, ranging from manufacturing facilities to various autonomous vehicles.

The Wind Event Warning System (WEWS) is high-energy Doppler LIDAR sensor that measures approaching changes of wind such as an oncoming variation of wind speed that will change the power output of a wind farm. Different from low-energy, the high-energy Doppler LIDAR has the energy to reach the long distances necessary to provide adequate warning time of a wind event. With the time provided by WEWS, the blades of a wind turbine could be feathered to prevent strong wind from damaging the turbine. In addition, airports could use WEWS to protect aircraft from sudden wind hazards.