As a new classification method with the matrix as the input, support matrix machine (SMM)
makes full use of the structured information between rows and columns of the input matrix to …

At present, the excellent performance of support vector machine (SVM) has made it
successfully applied in many fields. However, when SVM is used for two-dimensional matrix …

A novel intelligent fault diagnosis model based on multi-kernel support vector machine
(MSVM) with chaotic particle swarm optimization (CPSO) for roller bearing fault diagnosis is …

Roller bearing is one of the most widely used elements in rotary machines. Condition
monitoring of such elements is conceived as pattern recognition problem. Pattern …

Sensitive feature extraction from the raw vibration signal is still a great challenge for
intelligent fault diagnosis of rolling bearing. Current fault classification framework generally …

In the industrial processes, the intelligent fault diagnosis related to signal analysis and
pattern recognition is an important step to ensure the health of mechanical equipment. A …

Condition monitoring and fault diagnosis of rolling element bearings timely and accurately is
very important to ensure the reliable operation of rotating machinery. In this paper, a multi …

Random vector functional link neural network (RVFLNN) is an effective and powerful neural
network model, and it has been commonly used for various engineering applications. In this …

To improve the accuracy of bearing fault diagnosis in a multisensor monitoring environment,
it is necessary to obtain more accurate and effective fault classification features for bearings …

A new rolling bearing fault diagnosis method based on multi-scale fuzzy entropy (MFE),
Laplacian Score (LS) and variable predictive model-based class discrimination (VPMCD) is …