Abstract
In this paper, a novel bearing intelligent fault diagnosis method based on a novel krill herd algorithm (NKH) and kernel extreme learning machine (KELM) is proposed. Firstly, multiscale dispersion entropy (MDE) is used to extract fault features of bearings to obtain a set of fault feature vectors composed of dispersion entropy. Then, it is imported into the kernel extreme learning machine for fault diagnosis. But considering the kernel function parameters σ and the error penalty factor C will affect the classification accuracy of the kernel extreme learning machine, this paper uses the novel krill herd algorithm (NKH) for their optimization. The opposite populations are added to the NKH in the initialization of population to improve its speed and prevent local optimum, and during the period of looking for the optimal solution, the impulse operator is introduced to ensure it has enough impulse to rush out of the local optimal once into the local optimum. Finally, in order to verify the effectiveness of the proposed method, it was applied to the bearing fault experiment of Case Western Reserve University and XJTU-SY bearing data set. The results show that the proposed method not only has good fault diagnosis performance and generalization but also has fast convergence speed and does not easily fall into the local optimum. Therefore, this paper provides a method for fault diagnosis under different loads. Meanwhile, the new method (NKH-KELM) is compared and analyzed with other mainstream intelligent bearing fault diagnosis methods to verify the effectiveness and accuracy of the proposed method.
Highlights
In the intelligent fault diagnosis, the parameter optimization of the diagnosis method is a key point
The proposed optimization algorithms belong to the optimization algorithm of the population class but generally belong to the optimal problem [59]. at is to say, when an individual is in an optimal position, but other individuals cannot know whether this position is the local optimal or the global optimal, all other individuals will move towards this optimal position and the whole population will fall into the local optimal eventually
A novel krill herd algorithm (NKH) with opposite-based learning (OBL) and impulse operators is used to optimize kernel function parameters σ and the error penalty factor C in the kernel extreme learning machine (KELM). en, the bearing data from Case Western Reserve University are imported into NKH-KELM for training and testing
Summary
XN whose length is N, the conversion equation of the multiscale operation is as follows: u(jτ). After obtaining the sequence y, it is linearly mapped to z, and the equation is as follows: zcj round c × yi + 0.5,. (5) the standard dispersion entropy is obtained from the dispersion entropy standardization equation When dealing with the fault of the bearing vibration signal, it is often difficult to know the most suitable time scales, and signal nonstationarity and irregularity tend to be very strong; in order to solve this problem well, Azami et al proposed multiscale discrete entropy (MDE) in 2017 [47] and proved that the MDE approach based on processing of the nonstationary signal has a strong ability of feature extraction. X {9, 8, 7.2, 5.3, 6.4, 3.2, 0.89, 10.6, 5.2, 4.3, 6.4, 7.8}
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