Neural Network-based Fault Diagnosis of Satellites Formation Flight

The main objective of this thesis is to develop a methodology for detecting and isolating faults (i.e. fault diagnosis) in any of multiple reaction wheels that are commonly employed as actuators in a consensus-based virtual structure controlled formation of satellites. In order to accomplish this objective, a two-level fault diagnosis system is developed based on Dynamic Neural Networks (DNNs). In the lower-level of the formation flight system hierarchy, a local fault diagnosis module is available in each individual satellite. In this level, the fault diagnosis system may consist of a dynamic neural network that is trained by using absolute measurements and states of each single satellite. Unfortunately, a local fault diagnosis system may fail to detect the presence of low severity faults. In an individual satellite these low severity faults may not cause any serious complications with the specifications of the overall mission, however they can cause significant impact on the satellite’s attitude or rates in a given precision formation flight of a network of satellites. Consequently, in order to detect these low severity faults a fault detection system is required to be designed and developed at the higher-level or the formation-level of the mission hierarchy. Towards this end, the highly nonlinear dynamics of the formation flight and the reaction wheels are modeled by using dynamic multilayer perceptron neural networks. The proposed formation-level DNNs invoke the extended back propagation learning algorithm and are trained based on sets of input/output data that are collected from the relative attitude determination sensors of the 3-axis attitude control subsystems of the satellites. The DNN parameters are adjusted to minimize certain performance indices (representing the output estimation errors). The capabilities of the proposed DNNs are investigated under various faulty situations, including single and multiple actuator fault scenarios and under high severity and low severity faulty situations. Using a Confusion Matrix evaluation method, it is demonstrated that by using the proposed fault detection and isolation (FDI) scheme, one can achieve a high level of accuracy and precision in detecting faults. The proposed formation-level FDI system has capabilities in efficiently detecting and isolating actuator low severity faults simultaneously

Unattended network operations technology assessment study. Technical support for defining advanced satellite systems concepts

The results are summarized of an unattended network operations technology assessment study for the Space Exploration Initiative (SEI). The scope of the work included: (1) identified possible enhancements due to the proposed Mars communications network; (2) identified network operations on Mars; (3) performed a technology assessment of possible supporting technologies based on current and future approaches to network operations; and (4) developed a plan for the testing and development of these technologies. The most important results obtained are as follows: (1) addition of a third Mars Relay Satellite (MRS) and MRS cross link capabilities will enhance the network's fault tolerance capabilities through improved connectivity; (2) network functions can be divided into the six basic ISO network functional groups; (3) distributed artificial intelligence technologies will augment more traditional network management technologies to form the technological infrastructure of a virtually unattended network; and (4) a great effort is required to bring the current network technology levels for manned space communications up to the level needed for an automated fault tolerance Mars communications network

A Decoupled Parameters Estimators for in Nonlinear Systems Fault diagnosis by ANFIS

This paper presents a new and efficient Adaptive Neural Fuzzy Inference Systems approach for satellite’s attitude control systems (ACSs) fault diagnosis. The proposed approach formulates the fault modelling problem of system component into an on-line parameters estimation The learning  ability of the adaptive neural fuzzy inference system allow as to decoupling the effect of each fault from the estimation of the others.  Our solution provides a method to detect, isolate, and estimate various faults in system components, using Adaptive Fuzzy Inference Systems Parameter Estimators (ANFISPEs) that are designed and based on parameterizations related to each class of fault. Each ANFISPE estimates the corresponding unknown Fault Parameter (FP) that is further used for fault detection, isolation and identification purposes. Simulation results reveal the effectiveness of the developed FDI scheme of an ACSs actuators of a 3-axis stabilized satellite.DOI:http://dx.doi.org/10.11591/ijece.v2i2.22

Aircraft Cabin Noise Minimization Via Neural Network Inverse Model

This paper describes research to investigate an artificial neural network (ANN) approach to minimize aircraft cabin noise in flight. The ANN approach is shown to be able to accurately model the non-linear relationships between engine unbalance, airframe vibration, and cabin noise to overcome limitations associated with traditional linear influence coefficient methods. ANN system inverse models are developed using engine test-stand vibration data and on-airplane vibration and noise data supplemented with influence coefficient empirical data. The inverse models are able to determine balance solutions that satisfy cabin noise specifications. The accuracy of the ANN model with respect to the real system is determined by the quantity and quality of test stand and operational aircraft data. This data-driven approach is particularly appealing for implementation on future systems that include continuous monitoring processes able to capture data while in operation

Analysis of Artificial Intelligence based diagnostic methods for satellites

The growing utilization of small satellites in various applications has emphasized the need for reliable diagnostic methods to ensure their optimal performance and longevity. This master thesis focuses on the analysis of artificial intelligence-based diagnostic methods for these particular space assets. This work firstly explores the main characteristics and applications of small satellites, highlighting the critical subsystems and components that play a vital role in their proper functioning. The key components of this study revolve around Diagnosis, Prognosis, and Health Monitoring (DPHM) systems and techniques for small satellites. The DPHM systems aim at monitoring the health status of the satellite, detecting anomalies and predicting future system behavior. The reason why advanced DPHM systems are of interest for the space operators is the fact that they mitigate the risk of satellites catastrophic failures that may lead to service interruptions or mission abort. To achieve these objectives, a hybrid architecture combining Convolutional Neural Networks (CNN) and Long Short-Term Memory (LSTM) networks is proposed. This architecture leverages the strengths of CNNs in feature extraction and LSTM networks in capturing temporal dependencies. The integration of these two neural network architectures enhances the diagnostic capabilities and enables accurate predictions for small satellite systems. Real data collected from an operational satellite is utilized to validate and test the proposed CNN-LSTM hybrid architecture. Based on the experimental results obtained, advantages and drawbacks of the exploitation of this architecture are discussed.The growing utilization of small satellites in various applications has emphasized the need for reliable diagnostic methods to ensure their optimal performance and longevity. This master thesis focuses on the analysis of artificial intelligence-based diagnostic methods for these particular space assets. This work firstly explores the main characteristics and applications of small satellites, highlighting the critical subsystems and components that play a vital role in their proper functioning. The key components of this study revolve around Diagnosis, Prognosis, and Health Monitoring (DPHM) systems and techniques for small satellites. The DPHM systems aim at monitoring the health status of the satellite, detecting anomalies and predicting future system behavior. The reason why advanced DPHM systems are of interest for the space operators is the fact that they mitigate the risk of satellites catastrophic failures that may lead to service interruptions or mission abort. To achieve these objectives, a hybrid architecture combining Convolutional Neural Networks (CNN) and Long Short-Term Memory (LSTM) networks is proposed. This architecture leverages the strengths of CNNs in feature extraction and LSTM networks in capturing temporal dependencies. The integration of these two neural network architectures enhances the diagnostic capabilities and enables accurate predictions for small satellite systems. Real data collected from an operational satellite is utilized to validate and test the proposed CNN-LSTM hybrid architecture. Based on the experimental results obtained, advantages and drawbacks of the exploitation of this architecture are discussed

A Data-driven Fault Isolation and Identification Scheme for Multiple In-Phase Faults in Satellite Control Moment Gyros

A satellite can only complete its mission successfully when all its subsystems, including the attitude control subsystem, are in healthy condition and work properly. Control moment gyroscope is a type of actuator used in the attitude control subsystems of satellites. Any fault in the control moment gyroscope can cause the satellite mission failure if it is not detected, isolated and resolved in-time. Fault isolation provides an opportunity to detect and isolate the occurring faults and, if accompanied by proactive remedial actions, can avoid failure and improve the satellite reliability. It is also necessary to know the fault severity for better maintenance planning and prioritize the corrective actions. This way, the more severe faults can be corrected first. In this work, an enhanced data-driven fault diagnosis scheme is introduced for fault isolation and identification of multiple in-phase faults of satellite control moment gyroscopes that is not addressed in the literature before with high accuracy. The proposed method is based on an optimized support vector machine and an optimized support vector regressor. The results yield fault predictions with up to 95.6% accuracy for isolation and 94.9% accuracy for identification, on average. In addition, a sensitivity analysis with regards to noise, missing values, and missing sensors is done where the results show that the proposed model is robust enough to be used in real applications

Health Management and Adaptive Control of Distributed Spacecraft Systems

As the development of challenging missions like on-orbit construction and collaborative inspection that involve multi-spacecraft systems increases, the requirements needed to improve post-failure safety to maintain the mission performance also increases, especially when operating under uncertain conditions. In particular, space missions that involve Distributed Spacecraft Systems (e.g, inspection, repairing, assembling, or deployment of space assets) are susceptible to failures and threats that are detrimental to the overall mission performance. This research applies a distributed Health Management System that uses a bio-inspired mechanism based on the Artificial Immune System coupled with a Support Vector Machine to obtain an optimized health monitoring system capable of detecting nominal and off-nominal system conditions. A simulation environment is developed for a fleet of spacecraft performing a low-Earth orbit inspection within close proximity of a target space asset, where the spacecraft observers follow stable relative orbits with respect to the target asset, allowing dynamics to be expressed using the Clohessy-Wiltshire-Hill equations. Additionally, based on desired points of inspection, the observers have specific attitude requirements that are achieved using Reaction Wheels as the control moment device. An adaptive control based on Deep Reinforcement Learning using an Actor-Critic-Adverse architecture is implemented to achieve high levels of mission protection, especially under disturbances that might lead to performance degradation. Numerical simulations to evaluate the capabilities of the health management architecture when the spacecraft network is subjected to failures are performed. A comparison of different attitude controllers such as Nonlinear Dynamic Inversion and Pole Placement against Deep Reinforcement Learning based controller is presented. The Dynamic Inversion controller showed better tracking performance but large control effort, while the Deep Reinforcement controller showed satisfactory tracking performance with minimal control effort. Numerical simulations successfully demonstrated the potential of both the bioinspired Health Monitoring System architecture and the controller, to detect and identify failures and overcome bounded disturbances, respectively