Augmenting Aircraft Engine Flight Data with Generative Adversarial Networks for Fault Detection

Recent advancements in deep learning for aircraft engine fault detection have been predominantly focused on research using simulated datasets. Despite significant progress, the gap between simulated and real-world data underscores a pressing need for models that are more applicable and adaptable to the aerospace industry. This discrepancy stems...

A novel machine learning model to design historical-independent health indicators for composite structures

Developing comprehensive health indicators (HIs) for composite structures encompassing various damage types is challenging due to the stochastic nature of damage accumulation and uncertain events (like impact) during operation. This complexity is amplified when striving for HIs independent of historical data. This paper introduces an AI...

In Situ  Failure Detection of Electronic Control Units Using Piezoresistive Stress Sensor

Recent advancements in automotive technologies, most notably autonomous driving, demand electronic systems much more complex than those realized in the past. The automotive industry has been forced to adopt advanced consumer electronics to satisfy the demand, and thus it becomes more challenging to assess system reliability while adopting the...

Prognostics and health monitoring of electronic system: A review

Miniaturization of electronics, reduction of time to market and new functionalities in the current context of autonomous driving, electrification and connectivity, are bringing new reliability challenges. Prognostics and Health Management (PHM) can be used effectively to address some of the key challenges, in particular new challenges...