Physics-Informed Detection of Friction Anomalies in Satellite Reaction Wheels

Abstract

As the number of satellites in orbit has increased exponentially in recent years, ensuring their correct functionality has started to require automated methods to decrease human workload. In this work, we present an algorithm that analyzes the on-board data related to friction from the Reaction Wheel Assemblies (RWA) of a satellite and determines their operating status, distinguishing between nominal status and several possible anomalies that require preventive measures to be taken. The algorithm first uses a model based on hybrid systems theory to extract the information relevant to the problem. The extraction process combines techniques in changepoint detection, dynamic programming, and maximum likelihood in a structured way. A classifier then uses the extracted information to determine the status of the RWA. This last classifier has been previously trained with a labelled dataset produced by a high-fidelity simulator, comprised for the most part of nominal data. The final algorithm combines model-based and data-based approaches to obtain satisfactory results with an accuracy around 95%.

Figures (13)

• Figure 1: Cross-section of an RWA, adapted from pantaleoni_curing_2014.
• Figure 2: Examples of runs labeled with different anomalies. For each case, the anomalous run is shown in red while a nominal run is plotted in blue to help compare them.
• Figure 3: Evolution of the state and friction of an FSS. The left nodes show the three configurations and the transitions between them. The right side shows the friction distributions of each configuration, together with a possible trajectory of the friction value generated by the FSS as it jumps between configurations.
• Figure 4: Visualization of the two friction systems described in Examples 1 (FSS 1) and 2 (FSS 2). The first two plots show the evolution of the state variables of the system. The third plot shows the resulting total dry friction as in \ref{['eq:model_dry-friction']}, and the fourth the total friction after adding a viscous component and noise, as in \ref{['eq:model_friction']}.
• Figure 5: Structure of the proposed anomaly detection algorithm. It consists of 4 stages. The changepoint detection determines the location of the sudden changes of friction $k_z^\mathrm{jump}$. The friction estimation estimates the friction coefficients between changepoints. The interval assignment assigns the changepoints to the different FSS in the satellite. Finally, the friction classification uses the friction values associated to the FSS to determine the anomalies present in the RWA.

Theorems & Definitions (4)

• Example 1: Short-term friction changes
• Example 2: Long-term friction changes
• Remark 1
• Remark 2