Manufacturing systems are becoming more sophisticated and expensive, particularly with
the development of the intelligent industry. The complexity of the architecture and concept of …

Considering about slow drift and complicated relationships among process variables
caused by corrosion, fatigue, and so on in complex chemical engineering processes, an …

Condition monitoring (CM) of wind turbine gearbox is one of the key concerns for the reliable
operation of wind power generation. With the huge ongoing transition towards renewable …

Deep learning methods are essential for the application of data driven technologies on fault
diagnosis of rotating machinery. However, the generalization and performance of deep …

Focusing on the problems of complex structure and low feature extraction efficiency that
exist in some traditional neural network algorithms, an improved convolutional neural …

Very early bolt looseness monitoring has been a challenge in the field of structural health
monitoring. The authors have conducted a further study of the previous researches, with the …

Fault diagnosis (FD) has received attention because of its importance in maintaining safe
operations of industrial processes. Recently, modern data-driven FD approaches such as …

Compared with traditional model-based fault detection and classification (FDC) methods,
deep neural networks (DNNs) prove to be more accurate for aerospace sensors. An …

For better reliability of tractors with continuously variable transmission, reported here is fault
diagnosis of their power-shift systems. First, four hydraulic system faults are analyzed, ie …

Accurate fault diagnosis technology is essential for ensuring reliable operation of rotating
machinery. However, complex conditions and various damage forms bring challenges to …