Vehicle Integrated Prognostic Reasoner (VIPR) Metric Report

This document outlines a set of metrics for evaluating the diagnostic and prognostic schemes developed for the Vehicle Integrated Prognostic Reasoner (VIPR), a system-level reasoner that encompasses the multiple levels of large, complex systems such as those for aircraft and spacecraft. VIPR health managers are organized hierarchically and operate together to derive diagnostic and prognostic inferences from symptoms and conditions reported by a set of diagnostic and prognostic monitors. For layered reasoners such as VIPR, the overall performance cannot be evaluated by metrics solely directed toward timely detection and accuracy of estimation of the faults in individual components. Among other factors, overall vehicle reasoner performance is governed by the effectiveness of the communication schemes between monitors and reasoners in the architecture, and the ability to propagate and fuse relevant information to make accurate, consistent, and timely predictions at different levels of the reasoner hierarchy. We outline an extended set of diagnostic and prognostics metrics that can be broadly categorized as evaluation measures for diagnostic coverage, prognostic coverage, accuracy of inferences, latency in making inferences, computational cost, and sensitivity to different fault and degradation conditions. We report metrics from Monte Carlo experiments using two variations of an aircraft reference model that supported both flat and hierarchical reasoning

Data Mining for Anomaly Detection

The Vehicle Integrated Prognostics Reasoner (VIPR) program describes methods for enhanced diagnostics as well as a prognostic extension to current state of art Aircraft Diagnostic and Maintenance System (ADMS). VIPR introduced a new anomaly detection function for discovering previously undetected and undocumented situations, where there are clear deviations from nominal behavior. Once a baseline (nominal model of operations) is established, the detection and analysis is split between on-aircraft outlier generation and off-aircraft expert analysis to characterize and classify events that may not have been anticipated by individual system providers. Offline expert analysis is supported by data curation and data mining algorithms that can be applied in the contexts of supervised learning methods and unsupervised learning. In this report, we discuss efficient methods to implement the Kolmogorov complexity measure using compression algorithms, and run a systematic empirical analysis to determine the best compression measure. Our experiments established that the combination of the DZIP compression algorithm and CiDM distance measure provides the best results for capturing relevant properties of time series data encountered in aircraft operations. This combination was used as the basis for developing an unsupervised learning algorithm to define "nominal" flight segments using historical flight segments

Vehicle Integrated Prognostic Reasoner (VIPR) 2010 Annual Final Report

Honeywell's Central Maintenance Computer Function (CMCF) and Aircraft Condition Monitoring Function (ACMF) represent the state-of-the art in integrated vehicle health management (IVHM). Underlying these technologies is a fault propagation modeling system that provides nose-to-tail coverage and root cause diagnostics. The Vehicle Integrated Prognostic Reasoner (VIPR) extends this technology to interpret evidence generated by advanced diagnostic and prognostic monitors provided by component suppliers to detect, isolate, and predict adverse events that affect flight safety. This report describes year one work that included defining the architecture and communication protocols and establishing the user requirements for such a system. Based on these and a set of ConOps scenarios, we designed and implemented a demonstration of communication pathways and associated three-tiered health management architecture. A series of scripted scenarios showed how VIPR would detect adverse events before they escalate as safety incidents through a combination of advanced reasoning and additional aircraft data collected from an aircraft condition monitoring system. Demonstrating VIPR capability for cases recorded in the ASIAS database and cross linking them with historical aircraft data is planned for year two

DKit: A blackboard-based, distributed, multi-expert environment for abnormal situation management

Abnormal situation management (ASM) involves timely detection, diagnosis and correction of abnormal process conditions. Industrial statistics estimate the economic impact due to abnormal situations to be about $10 billion per year in the petrochemical industries in the U.S. alone. Process fault diagnosis, which forms the first step in ASM, deals with detection and analysis of root causes of abnormal behavior. Most diagnostic methods studied in literature tend to be restricted in their scope of application leading to the inadequacy of a single diagnostic method in meeting all the requirements of a good diagnostic system. Designing a hybrid framework based on collective problem solving is the theme of this thesis. A blackboard-based, distributed diagnostic tool kit called DKit was developed in this thesis for online real time process fault diagnosis and abnormal situation management in general. First, a set of desirable features is identified for a good diagnostic system. Different diagnostic methods are compared based on the form of process knowledge used. The inadequacy of a single method to meet all the features is the motivation for designing collective problem solving-based strategies. Second, a blackboard-based framework (DKit) is proposed and developed as an attractive alternative to individual diagnostic methods. DKit is possibly the first concrete realization of integration concepts for large scale process fault diagnosis. Key components of DKit, namely, the diagnostic methods and a scheduler which coordinates the function of different diagnostic experts are discussed in detail. A hierachical design for the scheduler with model-based digraph diagnosis at the bottom is proposed in the current design of DKit. Third, a fluid catalytic cracking unit-based testbed (called CATSIM) is developed for comprehensive testing of the proposed framework. The utility of DKit is shown through simulation runs. A hybrid neural network which combines process model information with history data for better fault diagnosis is proposed as a general framework for enhancing diagnosis by using all available process knowledge

Advanced Vibration Sensing with Radar -ADVISER

ABSTRACT A new low-cost stand-off vibration sensor based on the Doppler radar principle is presented. The baseline performance of this sensor was compared with a high-quality accelerometer in a well-controlled laboratory environment. This advanced vibration imaging sensor (ADVISER) was also validated for its prognostic health monitoring ability with a fault emulator. The ADVISER was able to detect machine misbalance and bearing damage at a distance of 4 feet without making any contact. This exceeded the performance of a high-quality screw accelerometer mounted directly on the bearing enclosure. In this paper, we present the sensor's principle of operation, summarize results of comparing it with standard accelerometers, and conclude with its potential use in industrial and aerospace applications

Model-based Diagnostics for Small-scale Turbomachines

This paper describes a case study of model-based diagnostics system development for an aircraft Auxiliary Power Unit (APU) turbine system. The o#-line diagnostics algorithms described in the paper work with historical data of a flight cycle. The diagnostics algorithms use detailed turbine engine systems models and fault model knowledge available to an engine manufacturer. The developed algorithms provide fault condition estimates and allow for consistent detection of incipient performance faults and abnormal conditions