Bearing fault diagnosis method based on an improved particle swarm optimization–variational mode decomposition (PSO–VMD) multidimensional index and its application in a crane amplitude-changing mechanism

In the luffing mechanism of a crane, the bearing is one of the parts that bear the important load. If the bearing fails, the luffing mechanism of the crane will lose its steady state of operation and may even cause an accident. To identify the early characteristics of bearing failure in the crane luffing mechanism and prevent losses resulting from severe bearing damage, this paper proposes a bearing fault diagnosis method for the crane luffing mechanism based on improved particle swarm optimization–variational mode decomposition (PSO–VMD) multidimensional indicators. Firstly, the Metropolis algorithm was introduced into the PSO, and an improved PSO (IPSO) was proposed to solve the problems of poor global search ability and easy falling into local extreme values. The parameters of VMD are adapted through IPSO to decompose model components. Then, the three components with the largest kurtosis are selected for signal reconstruction, and the peak factor, margin factor, pulse factor, sample entropy, energy entropy, and power spectrum entropy of the reconstructed signal are used to form a multidimensional composite feature vector, and then the principal component analysis (PCA) method is used to extract its core components. Finally, the core components are used as input samples of the support vector machine (SVM) for training and testing, which can effectively detect the bearing fault of the crane luffing mechanism. Experimental data demonstrate that the proposed method not only reduces the training time of the classification model but also enhances the classification accuracy.