A Neural Network Symbolic Approach to Structural Health Monitoring in Aerospace Applications

Ensuring the structural integrity of spacecraft and satellites is one of the most critical challenges for aerospace missions. Traditional visual inspection methods are often impractical in orbit, making it necessary to adopt advanced solutions based on distributed sensors and automated analysis algorithms. Among the main threats to the stability of space structures are impacts with orbital debris, extreme thermal oscillations, and material fatigue, all of which can compromise the proper functioning of satellites and their appendages, such as solar panels.

The integration of Artificial Intelligence into structural health monitoring represents a paradigm shift. The use of deep neural networks, particularly Long Short-Term Memory (LSTM) networks, enables the analysis of time-series data from accelerometers and other sensors, identifying anomalies that could indicate structural damage. The adopted model leverages an innovative approach, combining symbolic representation of time series with a recurrent neural network architecture. By compressing information through Symbolic Aggregate approXimation (SAX), data dimensionality is reduced, improving processing speed and enhancing the model’s ability to recognize recurring patterns. This method transforms complex time-series data into symbolic sequences, simplifying the classification process and making the algorithm more robust to data variations.

To test the system’s effectiveness, a simulation of a satellite with flexible solar panels equipped with acceleration sensors was conducted. The model was evaluated under multiple damage scenarios, replicating realistic conditions of impacts with space debris. The results showed that using symbolic representation enhances the accuracy of damage classification, achieving an almost 100% precision and significantly reducing false positives and negatives. The ability of an AI model to autonomously and reliably detect structural damage without human intervention is a crucial step forward for space missions. The combination of recurrent neural networks and dimensionality reduction techniques opens new possibilities for onboard automatic monitoring, essential for ensuring the safety and longevity of space infrastructures.

The adoption of AI in the aerospace sector is transforming the way space systems are monitored and managed. The ability to implement autonomous solutions based on deep learning and symbolic representation reduces computational load while improving diagnostic reliability. With advancements in onboard computing technologies, these solutions could become standard for satellite and space module monitoring, paving the way for a new era of autonomous and intelligent space exploration.