Abstract
In this paper, a novel bearing fault diagnosis method based on multi-layer extreme learning machine (MELM) optimized by the novel ant lion algorithm (NALO) is proposed. First, using permutation entropy of different scales (MPE) to extract fault features of bearings, a group of fault feature vectors composed of permutation entropy is obtained. Then, the fault feature vectors are classified by the MELM. However, with the increase of the number of hidden layers, the random input weight and bias will also increase, which will lead to the increase of the randomness of the MELM and affect the accuracy of fault diagnosis. Therefore, this paper uses the NALO to optimize the MELM. For the NALO, opposite populations are added to the initial population to improve its global search ability. When the ant lion updates its location, the influence of pheromones left by other ants with a certain sensing distance is taken into account to prevent the ant lion from falling into the local optimal and increased the robustness. Finally, the NALO-MELM and other bearing fault diagnosis methods are applied to the bearing fault experiment of Western Reserve University to test the performance and generalization of the proposed method.
Highlights
For rotating machine, rolling bearing is one of the most damaged parts in the rotating machine due to its poor working environment and easy occurrence of resonance [1] and other problems
The methods based on signal processing include modified variable modal decomposition (MVMD) [11]–[13], improved local mean decomposition (ILMD) [14], [15], maximum kurtosis spectral deconvolution (MKESD) [16], adaptive spectral kurtosis [17], minimum entropy deconvolution adjusted (MEDA) [18], empirical mode decomposition (EMD) [19], etc
Based on the above mentioned, this paper proposes a bearing fault diagnosis method based on the multi-layer extreme learning machine optimized by the novel ant lion algorithm
Summary
For rotating machine, rolling bearing is one of the most damaged parts in the rotating machine due to its poor working environment and easy occurrence of resonance [1] and other problems. The effect from pheromone [46] of a certain sensing distance in the novel ant lion algorithm (NALO) proposed in this paper is added to the formula of updating the position. Based on the above mentioned, this paper proposes a bearing fault diagnosis method based on the multi-layer extreme learning machine optimized by the novel ant lion algorithm. The many input weights and input biases of the multi-layer extreme learning machine are optimized by a novel ant lion algorithm. In the actual bearing fault diagnosis, the traditional permutation entropy only considers the complexity of time series in a single scale, which is often very one-sided and not conducive to the classification operation of the classifier based on machine learning. A novel ant lion algorithm (NALO) is proposed inspired by the method of krill updating position.
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