Richard Stottler, Stottler Henke Associates Inc.; Abhimanyu Singhal, Stottler Henke Associates Inc.; Christopher Healy, Stottler Henke Associates Inc.; Sowmya Ramachandran, Stottler Henke Associates Inc.; Kerry Quinn, Astrobotic; Joseph Palmieri, Astrobotic; Shawn Logan, Astrobotic
Keywords: anomaly detection, fault management, space situational awareness, machine learning, model-based reasoning, artificial intelligence
Abstract:
Bottom line up front (BLUF): our MBR and ML techniques correctly identified critical spacecraft subsystem anomalies in milliseconds in a wide variety of scenarios, while avoiding false alarms. An important component of Space Situational Awareness (SSA) / Space Domain Awareness (SDA) is knowledge of the true status of friendly assets and whether any assets are under attack. Therefore, it is important to be able to detect faults and other anomalies, and determine the components involved and the root cause as well as whether that root cause is likely an external attack. During space conflict, communications to satellites may be disrupted, requiring them to intelligently and autonomously “take care of themselves,” i.e., effectively detect faults, diagnose their root causes, and develop and execute recovery plans, autonomously, without necessarily being able to communicate with ground controllers. This lack of communication is analogous to lunar rovers and power systems where communication can be disrupted by terrain and other factors or take too long for some catastrophic anomalies. Astrobotic, for NASA, is developing a rover, Vertical Solar Array Technology (VSAT), that traverses over the lunar surface to an advantageous position, then unfurls its 60’ high Roll Out Solar Array (ROSA) photovoltaic mast to provide power for other lunar systems. Given the height of the deployed/deploying ROSA, the rover is very unstable and must adhere to very strict leveling requirements to avoid tipping over, with catastrophic loss of mission, even just a few degrees of lean would be disastrous. Prior to ROSA deployment a gimbal levels the base of the ROSA and then locks. As the solar array is unfurled inertial measurement units (IMUs) continuously monitor the array’s movement, including any lean, force sensors monitor the force on each of 4 wheels, and several side-facing and upward facing cameras observe the events. A problem during or after ROSA deployment may be very dynamic, denying ground controllers enough time to correct any problem, given the round trip communication delays. It is therefore important that the VSAT be equipped with the means to quickly detect problems, perform diagnosis and root cause determination, and quickly safe the system. Traditionally, Fault Detection, Isolation, and Recovery (FDIR) systems have utilized Model Based Reasoning (MBR), which requires knowledge of the subsystem design and the behavior of components down to the desired level of diagnosis. To the degree this information is readily available, it is important to make good use of it. However, the field of machine learning (ML) has shown that systems can also learn, offline, the normal behavior of complex systems in many different environments and states, and then detect abnormal behavior in real time. These systems can also be trained with known abnormal states, and recognize these more specifically when they occur. With the new types of VSAT subsystems (such as mechanical components and related sensors) came new challenges to be overcome. Some concerns included quick reaction times needed to avoid tipping or buckling during mast deployment and, at the opposite end of the spectrum, detecting very gradual changes, hard to discern in sensor noise (the mast moves very, very slowly while tracking the Sun). In some cases, data is severely limited, reducing the applicability of a pure ML approach. In our previous work, we outlined our modular approach to fault detection and diagnosis utilizing MBR and ML as well as a third independent method called the Thermodynamic Reasoning and Intelligent Anomaly Detection (TRIAD) system. Similarly to how aggregate variables such as thermodynamic variables such as pressure and temperature can summarize microstate variables (e.g. the speeds of individual molecules), TRIAD utilizes aggregate quantities such as mean, minimum, maximum, and Fourier Transforms to characterize anomalies. We also described how this hybridization enables additional confidence in diagnosis, as the advantages of each approach are emphasized while the disadvantages are mitigated, and summarized how we planned to apply these methods on the VSAT platform and subsystems. This paper first quickly reviews the concepts then describes progress on this work since our last paper, presented at AMOS 2023. This includes validation of the hybrid approach to fault detection, diagnosis, and recovery via a physical simulation of the VSAT Copyright © 2024 Advanced Maui Optical and Space Surveillance Technologies Conference (AMOS) – www.amostech.com platform as well as results from multiple fault detection modules. We enumerate many relevant scenarios, developed in conjunction with Astrobotic to best capture realistic, critical faults, as well as metrics from our approach. We show that the previously discussed methods are capable of both detecting and characterizing mechanical anomalies from simulated VSAT telemetry data within tens of milliseconds of the faults occurring, well below the allotted “reaction time” of 100 milliseconds. The paper will present quantitative results for a large range of fault scenarios, including soil collapse, soil slippage, ROSA levelling errors, and a wide variety of sensor faults. Both MBR and TRIAD were effective at detection and diagnosis and, as mentioned in our previous work, we identified several areas where hybridization of both techniques provides a significant advantage over the use of just one or the other. We will also present data gathered during slope testing of the actual ground VSAT physical prototype with some fore-shadowing of what we plan to present next year, based on testing with this (and more) actual data from the actual hardware prototype. We conclude with a discussion on the direction this work will take in the future. Based on these results, Astrobotic plans to include MAIFLOWER on the actual lunar VSAT, with integration beginning this Fall
Date of Conference: September 17-20, 2024
Track: Space-Based Assets