Fault Detection, Isolation And Recovery Research Articles

Physical modeling and simulation of electro-mechanical actuator-based TVC systems for reusable launch vehicles

This paper presents the high-fidelity physical modeling of a Thrust Vector Control (TVC) system operated by Electro-Mechanical Actuators (EMAs) for Reusable Launch Vehicles (RLVs). In contrast to simplified, often linear, models, high-fidelity physical models enable a better assessment of the actual system performance and a deeper verification of the on-board software robustness against disturbances, unmodeled dynamics or degradation. This aspect is essential to enable the reusability concept as driven by long-term reliability requirements. Moreover, critical systems like Fault Detection, Isolation and Recovery (FDIR) logic, whose robustness and efficacy are often difficult to prove, can be thoroughly tested without requiring hardware experiments with eventual invasive modifications for fault injections. This work captures the whole dynamics of the EMA and TVC components, including the power drive electronics, the electrical motor, and the mechanical transmission for the EMA, as well as the mechanical properties of the engine nozzle acting as a load. The resulting differential algebraic equation system is modeled in the Modelica acausal object-oriented modeling language. An ad-hoc framework is implemented to guarantee flexibility and modularity, which allows models with different fidelity levels to be easily exchanged to achieve the simulation objectives and needed accuracy level. The impact of the different models on the closed-loop TVC dynamics is analyzed in both the frequency and time domain, and then benchmarked with a realistic RLV mission. The results show that the high-fidelity physical models provide a better understanding of the more complex effects governing the TVC dynamics and can, in turn, effectively improve its requirement definition process.

The path towards increasing RAMS for novel complex missions based on CubeSat technology

The paper presents the initial outcomes of a project, currently ongoing under the supervision of the European Space Agency, having the main objective to specify and design a Fault Detection Isolation and Recovery (FDIR) system by making use of relevant RAMS (Reliability, Availability, Maintainability, Safety) analyses for missions in non-deterministic environment with limited resources. The initial project tasks have been to select a study case represented by a CubeSat complex mission, analyse in detail both its mission and system requirements and, based on them, define a set of relevant RAMS analyses to be carried out in the second phase of the project, as inputs for the development of a FDIR concept aimed at a careful balance of the limited spacecraft resources in case of critical failures. Two possible study cases have been identified: LUMIO, a 12U CubeSat mission for the observation of micro-meteoroid impacts on the Lunar farside, and M-ARGO, a 12U deep-space CubeSat which will rendezvous with a near-Earth asteroid and characterize its physical properties for the presence of in-situ resources. Although both missions are characterized by a high level of autonomy and complexity in a harsh environment, LUMIO has been eventually selected as study case for the project. In the paper, the challenges and features of this mission are shortly presented. The specificities of the RAMS analysis and FDIR concept for this specific class of small satellite missions (including the selected study case) are highlighted in the paper, looking in particular at aspects such as the improvement of reliability while maintaining the CubeSat philosophy, the tuning of mission and system requirements in view of facilitating the design and implementation of the FDIR concept, and the current gaps within the RAMS/FDIR body of knowledge. The conclusions drawn during this first project phase provide a real view of how systems engineering must work in tandem with RAMS analyses and FDIR to achieve a more robust and functional mission architecture, thus improving the mission reliability.

Design and implementation of payload system of the Zhurong Mars rover

<p indent="0mm">Using the scientific payload configured on the Mars rover to explore the Mars surface can conduct high-resolution <italic>in situ</italic> surveys in a critical area of Mars. The scientific payloads configured on the rover include Navigation and Terrain Camera (NaTeCam), Multispectral Camera (MSCam), Mars Rover Penetrating Radar (RoPeR), Mars Surface Composition Detector (MarSCoDe), Mars Rover Magnetometer (RoMAG) and Mars Climate Station (MCS). The payloads are controlled and managed by the integrated payload controller. In view of the demand for autonomous control of multiple payloads for collaborative scientific exploration under the resource shortage condition, the centralized payloads control method was adopted, and a novel integrated hardware architecture of payloads electronics, and payloads data management was established. An efficient autonomous payloads explore mode based on the working mode program was innovatively designed, and several health management measures were taken to perform fault detection, isolation and recovery (FDIR). The Zhurong Mars rover has successfully completed the scheduled exploration mission. All payloads work normally, and the exploration data are valid. This paper introduces the design and implementation of the payload system of the Mars rover.

Flight Anomaly Detection for Airborne Wind Energy Systems

Airborne wind energy (AWE) systems use tethered flying devices to harvest wind energy beyond the height range accessible to tower-based turbines. AWE systems can produce the electric energy with a lower cost by operating in high altitudes where the wind regime is more stable and stronger. For the commercialization of AWE, system reliability and safety have become crucially important. To reach required availability and safety levels, we adapted an fault detection, isolation and recovery (FDIR) architecture from space industry. This work focuses on, “flight anomaly detection” layer of the FDIR. Tests verifies that proposed architecture is capable of detecting flight anomalies without generating false alarms.

Embedded Bayesian Network Contribution for a Safe Mission Planning of Autonomous Vehicles

Bayesian Networks (BN) are probabilistic models that are commonly used for the diagnosis in numerous domains (medicine, finance, transport, robotics, …). In the case of autonomous vehicles, they can contribute to elaborate intelligent monitors that can take the environmental context into account. We show in this paper some main abilities of BN that can help in the elaboration of fault detection isolation and recovery (FDIR) modules. One of the main difficulty with the BN model is generally to elaborate these ones according to the case of study. Then, we propose some automatic generation techniques from failure mode and effects analysis (FMEA)-like tables using the pattern design approach. Once defined, these modules have to operate online for autonomous vehicles. In a second part, we propose a design methodology to embed the real-time and non-intrusive implementations of the BN modules using FPGA-SoC support. We show that the FPGA implementation can offer an interesting speed-up with very limited energy cost. Lastly, we show how these BN modules can be incorporated into the decision-making model for the mission planning of unmanned aerial vehicles (UAVs). We illustrate the integration by means of two models: the Decision Network model that is a straightforward extension of the BN model, and the BFM model that is an extension of the Markov Decision Process (MDP) decision-making model incorporating a BN. We illustrate the different proposals with realistic examples and show that the hybrid implementation on FPGA-SoC can offer some benefits.

In Orbit Demonstration of a FDIR Algorithm for the Attitude Control System of Micro Interplanetary Spacecraft PROCYON

A Fault Detection, Isolation, and Recovery (FDIR) algorithm for attitude control systems is a key technology to increasing the reliability and survivability of spacecraft. Micro/nano interplanetary spacecraft, which are rapidly evolving in recent years, also require robust FDIR algorithms. However, the implementation of FDIR algorithms to these micro/nano spacecraft is difficult because of the limitations of their resources (power, mass, cost, and so on). This paper shows a strategy of how to construct a FDIR algorithm in the limited resources, taking examples from micro deep space probe PROCYON. The strategy focuses on function redundancies and multi-layer FDIR. These ideas are integrated to suit the situation of micro/nano interplanetary spacecraft and demonstrated in orbit by the PROCYON mission. The in-orbit results are discussed in detail to emphasize the effectiveness of the FDIR algorithm.

Fault-tolerant Optimal Control Scheme for Satellite Micro-launchers

Abstract An integration of Fault Detection, Isolation and Recovery (FDIR) with the Linear Quadratic Gaussian (LQG) technique is presented, which achieves fault tolerance while maintaining control optimality. The FDIR scheme is tested on a space micro-launcher model, and simulation results show successful accommodation of both sensor and actuator faults.

Document-Based Programming System for Seamless Linking of Satellite Onboard Software and Ground Operating System

In satellites, onboard software is required to perform complicated mission sequences and autonomous scheduling, conduct preliminary data processing, and manage various onboard devices. The dependability of onboard software strongly affects the reliability of a satellite itself. Therefore, the onboard software must be both complex and reliable to perform complicated small satellite missions. We propose an automatic software generator to meet these requirements. This generates onboard software and a database for the ground operating system using satellite development documents, such as command and telemetry definition documents and fault detection, isolation, and recovery (FDIR) definition documents. By using this software generator, the software development load can be reduced and human error can be avoided, even if the definitions are modified in an ad hoc manner during the development process. The generator additionally enables the easy accommodation of user preferences and software depth variation during a mission.

Nanosatellites in LEO and beyond: Advanced Radiation protection techniques for COTS-based spacecraft

This paper presents an approach for implementing radiation protection FDIR (Fault Detection, Isolation and Recovery) techniques designed especially for nanosatellites, capable of ensuring reliable operation in harsh orbits using COTS (Commercial off the Shelf) components. The radiation environment, as encountered by nanosatellites utilizing Flash-based FPGAs in orbits higher than Low Earth Orbit, is analyzed, primarily focusing on SEE (Single Event Effects). In order to assure reliable operation, the FDIR policy is split into two levels: the Low Level FDIR which ensures that no permanent damage occurs to the satellite's electronics, which then allows the use of a High Level FDIR tasked with maintaining high availability. A hierarchical approach, consisting of three types of current limiters in combination with watchdog timers and fault tolerant logic implemented inside a flash-based FPGA is proposed for the Low Level FDIR. The impacts of various radiation-induced faults are analyzed with respect to how the FDIR techniques mitigate them. The proposed current limiters and watchdog timers have been implemented and evaluated for suitability of use with the hierarchical FDIR policy. In order to decrease the impacts on the size and weight footprints, the current limiters were implemented as stacked 3D modules.

Status Self-Validation of Sensor Arrays Using Gray Forecasting Model and Bootstrap Method

The reliability monitoring of sensor arrays is a challenging and critical issue that directly influences the performance of a measurement and control system. In this paper, a novel strategy based on gray forecasting model GM(1,1) coupled with the bootstrap method is proposed for status self-validation of sensor arrays. The proposed strategy focuses on fault detection, isolation, and recovery (FDIR), data validation and dynamic measurement uncertainty estimation of the sensor arrays. The FDIR scheme can effectively detect and isolate sensor abrupt faults and simultaneously accomplish fault recovery with high accuracy and good timeliness. Furthermore, the proposed FDIR scheme has the advantage of discriminating between fault-free signals with sudden changes and undoubted abrupt faults through the trust mechanism. The model GM(1,1) is updated continuously by a metabolism method to improve the adaptivity of the strategy for reliability monitoring. After signal reconstruction, the data validation and dynamic measurement uncertainty can be evaluated by the bootstrap method without any prior information about measurands. A real metal-oxide gas sensor array experimental system is designed to verify the excellent performance of the proposed strategy. The experimental results demonstrate that the proposed approach is capable of conducting the status self-validation of sensor arrays effectively and improving the reliability of sensor arrays in engineering applications.

Status self-validation of a multifunctional sensor using a multivariate relevance vector machine and predictive filters

A novel strategy by using a multivariable relevance vector machine coupled with predictive filters for status self-validation of a multifunctional sensor is proposed. The working principle and online updating algorithm of predictive filters are emphasized for multiple fault detection, isolation and recovery (FDIR), and the incorrect sensor measurements are validated online. The multivariable relevance vector machine is then employed for the signal reconstruction of the multifunctional sensor to generate the final validated measurement values (VMV) of multiple measured components, in which its advantages of sparse models and multivariable simultaneous outputs are fully used. With all likely uncertainty sources of the multifunctional self-validating sensor taken into account, the uncertainty propagation model is deduced in detail to evaluate the online validated uncertainty (VU) under a fault-free situation while a qualitative uncertainty component is appended to indicate the accuracy changes of VMV under different types of fault. A real experimental system of a multifunctional self-validating sensor is designed to verify the performance of the proposed strategy. From the real-time capacity and fault recovery accuracy of FDIR, and runtime of signal reconstruction under small samples, a performance comparison among different methods is made. Results demonstrate that the proposed scheme provides a better solution to the status self-validation of a multifunctional self-validating sensor under both normal and abnormal situations.

Smart architecture for highly available, robust and autonomous satellite

AbstractEuropean leader for satellite systems and at the forefront of orbital infrastructures, Thales Alenia Space is a joint venture between Thales (67%) and Finmeccanica (33%) and forms with Telespazio a Space Alliance. Thales Alenia Space is a worldwide reference in telecoms, radar and optical Earth observation, defense and security, navigation and science. Thales Alenia Space has 11 industrial sites in 4 European countries (France, Italy, Spain and Belgium) with over 7,200 employees worldwide.Satellite evolution and wish to design more autonomous mission imply an enhancement of satellite architecture to enable the smart control of spacecraft. New system architecture needs to be defined to permit the decision-taking, and special attention has to be paid to FDIR (Fault Detection, Isolation and Recovery) and the way action can be engaged. Nevertheless the constraints on architecture and related technique composing it, stay still invariant: robustness, high availability, industrially viable and cost efficient. This paper gives first some elements about a decisional architecture defined in joined work by Thales Alenia Space and CNES, then the current context of FDIR is briefly described and then a new FIDR strategy permitting smart decision is introduced, finally the way decision can be engaged on-board for the next generation of autonomous satellites is presented.

Mixing Diagnosis Techniques for Autonomous Satellite FDIR

Thales Alenia Space is developing a new generation of satellites dedicated to flexible and autonomous Space missions. It relies on a hierarchical component based architecture. Hierarchical layers are devoted to manage on one hand, different kind of knowledge from abstract (goal of the mission) to real (units, equipments), and on the other hand, different reaction times, from orbit's order of magnitude (planning activities) to real time (thrusting, sensing). Components are most of the time related to functional chains covering pure discrete behavior (battery management) to pure continuous one (Guidance, Navigation and Control), or mix of both leading to hybrid modeling. Due to the vast range of models (discrete, continuous, hybrid – real time, no time constraint – teleological, functional, structural, behavioral), defining a FDIR (Fault Detection, Isolation and Recovery) strategy is a challenge. Solutions have been defined by making collaborate different diagnosis techniques (analytical redundancy, consistency based approach, model checking and Hinf estimator) from several origins, locally in each component. Abstraction of the local diagnosis results is translated at satellite level under the form of events and managed by a discrete automaton based formalism. Focus of the paper is put on the knowledge's modeling from the different layers of the architecture, the diagnosis technique in relation with the different kinds of components and the description of the hierarchical diagnosis solution.

Parameter Estimation-Based Fault Detection, Isolation and Recovery for Nonlinear Satellite Models

This brief is concerned with the problem of nonlinear fault detection, isolation, and recovery (FDIR) for the satellite's orbital and attitude models through construction of residual generators that are based on least-squares parameter estimation techniques. By viewing system anomalies caused by faults and/or malfunctions as changes of certain parameters in the system, our goal is to detect, isolate, and recover from faults through estimating these parameters and adaptively redesigning and reconfiguring the controllers. The convergence and robustness properties of the residual generators are analytically and experimentally investigated. Furthermore, the corresponding decision logic and thresholds for fault diagnosis are properly selected and specified. Numerical simulation results for the proposed technique as applied to nonlinear satellite models are presented to demonstrate its performance capabilities.

Celanese signs catalyst research agreement

European leader for satellite systems and at the forefront of orbital infrastructures, Thales Alenia Space is a joint venture between Thales (67%) and Finmeccanica (33%) and forms with Telespazio a Space Alliance. Thales Alenia Space is a worldwide reference in telecoms, radar and optical Earth observation, defense and security, navigation and science. Thales Alenia Space has 11 industrial sites in 4 European countries (France, Italy, Spain and Belgium) with over 7,200 employees worldwide.Satellite evolution and wish to design more autonomous mission imply an enhancement of satellite architecture to enable the smart control of spacecraft. New system architecture needs to be defined to permit the decision-taking, and special attention has to be paid to FDIR (Fault Detection, Isolation and Recovery) and the way action can be engaged. Nevertheless the constraints on architecture and related technique composing it, stay still invariant: robustness, high availability, industrially viable and cost efficient. This paper gives first some elements about a decisional architecture defined in joined work by Thales Alenia Space and CNES, then the current context of FDIR is briefly described and then a new FIDR strategy permitting smart decision is introduced, finally the way decision can be engaged on-board for the next generation of autonomous satellites is presented.