Inductive Monitoring System

Meta Monitoring System (MMS) was developed as an add-on to NASA Ames patented Inductive Monitoring System (IMS), which estimates deviation from normal system operations. MMS helps to interpret deviation scores and determine whether anomalous behavior is transient or systemic. MMS has two phases: a model-building training phase, and a monitoring phase. MMS not only uses deviation scores from nominal data for training but can also make limited use of results from anomalous data. The invention builds two models: one of nominal deviation scores and one of anomalous deviation scores, each consisting of a probability distribution of deviation scores. After the models are built, incoming deviation scores from IMS (or a different monitoring system that produces deviation scores) are passed to the learned model, and probabilities of producing the observed deviation scores are calculated for both models. In this fashion, users of MMS can interpret deviation scores from the monitoring system more effectively, reducing false positives and negatives in anomaly detection. Note: Patent license only; no developed software available for licensing

The i-DME is a computer-user interactive procedure for repairing the system model through its abstract representation, diagnostic matrix (D-matrix) and then translating the changes back to the system model. The system model is a schematic representation of faults, tests, and their relationship in terms of nodes and arcs. D-matrix is derived from the system models propagation paths as the relationships between faults and tests. When the relation exists between fault and test, it is represented as 1 in the D-matrix. To repair the D-matrix and wrapper/test logic by playing back a sequence of nominal and failure scenarios (given), the user sets the performance criteria and accepts/declines the proposed repairs. During D-matrix repair, the interactive procedure includes conditions ranging from modifying 0s and 1s in the matrix, adding/removing the rows (failure sources) columns (tests), or modifying test/wrapper logic used to determine test results. The translation of changes to the system model is done via a process which maps each portion of the D-matrix model to the corresponding locations in the system model. Since the mapping back to the system model is non-unique, more than one candidate system model repair can be suggested. In addition to supporting the modification, it provides a trace for each modification such that a rational basis for each decision can be verified.

Acoustical studies of atmospheric events like convective storms, shear-induced turbulence, acoustic gravity waves, microbursts, hurricanes, and clear air turbulence over the last forty-five years have established that these events are strong emitters of infrasound (sound at frequencies below 20 Hz). Over the years, NASA Langley has designed and developed a portable infrasonic detection system which can be used to make useful infrasound measurements at a location where it was not possible previously. The system comprises an electret condenser microphone, and a small, compact windscreen. The system has been modified to be used in the air, underground, as well as underwater (to determine man-made and precursor to tsunami). The system also features a data acquisition system that permits real-time detection, bearing, and signature of a low frequency source. However, to determine bearing of the received signals, the microphones are to be arranged as an equilateral triangle with a certain microphone spacing. The spacing depends upon location of the microphone array. For a ground-based array, the microphone spacing of 100 feet (30.48m) is desired to determine time delay for signals arriving at each microphone location. The microphone spacing depends upon speed of sound through the array medium. For underwater array, the spacing between microphones would be around 1500 feet. The data acquisition system provides data output in the infrasonic bandwidth which is then analyzed using an adaptive algorithm (least-mean-squares time-delay-estimation) using modern computational power to locate source by plotting source location hyperbolas on-line. A smaller array size reduces the time resolution resulting in strong signal coherence. The innovation approach is to exploit modern signal processing methods, i.e. adaptive filtering, where computer is trained on-line to recognize features of the event to be detected. Modern computational capability permits the adaptive algorithm (least-mean-squares time-delay estimation or LMSTDE) which is vastly more powerful algorithm. This system has better resolution able to determine direction with arrived signals within five-degree accuracy.

In this method an autonomic entity manages a system through the generation of one or more stay alive signals by a hierarchical evolvable synthetic neural system. The generated signal is based on the current functioning status and operating state of the system and dictates whether the system will stay alive, initiate self-destruction, or initiate sleep mode. This method provides a solution to the long standing need for a synthetic autonomous entity capable of adapting itself to changing external environments and ceasing its own operation upon the occurrence of a predetermined condition deemed harmful.

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.