Online Fault Diagnosis, Prognosis, and Health Monitoring of Small Satellites

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The topic of the research being proposed is "Online Fault Diagnosis, Prognosis, and Health Monitoring of Space Systems." It should be noted that the focus of the program will be on small satellite constellation health monitoring with two major objectives, (1) development of the high-fidelity configurable dynamics model for small satellites in constellation and (2) developing health monitoring algorithms for the satellite constellation using the developed models in objective 1. For the first objective, development of the high-fidelity configurable dynamics model for small satellites in constellation, high-fidelity models for a monolithic satellite with reaction wheels (RW) and control moment gyros (CMG) with 3-axis stabilized via nonlinear sliding mode controller (SMC) are developed by the PI. These models only incorporate a satellite attitude control system (ACS) while in constellation and formation flying orbital control also needs to be incorporated for station keeping. Hence, the model needs to be expanded to include attitude and orbital control system (AOCS). Available models from individual satellites will be extended to multiple satellites. This task comprises of 2 parts: (a) developing dynamics models for the satellites in the formation (b) developing controllers for the formation assembly. The constellation assembly can be achieved in three main topologies (a) Centralized, (b) Hierarchical, (c) Distributed/Decentralized and the final model needs to be able to switch between these three topologies. For the second objective, fault diagnosis and prognosis of space systems, new approaches shall be investigated. Hybrid approaches, combining physics-based and data-driven methods, shall be explored. In the physics-based approach, state/parameter estimation of the non-measurable system parameters shall be used to minimize delay in detection. In the data-driven approach, simulated data, in addition to simulated data from models developed in objective 1, shall be used along with machine learning algorithms to model the system, its performance, and possible failure modes. Furthermore, fault prognosis algorithms shall be developed to estimate remaining useful life of the unit(s) and suggest potential remedial actions. This work will provide Canadian space industries with high-fidelity scalable and configurable models for small satellite constellation assemblies that would help increase the Canadian market share in the small satellite launch sector from 2.2% as it stands today. This yields new opportunities for space discovery compared to monolithic satellite missions to due initial and recurring costs. Furthermore, this program will provide intelligent diagnosis and prognosis technologies and trained personnel (HQP) that enable the industry to increase the system's availability and reduce maintenance costs by millions of dollars. The success of this program offers favorable trade balance to exports, presenting Canada as a leader in this area.