To predict potential problems and avoid an unexpected breakdown of rotating machinery, a
rule-based classifier ensemble approach is presented. Feature reduction was first …

Condition monitoring of rotating machinery is important to promptly detect early faults,
identify potential problems, and prevent complete failure. Four direct classification methods …

Recent researches in fault classification have shown the importance of accurately selecting
the features that have to be used as inputs to the diagnostic model. In this work, a multi …

Fault diagnosis is a major concern of the prognostics and health management of rotating
machinery. Current practice in fault diagnosis is often challenged by the non-normality …

Fault diagnosis of rotating machinery is receiving more and more attentions. Vibration
signals of rotating machinery are commonly analyzed to extract features of faults, and the …

Intelligent Fault Diagnosis and Remaining Useful Life Prediction of Rotating Machinery
provides a comprehensive introduction of intelligent fault diagnosis and RUL prediction …

The task of condition monitoring and fault diagnosis of rotating machinery faults is both
significant and important but is often cumbersome and labour intensive. Automating the …

The key to fault diagnosis of rotating machinery is to extract fault features effectively and
select the appropriate classification algorithm. As a common signal decomposition method …

Fault diagnosis of rotating machines is an important task to prevent machinery downtime,
and provide verifiable support for condition-based maintenance (CBM) decision-making …

Deep learning has made fascinating achievements in fault diagnosis of rotating machinery.
However, the individual deep learning models generally have poor performances under …