To diagnose rotating machinery fault for imbalanced data, a method based on fast clustering
algorithm (FCA) and support vector machine (SVM) was proposed. Combined with …

To diagnose rotating machinery fault for imbalanced data, a kind of method based on fast
clustering algorithm and decision tree is proposed. Combined with wavelet packet …

Fault diagnosis plays an essential role in rotating machinery manufacturing systems to
reduce their maintenance costs. How to improve diagnosis accuracy remains an open issue …

In an actual engineering environment, some rotating machines are usually in normal
operation, but their time in a fault state is very short, which leads to a serious imbalance in …

A novel method to solve the rotating machinery fault diagnosis problem is proposed, which
is based on principal components analysis (PCA) to extract the characteristic features and …

Fault diagnosis methods based on machine learning have made great progress for rotating
machinery. The main steps of the machine learning process involve feature extraction …

Rolling bearing is of great importance in modern industrial products, the failure of which may
result in accidents and economic losses. Therefore, fault diagnosis of rolling bearing is …

Although the diagnosis methods of rotating machinery based on convolutional neural
network (CNN) have achieved great success, they generally assume the number of normal …

Imbalanced data problems are prevalent in the real rotating machinery applications.
Traditional data-driven diagnosis methods fail to identify the fault condition effectively for …

The rolling bearing is a key element of rotating machine and its fault diagnosis is a research
focus. When a single fault of a rolling bearing fails to be addressed in time, it will cause …