Anomaly signals in the Attitude Determination and Control System (ADCS) of nanosatellites can significantly degrade mission performance, especially in the absence of robust fault detection, isolation, and recovery (FDIR) mechanisms. Thus, traditional threshold-based approaches, while portable and compact, may overlook subtle faults, whereby abnormal sensor signals or current spikes within the threshold may compromise the operation of the entire ADCS as a subsystem. Furthermore, the lack of interpretable detection methods further limits the development of reliable machine learning (ML) FDIR solutions. To address these limitations, this work presents a wavelet-based anomaly detection framework that introduces a two-stage hybrid architecture combining a lightweight Convolutional Neural Network (CNN) for fault detection with logistic regression for fault classification, both based on Discrete Wavelet Transform (DWT) detail coefficients extracted from sensor and actuator data. The framework was validated using a statistics-based anomaly dataset for a 1U CubeSat ADCS simulated in MATLAB, in which anomalies are introduced at the component level with controlled variations in magnitude, frequency, and waveform, ensuring 99% statistical significance. Additionally, to demonstrate operational feasibility, constraints for onboard implementation were considered by executing the proposed framework in a Processor-in-the-Loop (PIL) environment. For benchmarking, lightweight detection and classification algorithms were compared, including Out-Of-Limits (OOL) and compact machine learning approaches. Finally, to identify the framework’s limitations and trace faulty events to physical phenomena, Grad-CAM, SHAP, and impurity analysis were performed on the proposed algorithms as primary interpretability tools. Consequently, the results demonstrate accurate fault detection and identification to support both autonomous FDIR actions and ground operator decision-making. The proposed validation framework and dataset provide a reproducible basis for advancing anomaly detection onboard nanosatellites.

Anomaly signals in the Attitude Determination and Control System (ADCS) of nanosatellites can significantly degrade mission performance, especially in the absence of robust Fault Detection, Isolation, and Recovery (FDIR) mechanisms. Thus, traditional threshold-based approaches, while portable and compact, may overlook subtle faults, whereby abnormal sensor signals or current spikes within the threshold may compromise the operation of the entire ADCS as a subsystem. Furthermore, the lack of interpretable detection methods further limits the development of reliable machine learning (ML) FDIR solutions. To address these limitations, this work presents a wavelet-based anomaly detection framework that introduces a two-stage hybrid architecture combining a lightweight Convolutional Neural Network (CNN) for anomaly detection with logistic regression for fault classification, both based on discrete wavelet transform (DWT) detail coefficients extracted from sensor and actuator data. The framework was validated using a statistics-based anomaly dataset for a 1U CubeSat ADCS simulated in MATLAB, in which anomalies are introduced at the component level with controlled variations in magnitude, frequency, and waveform, ensuring 99% statistical significance. Additionally, to demonstrate operational feasibility, constraints for onboard implementation were considered by executing the proposed framework in a Processor-in-the-Loop (PIL) environment. For benchmarking, lightweight detection and classification algorithms were compared, including Out-Of-Limit (OOL) and compact machine learning approaches. Finally, to identify the framework’s limitations and trace faulty events to physical phenomena, Gradient-weighted Class Activation Mapping (Grad-CAM), SHapley Additive exPlanations (SHAP), and impurity analysis were performed on the proposed algorithms as primary interpretability tools. Consequently, the results demonstrate accurate anomaly detection and identification to support both autonomous FDIR actions and ground operator decision-making. The proposed validation framework and dataset provide a reproducible basis for advancing anomaly detection onboard nanosatellites.