Traditionally, system prognostics and health management (PHM) depends on sufficient prior
knowledge of critical components degradation process in order to predict the remaining …

Accurate evaluation of machine degradation during long-time operation is of great
importance. With the rapid development of modern industries, physical model is becoming …

In the past years, data-driven approaches such as deep learning have been widely applied
on machinery signal processing to develop intelligent fault diagnosis systems. In real-world …

In the recent years, deep learning-based intelligent fault diagnosis methods of rolling
bearings have been widely and successfully developed. However, the data-driven method …

Effective fault diagnosis of rotating machinery has always been an important issue in real
industries. In the recent years, data-driven fault diagnosis methods such as neural networks …

Intelligent data-driven fault diagnosis methods for rolling element bearings have been
widely developed in the recent years. In real industries, the collected machinery signals are …

Despite the rapid development of deep learning-based intelligent fault diagnosis methods
on rotating machinery, the data-driven approach generally remains a'black box'to …

As key equipment in modern industry, it is important to diagnose and predict the health
status of bearings. Data-driven methods for remaining useful life (RUL) prognostics have …

Convolutional neural networks (CNN) with their deep feature extraction capability have
recently been applied in numerous image fusion tasks. However, the image fusion of …

In order to obtain improved performance in complicated visual categorization tasks,
considerable research has adopted multiple kernel learning based on dozens of different …