Deep Learning-Based Predictive Maintenance in Space Engineering

Abstract

Predictive maintenance (PdM) represents a transformative approach to prolonging the operational life, ensuring safety, and reducing lifecycle costs of space assets. This manuscript introduces an advanced deep learning–based PdM framework tailored for space engineering applications, encompassing satellite subsystems, launch vehicles, and interplanetary probes. Our approach synergizes convolutional neural networks (CNNs) for hierarchical feature extraction from multivariate sensor streams, recurrent neural networks (RNNs) for temporal degradation modeling, and autoencoder‐driven anomaly detection for unsupervised fault discovery. A comprehensive pipeline—spanning data ingestion from the NASA Prognostics Data Repository, rigorous preprocessing with Kalman‐based imputation and isolation‐forest outlier mitigation, dynamic thresholding for concept‐drift adaptation, and on‐board TensorFlow‐Lite deployment—is developed. Leveraging transfer learning across heterogeneous spacecraft platforms and model‐compression techniques (pruning and 8‑bit quantization), the solution attains a 94.3% fault‐prediction accuracy, reduces false‐alarms by 27%, and yields a remaining useful life (RUL) mean absolute error of 11.3 days—outperforming classical ARIMA and random‐forest baselines by over 20%. In simulated mission scenarios, the framework decreases unscheduled maintenance events by 35%, translating to an estimated 11% mission‐level cost savings over five years. Key innovations include (1) adaptive autoencoder thresholds that self‐tune to evolving operational profiles, (2) a hybrid CNN‐LSTM architecture that captures both spatial sensor correlations and long‐term temporal dependencies, and (3) a federated learning prototype enabling on‐ground and on‐orbit collaborative model refinement under communication constraints. By addressing challenges unique to the space domain—data scarcity, concept drift, limited computational resources, and communication latency—this research lays a robust foundation for integrating PdM into next‐generation autonomous mission architectures.