Abstract Prognostics and Health Management (PHM), including monitoring, diagnosis,
prognosis, and health management, occupies an increasingly important position in reducing …

Performances are a key concern in aerospace vehicles, requiring safer structures with as
little consumption as possible. Composite materials replaced aluminum alloys even in …

Detecting anomalous subsequences in time series data is an important task in areas
ranging from manufacturing processes over finance applications to health care monitoring …

Deep learning applications have been thriving over the last decade in many different
domains, including computer vision and natural language understanding. The drivers for the …

Fault detection is a critical step for machine condition monitoring and maintenance. With
advances in machine learning technologies, automated faulty condition identification can be …

With the introduction of damage tolerance-based design philosophies, the demand for
reliable and robust structural health monitoring (SHM) procedures for aerospace composite …

Deep learning based on neural networks has been widely used in image recognition,
speech recognition, natural language processing, automatic driving, and other fields and …

Extensive research has been conducted on fault diagnosis of planetary gearboxes using
vibration signals and deep learning (DL) approaches. However, DL-based methods are …

The increasing demand for advanced fault detection in manufacturing processes has
encouraged the application of industrial intelligence based on deep learning. However …

Abstract Machine learning (ML) methods for the structural health monitoring (SHM) of
composite structures rely on sufficient domain knowledge as they typically demand to extract …