No abstract

No abstract

Abstract:The mechanical fault diagnosis results of the high voltage circuit breakers (HVCBs) are mainly determined by the feature vector and classifier used. In order to obtain more remarkable characteristics of signals and a robust classifier which is suitable for small sample classification, in this paper, a new mechanical fault diagnosis method is proposed. Firstly, the vibration signals of HVCBs are collected by a designed acquisition system, and the noise of signals is eliminated by a soft threshold de-noising method. Secondly, the empirical wavelet transform (EWT) is adopted to decompose the signals into a series of physically meaningful modes, and then, the improved time-frequency entropy (ITFE) method is used to extract the characteristics of the vibration signals. Finally, a generalized regression neural network (GRNN) is used to identify four types of vibration signals of HVCBs, while the smoothing parameter δ of GRNN is optimized by a loop traversal method. The experimental results show that by using this optimal classifier for fault diagnosis, the proposed fault diagnosis method has the better generalization performance and the recognition rate of unknown samples is over 95%, and the signal features obtained by the ITFE method are more significant than those of the traditional TFE method.

No 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%.

Traditional fault diagnosis methods of DC (direct current) motors require establishing accurate mathematical models, effective state and parameter estimations, and appropriate statistical decision-making methods. However, these preconditions considerably limit traditional motor fault diagnosis methods. To address this issue, a new mechanical fault diagnosis method was proposed. Firstly, the vibration signals of motors were collected by the designed acquisition system. Subsequently, variational mode decomposition (VMD) was adopted to decompose the signal into a series of intrinsic mode functions and extract the characteristics of the vibration signals based on sample entropy. Finally, a united random forest improvement based on a SPRINT algorithm was employed to identify vibration signals of rotating machinery, and each branch tree was trained by applying different bootstrap sample sets. As the results reveal, the proposed fault diagnosis method is featured with good generalization performance, as the recognition rate of samples is more than 90%. Compared with the traditional neural network, data-heavy parameter optimization processes are avoided in this method. Therefore, the VMD-SampEn-RF-based method proposed in this paper performs well in fault diagnosis of DC motors, providing new ideas for future fault diagnoses of rotating machinery.

No abstract

No abstract