Software Health Management with Bayesian Networks

Most modern aircraft as well as other complex machinery is equipped with diagnostics systems for its major subsystems. During operation, sensors provide important information about the subsystem (e.g., the engine) and that information is used to detect and diagnose faults. Most of these systems focus on the monitoring of a mechanical, hydraulic, or electromechanical subsystem of the vehicle or machinery. Only recently, health management systems that monitor software have been developed. In this paper, we will discuss our approach of using Bayesian networks for Software Health Management (SWHM). We will discuss SWHM requirements, which make advanced reasoning capabilities for the detection and diagnosis important. Then we will present our approach to using Bayesian networks for the construction of health models that dynamically monitor a software system and is capable of detecting and diagnosing faults

Bayesian Software Health Management for Aircraft Guidance, Navigation, and Control

Modern aircraft, both piloted fly-by-wire commercial aircraft as well as UAVs, more and more depend on highly complex safety critical software systems with many sensors and computer-controlled actuators. Despite careful design and V&V of the software, severe incidents have happened due to malfunctioning software. In this paper, we discuss the use of Bayesian networks (BNs) to monitor the health of the on-board software and sensor system, and to perform advanced on-board diagnostic reasoning. We will focus on the approach to develop reliable and robust health models for the combined software and sensor systems

R2U2: Tool Overview

R2U2 (Realizable, Responsive, Unobtrusive Unit) is an extensible framework for runtime System HealthManagement (SHM) of cyber-physical systems. R2U2 can be run in hardware (e.g., FPGAs), or software; can monitorhardware, software, or a combination of the two; and can analyze a range of different types of system requirementsduring runtime. An R2U2 requirement is specified utilizing a hierarchical combination of building blocks: temporal formula runtime observers (in LTL or MTL), Bayesian networks, sensor filters, and Boolean testers. Importantly, the framework is extensible; it is designed to enable definitions of new building blocks in combination with the core structure. Originally deployed on Unmanned Aerial Systems (UAS), R2U2 is designed to run on a wide range of embedded platforms, from autonomous systems like rovers, satellites, and robots, to human-assistive ground systems and cockpits. R2U2 is named after the requirements it satisfies; while the exact requirements vary by platform and mission, the ability to formally reason about realizability, responsiveness, and unobtrusiveness is necessary for flight certifiability, safety-critical system assurance, and achievement of technology readiness levels for target systems. Realizability ensures that R2U2 is suficiently expressive to encapsulate meaningful runtime requirements while maintaining adaptability to run on different platforms, transition between different mission stages, and update quickly between missions. Responsiveness entails continuously monitoring the system under test, real-time reasoning, reporting intermediate status, and as-early-as-possible requirements evaluations. Unobtrusiveness ensures compliance with the crucial properties of the target architecture: functionality, certifiability, timing, tolerances, cost, or other constraints

R2U2: Monitoring and Diagnosis of Security Threats for Unmanned Aerial Systems

We present R2U2, a novel framework for runtime monitoring of security properties and diagnosing of security threats on-board Unmanned Aerial Systems (UAS). R2U2, implemented in FPGA hardware, is a real-time, REALIZABLE, RESPONSIVE, UNOBTRUSIVE Unit for security threat detection. R2U2 is designed to continuously monitor inputs from the GPS and the ground control station, sensor readings, actuator outputs, and flight software status. By simultaneously monitoring and performing statistical reasoning, attack patterns and post-attack discrepancies in the UAS behavior can be detected. R2U2 uses runtime observer pairs for linear and metric temporal logics for property monitoring and Bayesian networks for diagnosis of security threats. We discuss the design and implementation that now enables R2U2 to handle security threats and present simulation results of several attack scenarios on the NASA DragonEye UAS

Diagnostic Reasoning using Prognostic Information for Unmanned Aerial Systems

With increasing popularity of unmanned aircraft, continuous monitoring of their systems, software, and health status is becoming more and more important to ensure safe, correct, and efficient operation and fulfillment of missions. The paper presents integration of prognosis models and prognostic information with the R2U2 (REALIZABLE, RESPONSIVE, and UNOBTRUSIVE Unit) monitoring and diagnosis framework. This integration makes available statistically reliable health information predictions of the future at a much earlier time to enable autonomous decision making. The prognostic information can be used in the R2U2 model to improve diagnostic accuracy and enable decisions to be made at the present time to deal with events in the future. This will be an advancement over the current state of the art, where temporal logic observers can only do such valuation at the end of the time interval. Usefulness and effectiveness of this integrated diagnostics and prognostics framework was demonstrated using simulation experiments with the NASA Dragon Eye electric unmanned aircraft

Towards Real-time, On-board, Hardware-Supported Sensor and Software Health Management for Unmanned Aerial Systems

Unmanned aerial systems (UASs) can only be deployed if they can effectively complete their missions and respond to failures and uncertain environmental conditions while maintaining safety with respect to other aircraft as well as humans and property on the ground. In this paper, we design a real-time, on-board system health management (SHM) capability to continuously monitor sensors, software, and hardware components for detection and diagnosis of failures and violations of safety or performance rules during the flight of a UAS. Our approach to SHM is three-pronged, providing: (1) real-time monitoring of sensor and/or software signals; (2) signal analysis, preprocessing, and advanced on the- fly temporal and Bayesian probabilistic fault diagnosis; (3) an unobtrusive, lightweight, read-only, low-power realization using Field Programmable Gate Arrays (FPGAs) that avoids overburdening limited computing resources or costly re-certification of flight software due to instrumentation. Our implementation provides a novel approach of combining modular building blocks, integrating responsive runtime monitoring of temporal logic system safety requirements with model-based diagnosis and Bayesian network-based probabilistic analysis. We demonstrate this approach using actual data from the NASA Swift UAS, an experimental all-electric aircraft

Intelligent Hardware-Enabled Sensor and Software Safety and Health Management for Autonomous UAS

Unmanned Aerial Systems (UAS) can only be deployed if they can effectively complete their mission and respond to failures and uncertain environmental conditions while maintaining safety with respect to other aircraft as well as humans and property on the ground. We propose to design a real-time, onboard system health management (SHM) capability to continuously monitor essential system components such as sensors, software, and hardware systems for detection and diagnosis of failures and violations of safety or performance rules during the ight of a UAS. Our approach to SHM is three-pronged, providing: (1) real-time monitoring of sensor and software signals; (2) signal analysis, preprocessing, and advanced on-the- y temporal and Bayesian probabilistic fault diagnosis; (3) an unobtrusive, lightweight, read-only, low-power hardware realization using Field Programmable Gate Arrays (FPGAs) in order to avoid overburdening limited computing resources or costly re-certi cation of ight software due to instrumentation. No currently available SHM capabilities (or combinations of currently existing SHM capabilities) come anywhere close to satisfying these three criteria yet NASA will require such intelligent, hardwareenabled sensor and software safety and health management for introducing autonomous UAS into the National Airspace System (NAS). We propose a novel approach of creating modular building blocks for combining responsive runtime monitoring of temporal logic system safety requirements with model-based diagnosis and Bayesian network-based probabilistic analysis. Our proposed research program includes both developing this novel approach and demonstrating its capabilities using the NASA Swift UAS as a demonstration platform

Novel Evolutionary-based Methods for the Robust Training of SVR and GMDH Regressors

En los últimos años se han consolidado una serie de diferentes métodos y algoritmos para problemas de aprendizaje máquina y optimización de sistemas, que han dado lugar a toda una corriente de investigación conocida como Soft-Computing. El término de Soft-Computing hace referencia a una colección de técnicas computacionales que intenta estudiar, modelar y analizar fenómenos muy complejos, para los que los métodos convencionales no proporcionan soluciones completas, o no las proporcionan en un tiempo razonable. Dentro de lo que se considera como Soft-Computing existen una gran cantidad de técnicas tales como Redes Neuronales, Máquinas de Vectores Soporte (SVM), Redes Bayesianas, Computación Evolutiva (Algoritmos Genéticos, Algoritmos Evolutivos etc), etc. La investigación de la Tesis está enfocada en dos de estas técnicas, en primer lugar las máquinas de vectores soporte de regresión (SVR) y en segundo lugar a las GMDH (Group Method of Data Handling). Las SVM son una técnica ideada por Vapnik, basada en el principio de minimización del riesgo estructural y la teoría de los métodos kernel, que a partir de un conjunto de datos construye una regla de decisión con la cual intentar predecir nuevos valores para dicho proceso a partir de nuevas entradas. La eficiencia de los sistemas SVM ha hecho que tengan un desarrollo muy significativo en los últimos años y se hayan utilizado en una gran cantidad de aplicaciones tanto para clasificación como para problemas de regresión (SVR). Uno de los principales problemas es la búsqueda de los que se conoce como hiper-parámetros. Estos parámetros no pueden ser calculados de forma exacta, por lo que se hace necesario testear un gran número de combinaciones, para obtener unos parámetros que generen una buena función de estimación. Debido a esto el tiempo de entrenamiento suele ser elevado y no siempre los parámetros encontrados generan una buena solución: ya sea porque el algoritmo de búsqueda tenga un pobre rendimiento o porque el modelo generado está sobre-entrenado. En esta Tesis se ha desarrollado un nuevo algoritmo de tipo evolutivo para el entrenamiento con kernel multi-paramétrico. Este nuevo algoritmo tiene en cuenta un parámetro distinto, para cada una de las dimensiones del espacio de entradas. En este caso, debido al incremento del número de parámetros no puede utilizarse una búsqueda en grid clásica, debido al coste computacional que conllevaría. Por ello, en esta Tesis se propone la utilización de un algoritmo evolutivo para la obtención de los valores óptimos de los parámetros de la SVR y la aplicación de nuevas cotas para los parámetros de este kernel multi-paramétrico. Junto con esto, se han desarrollado nuevos métodos de validación que mejoren el rendimiento de las técnicas de regresión en problemas data-driven. La idea es obtener mejores modelos en la fase de entrenamiento del algoritmo, de tal forma que el desempeño con el conjunto de test mejore, principalmente en lo que a tiempo de entrenamiento se refiere y en el rendimiento general del sistema, con respecto a otros métodos de validación clásicos como son K-Fold cross-validation, etc. El otro foco de investigación de esta Tesis se encuentra en la técnica GMDH, ideada en los años 70 por Ivakhnenko. Es un método particularmente útil para problemas que requieran bajos tiempos de entrenamiento. Es un algoritmo auto-organizado, donde el modelo se genera de forma adaptativa a partir de los datos, creciendo con el tiempo en complejidad y ajustándose al problema en cuestión, hasta que el modelo alcanza un grado de complejidad óptima, es decir, no es demasiado simple ni demasiado complejo. De esta forma el algoritmo construye el modelo en base a los datos de los que dispone y no a una idea preconcebida del investigador, como ocurre en la mayoría de las técnicas de Soft-Computing. Las GMDH también tienen algunos inconvenientes como son los errores debido al sobre-entrenamiento y la multicolinealidad, esto hace que en algunas ocasiones el error sea elevado si lo comparamos con otras técnicas. Esta Tesis propone un nuevo algoritmo de construcción de estas redes basado en un algoritmo de tipo hiper-heurístico. Esta aproximación es un concepto nuevo relacionado con la computación evolutiva, que codifica varios heurísticos que pueden ser utilizados de forma secuencial para resolver un problema de optimización. En nuestro caso particular, varios heurísticos básicos se codifican en un algoritmo evolutivo, para crear una solución hiper-heurística que permita construir redes GMDH robustas en problemas de regresión. Todas las propuestas y métodos desarrollados en esta Tesis han sido evaluados experimentalmente en problemas benchmark, así como en aplicaciones de regresión reales

Software Health Management with Bayesian Networks

Carnegie Mellon Silicon Valle

Software Health Management with Bayesian Networks

Software Health Management (SWHM) is an emerging field which addresses the critical need to detect, diagnose, predict, and mitigate adverse events due to software faults and failures. These faults could arise for numerous reasons including coding errors, unanticipated faults or failures in hardware, or problematic interactions with the external environment. This paper demonstrates a novel approach to software health management based on a rigorous Bayesian formulation that monitors the behavior of software and operating system, performs probabilistic diagnosis, and provides information about the most likely root causes of a failure or software problem. Translation of the Bayesian network model into an efficient data structure, an arithmetic circuit, makes it possible to perform SWHM on resource-restricted embedded computing platforms as found in aircraft, unmanned aircraft, or satellites. SWHM is especially important for safety critical systems such as aircraft control systems. In this paper, we demonstrate our Bayesian SWHM system on three realistic scenarios from an aircraft control system: (1) aircraft file-system based faults, (2) signal handling faults, and (3) navigation faults due to IMU (inertial measurement unit) failure or compromised GPS (Global Positioning System) integrity. We show that the method successfully detects and diagnoses faults in these scenarios. We also discuss the importance of verification and validation of SWHM systems