As the core of the Sparseland, dictionary learning has represented excellent performances
in many fields, such as pattern recognition, fault diagnosis, noise reduction, image …

The high complexity of actual machinery vibration environments introduces various
interferences into vibration signals, making it challenging to eliminate redundant information …

To date, planetary bearings remain challenging for machinery fault diagnosis because of
their intricate kinematics, time-variant modulations, and strong interferences. To address this …

Data-driven machinery fault diagnosis is important for smart industrial systems to guarantee
safety and reliability. However, the conventional data-driven fault diagnosis methods rely on …

Fault detection and diagnosis in the chemical industry is a challenging task due to the large
number of measured variables and complex interactions among them. To solve this …

Dictionary learning has made enormous achievements for its powerful feature
representation capabilities. For the bearing fault diagnosis, the lack of failure samples is …

Sparse representation classification (SRC) has gradually received attention due to its
powerful feature representation ability. However, the discriminative ability of traditional SRC …

Feature extraction is the key technology in the data-driven intelligent fault diagnosis
methods of rolling bearing. However, the acquired features by the traditional methods, which …

Vibration signals play a key role in machinery fault diagnosis, which are often buried by
strong noises due to complex working conditions. Typical deep neural networks (eg …

Fault diagnosis technique is the core of Prognostics and Health Management (PHM) system,
which plays a crucial role in the intelligent operation and maintenance of various rotating …