In the era of data deluge,“big data” generated by mechanical equipment creates higher
requirements for the field of mechanical fault diagnosis. Intelligent diagnosis methods have …

Background Deep learning-based fault diagnosis techniques are promising approaches that
can eliminate the need for advanced skills in signal processing and diagnostic expertise …

Intelligent fault diagnosis is an effective method to guarantee the continuous and efficient
operation of rotating machinery. Compared with the experimental environment, noise is …

Generally, the health conditions of rotating machinery are complicated and changeable.
Meanwhile, its fault labeled information is mostly unknown. Therefore, it is man-sized to …

Wind power has developed rapidly over the past decade where study on wind turbine fault
diagnosis methods are of great significance. The conventional intelligent diagnosis …

Fault diagnosis based on artificial intelligence methods is a promising tool to eliminate
reliance on a priori knowledge. Sparsity is an increasingly important topic in the field of …

Unsupervised learning method can obtain the desired feature distribution by changing the
objective function. Sparse optimization is an important principle. Given the importance of …

Early fault feature extraction and fault diagnosis are of great importance for predictive
maintenance of rotating machinery. To accurately extract early fault features from original …

Nowadays, the measuring and monitoring data collected from rotating machinery exists a
high-dimensional and massive status in quo, but the acquisition of the valuable labeled …

It is incredibly difficult to build a data-driven machine learning model for the automatic
detection of defects in rotating machinery. The existing techniques, based on machine …