Citation: Zhang, X.; Wang, H.; Ren,
M.; He, M.; Jin, L. Rolling Bearing
Fault Diagnosis Based on Multiscale
Permutation Entropy and SOA-SVM.
Machines 2022, 10, 485. https://
doi.org/10.3390/machines10060485
Academic Editor: Antonio J. Marques
Cardoso
Received: 8 May 2022
Accepted: 13 June 2022
Published: 16 June 2022
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Article
Rolling Bearing Fault Diagnosis Based on Multiscale
Permutation Entropy and SOA-SVM
Xi Zhang, Hongju Wang *, Mingming Ren, Mengyun He and Lei Jin
School of Mechanical Electronic & Information Engineering, China University of Mining & Technology (Beijing),
Beijing 100083, China; zhangx@cumtb.edu.cn (X.Z.); bqt2000401010@student.cumtb.edu.cn (M.R.);
zqt2100401014@student.cumtb.edu.cn (M.H.); sqt2100401007@student.cumtb.edu.cn (L.J.)
* Correspondence: bqt1900401005@student.cumtb.edu.cn
Abstract:
The service conditions of underground coal mine equipment are poor, and it is difficult
to accurately extract the fault characteristics of rolling bearings. In order to better improve the
accuracy of the fault identification of rolling bearings, a fault-detection method based on multiscale
permutation entropy and SOA-SVM is proposed. First, the whale optimization algorithm is used to
select the modal analysis number K and the penalty factor
α
of the variational mode decomposition
algorithm. Then, the vibration signal of rolling bearings is dissolved according to the optimized
variational mode decomposition algorithm, and the multi-scale permutation entropy of the main
intrinsic mode function is calculated. Finally, the feature values of the matrix are entered into the SVM
algorithm optimized by the seagull optimization algorithm to obtain the classification result. The
experimental results based on the published rolling bearing datasets of Western Reserve University
show that the identification success rate of the proposed method can reach 98.75%. The fault detection
of the rolling bearings can be completed accurately and efficiently.
Keywords:
whale optimization algorithm; variational mode decomposition; seagull optimization
algorithm; support vector machine; multi-scale permutation entropy; fault diagnosis
1. Introduction
As a key component of rotating machinery and equipment, the operating conditions
of rolling bearings immediately impact the working characteristics of mining fans. When
there is a problem with a rolling bearing, the damage point constantly collides with other
parts that it touches, resulting in shock oscillation and unstable, nonlinear, multi-frequency
data signals [
1
]. Sudden faults such as loose or damaged rolling bearings will cause uneven
bearing capacity, the expansion of frictional resistance, or shutdown, leading to faults such
as displacement, unbalance, and the surge of the mining fan. The problems caused by
rolling bearings account for about 50% of the common failures of mining fans, and the
shutdown time caused by rolling bearings also accounts for about 45%. Therefore, the
accurate identification of faults of rolling bearings is of key practical significance to the
safety and stability of mining fans.
The stucture of rolling bearing determines the load distribution showing cycling
changes. Rolling balls and outer race cantact point changes will make the stiffness of the
system form a periodic change, thus producing harmonic vibration. The causes of vibration
include raceway waviness, radial play, ball errors, etc. Zmarzły [
2
] evaluates the impact of
the race’s roundness and waviness deviations, radial clearance, and total curvature ratio on
the vibration. Vibration will occur whether the rolling bearing is normal or not. Different
vibration characteristics of the bearing can reflect the different operating conditions of the
bearing. The testing of rolling bearing vibration can be classified into three groups. The
first group concerns the evaluation of the vibration of new rolling bearings on testing rigs.
The second testing group concerns the vibration analysis of rolling bearings operating in
real application conditions. The third testing group concerns the intentional induction of
Machines 2022, 10, 485. https://doi.org/10.3390/machines10060485 https://www.mdpi.com/journal/machines
