Spacecraft Health Monitoring Research Articles

Design, fabrication, and hypervelocity impact testing of screen-printed flexible micrometeoroid and orbital debris impact sensors for long-duration spacecraft health monitoring

Micrometeoroid and orbital debris (MMOD) are a key risk for spacecraft damage that could compromise missions. Detecting and evaluating MMOD damage is therefore a crucial component in the health monitoring of spacecraft, especially for long duration, deep space expeditions. In this work, we developed a passive sensor system fabricated from conductive metal ink screen-printed on flexible Kapton, using roll-to-roll manufacturing suitable for low-cost fabrication of large areas of sensors, as a MMOD sensor for a spacecraft shield. The sensor is integrated into a low density, two-wall Whipple shield comprising of thin aluminum sheets sandwiching a polyimide foam. The shield with the sensors were tested with hypervelocity impacts at approximately 7 km/s using different particle diameters. Data collected from the sensors were successfully used to determine the impact size, impact location, and predict the impact energy of the damage.

Spectral Splitting Sensing Using Optical Fiber Bragg Grating for Spacecraft Lateral Stress Health Monitoring

Optical fiber sensing is a promising detection method for spacecraft health monitoring, since optical fiber sensors are lightweight, small in size, easy to integrate and immune to electromagnetic interference. As a significant optical sensor, fiber Bragg gratings (FBG) are widely used for force sensing because of their axial strain characteristics. However, it is necessary to detect not only one-dimensional strain but also plane strain and its deformation in order to comprehensively evaluate the condition of the structure. Therefore, it is very important to analyze the reflection spectrum of FBG under lateral stress. When FBG are subjected to lateral stress, the refractive index of the waveguide in the x and y directions changes, resulting in a birefringence phenomenon. This result causes the reflection spectrum of FBG to split into two peaks. In this paper, a transverse stress detection method based on spectral split sensing for the fiber Bragg grating is proposed, intended for monitoring spacecraft–small particle collisions. The FBG local lateral stress detection system is designed and verified by experiments. The wavelength pressure correlation is established in the experiment by adjusting the number of weights to change the lateral pressure on the FBG. The loading range of FBG lateral pressure is 4.0–7.0 N, the step size is 0.5 N, and round-trip measurement is carried out four times. The wavelengths of the peak and split point of the FBG reflection spectrum are recorded. The experimental results show that FBG’s split point and right peak pressure sensitivities are 16.57 pm/N and 45.14 pm/N, respectively. The spectral splitting phenomenon can be applied in spacecraft structure health monitoring systems and has certain reference value for the simplification of sensor systems.

Modern Machine Learning Methods for Telemetry-Based Spacecraft Health Monitoring

Modern Machine Learning Methods for Telemetry-Based Spacecraft Health Monitoring

A deep auto-encoder satellite anomaly advance warning framework

PurposeThe purpose of this paper is to ensure the stable operation of satellites in orbit and to assist ground personnel in continuously monitoring the satellite telemetry data and finding anomalies in advance, which can improve the reliability of satellite operation and prevent catastrophic losses.Design/methodology/approachThis paper proposes a deep auto-encoder (DAE) satellite anomaly advance warning framework for satellite telemetry data. Firstly, this study performs grey correlation analysis, extracts important feature attributes to construct feature vectors and builds the variational auto-encoder with bidirectional long short-term memory generative adversarial network discriminator (VAE/BLGAN). Then, the Mahalanobis distance is used to measure the reconstruction score of input and output. According to the periodic characteristic of satellite operation, a dynamic threshold method based on periodic time window is proposed. Satellite health monitoring and advance warning are achieved using reconstruction scores and dynamic thresholds.FindingsExperiment results indicate DAE methods can probe that satellite telemetry data appear abnormal, trigger a warning before the anomaly occurring and thus allow enough time for troubleshooting. This paper further verifies that the proposed VAE/BLGAN model has stronger data learning ability than other two auto-encoder models and is sensitive to satellite monitoring data.Originality/valueThis paper provides a DAE framework to apply in the field of satellite health monitoring and anomaly advance warning. To the best of the authors’ knowledge, this is the first paper to combine DAE methods with satellite anomaly detection, which can promote the application of artificial intelligence in spacecraft health monitoring.

Anomaly detection in mixed telemetry data using a sparse representation and dictionary learning

Spacecraft health monitoring and failure prevention are major issues in space operations. In recent years, machine learning techniques have received an increasing interest in many fields and have been applied to housekeeping telemetry data via semi-supervised learning. The idea is to use past telemetry describing normal spacecraft behaviour in order to learn a reference model to which can be compared most recent data in order to detect potential anomalies. This paper introduces a new machine learning method for anomaly detection in telemetry time series based on a sparse representation and dictionary learning. The main advantage of the proposed method is the possibility to handle multivariate telemetry time series described by mixed continuous and discrete parameters, taking into account the potential correlations between these parameters. The proposed method is evaluated on a representative anomaly dataset obtained from real satellite telemetry with an available ground-truth and compared to state-of-the-art algorithms.

Impact Location on a Fan-Ring Shaped High-Stiffened Panel Using Adaptive Energy Compensation Threshold Filtering Method

The increase in the number of space debris is a serious threat to the safe operation of in-orbit spacecraft. The propagation law of the impact signal in the stiffened panel of the spacecraft’s sealed bulkhead is very complicated, and there is less research on the impact source location in the high-stiffened panel. In this paper, an adaptive energy compensation threshold filtering (AECTF) method based on acoustic emission is proposed, which can realize large-scale, fast and accurate locating of the impact source on the stiffened panel with less resource consumption. The influence law of the stiffeners on the lamb wave is analyzed by finite element simulation, and the Lamb wave energy factor curve is obtained. The correctness of the simulation is verified by the locating experiment on the impact point. The results show that the proposed AECTF method has better adaptability and can correctly locate the impact points in complicated locations. By selecting the appropriate frequency band to filter the signal, the locating accuracy and stability can be improved. When the frequency band is 100–200 kHz, the locating result is optimal, the average absolute error is 7.0 mm, the average relative error is 0.86%, and the error standard deviation is 3.5 mm. This study will generate fresh insight into the impact location technology of high-stiffened panel and provide a reference for the in-orbit spacecraft health monitoring system.

C Source Code Auto Review Tool for Secure Programming of Indian Spacecraft Ground Software Elements, GEOSCHEMACS

Secure software development involves practicing secure programming constructs and standards. To enable the software is developed with secure programming standards, secure static analysis play major role in finding out the hidden vulnerabilities in the source code. Secure static analysis tools yield significant reduction in runtime failures of the software. GEOSCHEMACS (Geostationary Earth Orbit SpaceCraft HEalth Monitoring, Analysis and Control Software) is Indian Spacecrafts primary ground software element and developed mostly based on C programming language. The aim of this paper was to discuss the development of software for auto reviewing of C source code. The paper highlights the necessity of a customized & proprietary tool in complying against CERT (Computer Emergency Response Team) C secure coding standard and enhancing the secure software development of GEOSCHEMACS. It brings out the design and test results of the tool.

Telemetry data processing flow model: a case study

Purpose – The purpose of this paper is to develop the model of telemetry data processing flow (TDPF) for TDPF development and the TDPF run-time infrastructure to improve the spacecraft health monitoring capability. Design/methodology/approach – This research tries to develop the TDPF by flow-based programming (FBP) method and the component-based telemetry data processing software. Findings – The result from the case study is positive, thus reflecting the appropriateness of the suggested method. Practical implications – Application of the proposed TDPF model and the component-based telemetry data processing software may result in improved development efficiency and less development costs. Originality/value – This paper provides an effective way to develop TDPF without recompiling the software. It greatly facilitates the TDPF development that hopefully will save the TDPF development cost.

Megha-Tropiques: mission planning, analysis, and operations

Climate in the tropics is primarily influenced by variations in energy and water budget exchanges in the land/ocean/atmosphere systems. The interaction of these systems with the general atmospheric circulation is still not fully understood – one of the reasons why weather forecasts and predictions of climatic events are less accurate. The Megha-Tropiques mission, an ISRO-CNES (Indian Space Research Organisation–Centre National d’Etudes Spatiales) collaborative programme, aims to study water cycle and energy exchanges in the tropics, and use the data in climate and meteorology models. Megha-Tropiques is part of the Global Precipitation Measurement Mission, which is an international network of satellites. Of the four payloads on board, MADRAS is jointly developed by ISRO and the French space agency, CNES; SAPHIR and SCARAB completely by CNES; and GPS-ROSA is obtained from TAS-I (Thales Alenia Space Italia, Milan, Italy). GPS-ROSA supplements the geophysical parameters of the other payloads for atmosphere modelling. Mission planning involved integrating and interrelating the efforts of space and ground systems in realizing the operational system that provides the satellite-based science data meeting the turnaround time (TAT). Pre-launch simulations involved usage of a software simulator as a tool for network tests, training of spacecraft operation personnel, and validating the spacecraft health-monitoring software. The required operations were performed to characterize the payloads as well as to calibrate the various attitude sensors of the spacecraft. This paper summarizes mission planning and analysis aiding spacecraft configuration, pre-launch mission simulations, launch and early-orbit operations, and the on-orbit operational guidelines for Megha-Tropiques.

Hierarchical Fault Diagnosis and Health Monitoring in Satellites Formation Flight

Current spacecraft health monitoring and fault-diagnosis practices involve around-the-clock limit-checking and trend analysis on large amount of telemetry data. They do not scale well for future multiplatform space missions due the size of the telemetry data and an increasing need to make the long-duration missions cost-effective by limiting the operations team personnel. The need for efficient utilization of telemetry data achieved by employing machine learning and reasoning algorithms has been pointed out in the literature for enhancing diagnostic performance and assisting the less-experienced personnel in performing monitoring and diagnosis tasks. In this paper, we develop a systematic and transparent fault-diagnosis methodology within a hierarchical fault-diagnosis framework for a satellites formation flight. We present our proposed hierarchical decomposition framework through a novel Bayesian network, whose structure is developed from the knowledge of component health-state dependencies. We have developed a methodology for specifying the network parameters that utilizes both node fault-diagnosis performance data and domain experts' beliefs. Our proposed model development procedure reduces the demand for expert's time in eliciting probabilities significantly. Our proposed approach provides the ground personnel with an ability to perform diagnostic reasoning across a number of subsystems and components coherently. Due to the unavailability of real formation flight data, we demonstrate the effectiveness of our proposed methodology by using synthetic data of a leader-follower formation flight architecture. Although our proposed approach is developed from the satellite fault-diagnosis perspective, it is generic and is targeted toward other types of cooperative fleet vehicle diagnosis problems.

Resourcesat-1 mission planning, analysis and operations—Outline of key components

Resourcesat-1 is a global mission which provides continuity to the earth resources monitoring missions earlier launched by India. Resourcesat-1 is designed to have an improvement in the payload camera characteristics providing multi-spectral imaging capability in high resolution. This paper provides an overview of mission planning and analysis aiding spacecraft configuration, pre-launch simulations and operations, early-orbit operations and the on-orbit operational guidelines for Resourcesat-1. Mission planning involved integrating and inter-relating the efforts of space and ground systems in realizing the operational system which provides the continuity of satellite-based remote sensing data to the user community. Mission analysis related to contiguous imaging requirement of LISS-4 payload, resulted in a spacecraft yaw steering during imaging operations. Pre-launch simulation involved development of a software spacecraft simulator to be used as a tool for network tests, training of spacecraft operation personnel and validating the spacecraft health monitoring software. The required operations that were performed on the spacecraft to characterize and establish the on-orbit configuration for the normalization of daily operations are addressed in the sections relating to early orbit operations and on-orbit operational guidelines.

Fibre optic sensor network for spacecraft health monitoring

Close meshed instrumentation or sensor networks applyingconventional sensors for temperature and strain monitoring may result inexcessive penalties in terms of weight constraints, reliability andsensitivity to environmental conditions, and complex interfaces. Thefibre-optic sensor network described in this paper is a multiplexed system offibre Bragg grating (FBG) strain and temperature sensors and was designed anddeveloped for a demanding space environment, but it can also be emphasized asa promising sensor technology with high potential for non-space applications.The FBG sensor network measures both strain and temperature at the measuringconditions of the structural core of the X-38 spacecraft, by means ofwavelength shifts due to tensile stress on a Bragg grating. Dependent on thefixation of the fibre, either isolated from or mechanically coupled to thestructure, local thermal or mechanical loads can be determined in thetemperature range from -40 to +190 °C, and in the strainrange from -0.1% to +0.3%. Short-term resolution and repeatabilityof the strain measurement amount to 5 µε and25 µε, respectively.The FBG sensor network is very suitable for structural health monitoring oflarge structures, i.e. to determine thermal and mechanical load profilesduring operation, to assess residual strength of structural elements orto detect irregular working conditions. In comparison to conventional sensorslike thermocouples and strain gauges, an FBG sensor network significantlyreduces the amount of required front end electronics (FEE) and harness.