Abstract
Since variational mode decomposition (VMD) was proposed, it has been widely used in condition monitoring and fault diagnosis of mechanical equipment. However, the parameters K and α in the VMD algorithm need to be set before decomposition, which causes VMD to be unable to decompose adaptively and obtain the best result for signal decomposition. Therefore, this paper optimizes the VMD algorithm. On this basis, this paper also proposes a method of multi-domain feature extraction of signals and combines an extreme learning machine (ELM) to realize comprehensive and accurate fault diagnosis. First, VMD is optimized according to the improved grey wolf optimizer; second, the feature vectors of the time, frequency, and time-frequency domains are calculated, which are synthesized after dimensionality reduction; ultimately, the synthesized vectors are input into the ELM for training and classification. The experimental results show that the proposed method can decompose the signal adaptively, which produces the best decomposition parameters and results. Moreover, this method can extract the fault features of the signal more completely to realize accurate fault identification.
Highlights
With the increasing depletion of traditional energies, wind energy, a clean energy, has been widely considered
In order to further illustrate the effectiveness of the proposed method, ensemblemode empirical mode decomposition (EEMD) was combined with 4 pattern recognition methods: spacetheissupport used to vector machine (SVM), genetic algorithm back propagation (GA-BP) neural network, extreme learning machine (ELM), and deep convolutional neural networks (Deep-CNN) to obtain accuracy rate of diagnosis in 3 cases
0.791 feature extraction, this paper proposes a multi-domain fault diagnosis method
Summary
With the increasing depletion of traditional energies, wind energy, a clean energy, has been widely considered. In reference [6], a fault feature extraction method processing methods are the mainstream at present. In reference [6], a fault feature extraction method based basedon onlocal localmean meandecomposition decomposition(LMD). Inthe reference [8], signal the vibration signal is decomposed into a setmode of intrinsic mode(IMFs) functions ensemble mode decomposition (EEMD). To fundamentally solve this problem, a new method is decomposition principle. Extreme learning machine (ELM) has thehigh advantages fast training learning efficiency, and strong robustness [16,22,23] Based on these researches, this paper applies an improved GWO algorithm [24,25] to VMD parameter optimization to achieve better adaptive VMD decomposition. Combined with the ELM, this paper proposes a multi-domain fault diagnosis method and applies it to the fault diagnosis of the rolling bearing of the gearbox
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