Abstract
Aiming at the shortcomings of difficult classification of rolling bearing compound faults and low recognition accuracy, a composite fault diagnosis method of rolling bearing combined with ALIF and KELM is proposed. First, the basic concepts of ALIF and KELM are introduced, and then ALIF is used to decompose the sample data of vibration signals of different bearing states so that each sample can get several IMFs, select the top K IMFs containing the main fault information from each sample, calculate the energy feature and sample entropy of each IMF, and construct a fault feature vector with a dimension of 2K. Finally, the feature vectors of the training set and the test set are input into the KELM model for fault classification. Experimental results show that, compared with EMD-KELM model, ALIF-ELM model, ALIF-BP model, and IFD-KELM model, the rolling bearing composite fault diagnosis method based on the ALIF-KELM model has higher classification accuracy.
Highlights
Rolling bearings are one of the basic components and play an important role in various types of industrial equipment
To effectively resolves rolling bearing problems, researchers have proposed many adaptive mode decomposition methods inspired by the idea of empirical mode decomposition (EMD), including local mean decomposition (LMD), empirical wavelet transform (EWT), and variational modal decomposition (VMD) [5,6,7]
The overall fault diagnosis accuracy rate is 99%, of which the single fault diagnosis accuracy rate is 100%, and the compound fault diagnosis accuracy rate is 95%. erefore, the effectiveness of the intelligent diagnosis method of rolling bearing composite fault based on Adaptive Local Iterative Filter (ALIF) and KELM proposed in the paper can be proved
Summary
Rolling bearings are one of the basic components and play an important role in various types of industrial equipment. To effectively resolves rolling bearing problems, researchers have proposed many adaptive mode decomposition methods inspired by the idea of EMD, including local mean decomposition (LMD), empirical wavelet transform (EWT), and variational modal decomposition (VMD) [5,6,7]. The algorithm has been increasingly applied to the field of rotating machinery fault diagnosis, Chen et al [13] combined ALIF and energy operator demodulation methods to effectively diagnose the fault characteristic frequencies of rolling bearings. For the nonstationary characteristics of bearing vibration signals, scholars, locally and abroad, usually use various nonstationary signal processing and analysis methods combined with the ELM algorithm to conduct intelligent diagnosis research on rolling bearing faults. Is paper proposes a composite fault diagnosis method for rolling bearings that combines both Adaptive Local Iterative Filter (ALIF) and KELM approaches The original algorithm is optimized, and the kernel function is used to replace the activation function of the hidden layer to make the model stable and universal. e KELM algorithm has improved generalization ability and is more suitable for solving multiclassification problems. is paper proposes a composite fault diagnosis method for rolling bearings that combines both Adaptive Local Iterative Filter (ALIF) and KELM approaches
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