A new hierarchical method for fault detection, isolation, and identification of nonlinear systems with applications for in-orbit closed-loop controlled satellites is proposed where fault detection employs unscented Kalman filter and adaptive thresholds. Fault isolation employs multiple model approach in conjunction with Bayes’ probability theorem and adaptive window, and fault identification employs dual state/parameter estimation using unscented Kalman filter. Results show up to 97% success rate under comprehensive Monte Carlo simulations for transient abrupt faults in satellite actuators.

Fault Isolation of Reaction Wheels for Satellite Attitude Control

This paper presents a model-based fault management (FM) system designed to provide off-nominal state detection and isolation capabilities that are key components to assessing spacecraft state awareness. The ability to autonomously isolate spacecraft failures to component levels will enable faster and more targeted responses and recovery thereby reducing down time. The use of model-based systems and practices is being explored by the FM community as a viable approach to developing more capable, autonomous systems in order to meet mission objectives. Model-based systems can provide better fault identification than traditional methods of fault detection such as limit-checking. They also lend themselves to more straight-forward approaches to verification and validation. We have chosen a particular model-based technique called Constraint Suspension for autonomous fault detection and isolation that does not require explicit fault modeling. The system is composed of a diagnostic engine and nominal system models of the target application, for example sensors and actuators. Sensed data are propagated through models of nominal system behavior. Faults are diagnosed when inconsistencies arise between sensed and modeled data. Several benefits result from this choice. First, because knowledge of faulty behavior is not required, it is possible to detect unanticipated and unforeseen faults. In fact, anomalous, degraded, and failed states all can be detected. Second, the same models used for nominal analyses and operations can be re-used for fault management, saving development resources and time. Third, the core diagnostic engine algorithm is complete and requires no additions to accommodate a potentially growing number of faults over time resulting in a relatively compact software footprint. Related to the second and third points is that the core algorithm and, potentially, models can be reused from mission to mission. Finally, the system can be used early in the design phase as a tool for sensor placement analyses and model verification. Health information produced by the FM system can be used to make resource allocation and planning and scheduling decisions by ground operations or by other on-board autonomy agents. Autonomous fault detection, isolation, and recovery (FDIR) on board space vehicles will provide protection and increased mission availability and reliability. On the ground such systems enable lights-out monitoring as well as training and support for operators. This paper presents the development of fault detection and isolation algorithms and models. Application of the system to a spacecraft attitude control system is discussed. Finally we apply Model-Based Systems Engineering (MBSE) modeling patterns to the fault management system models as a way to facilitate the development of the models through the use of SysML.

Model-based off-nominal state isolation and detection system for autonomous fault management

Human-robot interaction (HRI) research frequently explores how to design interfaces that enable humans to effectively teleoperate and supervise robots. One of the principle goals of such systems is to support data collection, analysis, and human decision making, which requires representing robot data in ways that support fast and accurate analyses by humans. However, the interfaces for these systems do not always use best-practice principles for effectively visualizing data. We present a new framework to scaffold reasoning about robot interface design that emphasizes the need to consider data visualization for supporting analysis and decision making processes, detail several data visualization best practices relevant to HRI, identify a set of core data tasks that commonly occur in HRI, and highlight several promising opportunities for further synergistic activities at the intersection of these two research areas.

https://dl.acm.org/doi/pdf/10.1145/3434073.3444683

Connecting Human-Robot Interaction and Data Visualization

Self‐reliant rovers for increased mission productivity

System health diagnosis serves as an underpinning enabler for enhanced safety and optimized maintenance tasks in complex assets. In the past four decades, a wide-range of diagnostic methods have been proposed, focusing either on system or component level. Currently, one of the most quickly emerging concepts within the diagnostic community is system level diagnostics. This approach targets in accurately detecting faults and suggesting to the maintainers a component to be replaced in order to restore the system to a healthy state. System level diagnostics is of great value to complex systems whose downtime due to faults is expensive. This paper aims to provide a comprehensive review of the most recent diagnostics approaches applied to hardware systems. The main objective of this paper is to introduce the concept of system level diagnostics and review and evaluate the collated approaches. In order to achieve this, a comprehensive review of the most recent diagnostic methods implemented for hardware systems or components is conducted, highlighting merits and shortfalls.

http://www.diagnostyka.net.pl/pdf-99603-31822?filename=A review of model based.pdf

A review of model based and data driven methods targeting hardware systems diagnostics

This paper presents an analysis tool useful for assessing diagnostic performance of a model-based fault management (FM) system. The FM system called MONSID is designed to provide off-nominal state detection and identification capabilities that are key components to assessing spacecraft state awareness. The analysis tool can be applied to MONSID models to predict MONSID's diagnostic performance for various sensor suite configurations and model topologies. The underlying algorithms of the diagnostic resolution analysis tool are discussed. The tool is applied to a MONSID model of a robot power subsystem to illustrate how MONSID's ability to distinguish among potentially faulty components is affected by the number of sensors and their placement (injection points) in the model. The tool can be utilized for FM design early in the program by supporting sensor suite selection. In the operations phase, it can be used to reduce the amount of onboard processing required by the MONSID engine in the fault identification process.

Model-based approach to rover health assessment for increased productivity

Achieving the science exploration and defense goals of the following decades will require flight systems capable of operations with limited operator contact, system mode changes and retasking based on sensor data, and complex robotic operations. To support these capabilities, increasingly autonomous flight systems are required that can perform dedicated mission functions, e.g. payload targeting and communications, and system-level functions, e.g. planning and goal monitoring. Architecting an autonomous system requires a well-reasoned, self-consistent framework to avoid ad hoc design choices that will introduce complexity and risk. The Framework for Robust Execution and Scheduling of Commands On-Board, FRESCO, is the result of lessons learned in developing a software architecture to enable autonomous solar system exploration. FRESCO generalizes this work to offer a modular, software-agnostic approach to developing verifiable architecture for autonomous space systems. FRESCO specifies guiding principles, functions, interfaces, and interactions from which mission-specific autonomous control architectures can be derived. FRESCO is a principled framework relying on explicit, state-based goal definitions, centralized management of state knowledge, clearly separated control boundaries, and hierarchical reasoning. Using components from FRESCO reference architecture, an autonomous decision-making architecture can be designed for spacecraft which can then be mapped to flight software architecture. FRESCO is flexibly defined to enable autonomous control of flight systems built using extensive software and hardware heritage. Finally, FRESCO-derived architectures support a spectrum of operator/spacecraft interactions, ranging from traditional commanding to goal-driven commanding with the ability to change mission goals autonomously. FRESCO has been used in defining the autonomy architectures for the ASTERIA mission and have been demonstrated in laboratory and software simulation for small body rendezvous and in-space servicing missions.

FRESCO: A Framework for Spacecraft Systems Autonomy

This paper presents the modeling process and test results of the MONSID fault diagnosis system as applied to the mobility subsystem of JPL's Athena development rover. Athena's mobility system includes independent steering and driving motors for six wheels as well as wheel position sensors. The rover mobility system proved particularly challenging to model due to limited visibility into lower level wheel assembly hardware, a high degree of terrain interaction, and incomplete understanding of Athena behavior. However, even with simplified models, MONSID proved able to detect 16 distinct anomaly types during functional tests and simulated science activities carried out in JPL's Mars Yard. These faults included simulated and actual motor faults as well as instances of stalls due to terrain. Additionally, several detected faults were discovered during MONSID testing that were not anticipated. System characterization during the modeling process revealed several hardware and operational problems, leading to updates in Athena's control firmware and sensor processing software.

Ksenia Kolcio

Papers

Model-based off-nominal state isolation and detection system for autonomous fault management

Self‐reliant rovers for increased mission productivity

Model-based approach to rover health assessment for increased productivity

FRESCO: A Framework for Spacecraft Systems Autonomy

Model-Based Approach to Rover Health Assessment - Mars Yard Discoveries