A New Fault Diagnosis Classifier for Rolling Bearing United Multi-Scale Permutation Entropy Optimize VMD and Cuckoo Search SVM

Abstract

Aiming at the influence of mixed noise of bearing vibration signal on the extraction of useful information, a fault diagnosis optimize classifier based on multi-scale permutation entropy (MPE) and cuckoo search algorithm (CS) is proposed. Firstly, the MPE threshold method is adopted to select the appropriate variational mode decomposition algorithm (VMD) parameters, and then the signal is reconstructed by adding neutral white noise, and the reconstructed signal is decomposed by MPE-OVMD algorithm to obtain the optimal IMF component. Finally, the cuckoo search algorithm is used to optimize the global optimal solution of the support vector machine, thereby achieving the classification model of support vector machine with the best parameters. The analysis results of motor signals show that the method can eliminate the phenomena of mode aliasing and signal over-decomposition. An analytical comparison of the CSSVM classifier is carried out with the performance of the learners such as recall rate, ROC curve, AUC. The contrast experiment shows that the classification model can avoid misrecognition of the fault sample as the normal condition and maximum the optimal maintenance time of the equipment under the premise of ensuring the accuracy. The classifier model of the cuckoo optimization algorithm has better fitting accuracy than others such as the Grid Search algorithm (GS), Particle Swarm Optimization (PSO), Genetic Algorithm search (GA), and the ensemble fault recognition rate is as high as 90%.

Keywords

• Variational mode decomposition
• multi-scale permutation entropy
• signal reconstruction
• cuckoo algorithm
• support vector machine