Intelligent Fault Diagnosis of Rolling Bearings Based on Refined Composite Multi-Scale Dispersion q-Complexity and Adaptive Whale Algorithm-Extreme Learning Machine

Measurement ◽  
2021 ◽  
pp. 108977
Author(s):  
Wei Dong ◽  
Shuqing Zhang ◽  
Anqi Jiang ◽  
Wanlu Jiang ◽  
Liguo Zhang ◽  
...  
Keyword(s):  
Fault Diagnosis ◽  
Extreme Learning Machine ◽  
Intelligent Fault Diagnosis ◽  
Rolling Bearings ◽  
Multi Scale ◽  
Learning Machine

Intelligent Fault Diagnosis of Multichannel Motor–Rotor System Based on Multimanifold Deep Extreme Learning Machine

2020 ◽  
Vol 25 (5) ◽  
pp. 2177-2187 ◽  
Author(s):  
Xiaoli Zhao ◽  
Minping Jia ◽  
Peng Ding ◽  
Chen Yang ◽  
Daoming She ◽  
...  
Keyword(s):  
Fault Diagnosis ◽  
Extreme Learning Machine ◽  
Rotor System ◽  
Intelligent Fault Diagnosis ◽  
Learning Machine

scholarly journals An Intelligent Fault Diagnosis Approach for PV Array Based on SA-RBF Kernel Extreme Learning Machine

2017 ◽  
Vol 105 ◽  
pp. 1070-1076 ◽  
Author(s):  
Yue Wu ◽  
Zhicong Chen ◽  
Lijun Wu ◽  
Peijie Lin ◽  
Shuying Cheng ◽  
...  
Keyword(s):  
Fault Diagnosis ◽  
Extreme Learning Machine ◽  
Intelligent Fault Diagnosis ◽  
Pv Array ◽  
Rbf Kernel ◽  
Kernel Extreme Learning Machine ◽  
Learning Machine ◽  
Diagnosis Approach

scholarly journals Improved Butterfly Optimizer-Configured Extreme Learning Machine for Fault Diagnosis

Complexity ◽  
2021 ◽  
Vol 2021 ◽  
pp. 1-17
Author(s):  
Helong Yu ◽  
Kang Yuan ◽  
Wenshu Li ◽  
Nannan Zhao ◽  
Weibin Chen ◽  
...  
Keyword(s):  
Fault Diagnosis ◽  
Extreme Learning Machine ◽  
Rolling Bearing ◽  
Classification Performance ◽  
Feature Subset ◽  
Intrinsic Mode Functions ◽  
Rolling Bearings ◽  
Outer Race ◽  
Rolling Ball ◽  
Learning Machine

An efficient intelligent fault diagnosis model was proposed in this paper to timely and accurately offer a dependable basis for identifying the rolling bearing condition in the actual production application. The model is mainly based on an improved butterfly optimizer algorithm- (BOA-) optimized kernel extreme learning machine (KELM) model. Firstly, the roller bearing’s vibration signals in the four states that contain normal state, outer race failure, inner race failure, and rolling ball failure are decomposed into several intrinsic mode functions (IMFs) using the complete ensemble empirical mode decomposition based on adaptive noise (CEEMDAN). Then, the amplitude energy entropies of IMFs are designated as the features of the rolling bearing. In order to eliminate redundant features, a random forest was used to receive the contributions of features to the accuracy of results, and subsets of features were set up by removing one feature in the descending order, using the classification accuracy of the SBOA-KELM model as the criterion to obtain the optimal feature subset. The salp swarm algorithm (SSA) was introduced to BOA to improve optimization ability, obtain optimal KELM parameters, and avoid the BOA deteriorating into local optimization. Finally, an optimal SBOA-KELM model was constructed for the identification of rolling bearings. In the experiment, SBOA was validated against ten other competitive optimization algorithms on 30 IEEE CEC2017 benchmark functions. The experimental results validated that the SBOA was evident over existing algorithms for most function problems. SBOA-KELM employed for diagnosing the fault diagnosis of rolling bearings obtained improved classification performance and higher stability. Therefore, the proposed SBOA-KELM model can be effectively used to diagnose faults of rolling bearings.

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