A novel hybrid method based on KELM with SAPSO for fault diagnosis of rolling bearing under variable operating conditions

Su, Hao; Xiang, Ling; Hu, Aijun; Gao, Bin; Yang, Xin

doi:10.1016/j.measurement.2021.109276

Cited by 25 publications

(7 citation statements)

References 27 publications

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“…The matrix form expressed by the operator Q k g (g = 0 or 1) at the hierarchical k is shown in formula (8):…”

Section: Hierarchical State Space Correlation Entropymentioning

confidence: 99%

“…In industrial production, when a part of the bearing is defective, it will cause the change of dynamic characteristics [7]. The dynamic abrupt behavior in bearing vibration signal can be effectively detected by using the entropise index, so as to achieve the purpose of bearing fault identification [8]. The main types of signal entropy measurement include sample entropy, permutation entropy (PE), fuzzy entropy (FE), dispersion entropy (DE) and so on.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Rolling bearing diagnosis method based on improved standardized variable distance fusion hierarchical state space correlation entropy

Wang¹,

Li²,

Du³

et al. 2021

Meas. Sci. Technol.

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In recent years, the machine learning method has been successfully applied to the intelligent health monitoring of rolling bearings. However, its effectiveness depends heavily on the vibration signal time series collected. For the purpose of abundantly excavate the fault feature information in time series, some new multi-scale entropy methods are used to solve this problem. However, the multi-scale entropy only considers the low frequency component of the signal, neglects the high frequency component of the signal, loses some fault signals with modulation phenomenon, and reduces the accuracy of fault classification. Therefore, in order to overcome the shortcomings of multi-scale entropy. In this paper, proposing a new hierarchical state space correlation entropy (HSSCEn) to extract the fault features contained in the signal. At the same time, in practice, the fault features extracted by HSSCEn are usually high and nonlinear. Directly input to the classifier for training and testing will reduce the accuracy of machine learning method recognition. Therefore, an improved standardized variable distance classifier is proposed in this paper. It effectively reduces the feature redundancy of signal and improves the accuracy of original classifier. Finally, in order to verify the validity of the fault diagnosis method based on HSSCEn fusion standardized variable distance classifier, two sets of fault classification experiments are carried out in this paper. Compared with existing fault identification classifier algorithms, the proposed method has more advantages in accuracy and stability of fault classification.

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“…The matrix form expressed by the operator Q k g (g = 0 or 1) at the hierarchical k is shown in formula (8):…”

Section: Hierarchical State Space Correlation Entropymentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Rolling bearing diagnosis method based on improved standardized variable distance fusion hierarchical state space correlation entropy

Wang¹,

Li²,

Du³

et al. 2021

Meas. Sci. Technol.

View full text Add to dashboard Cite

show abstract

“…Aiming at the defects of the single-class classifier in mechanical fault diagnosis, Zhang et al [26] proposed a hybrid artificial bee colony-optimized Morlet wavelet kernel-based ELM with sparse representation classifier. Su et al [27] proposed a hybrid diagnosis method based on simulated annealing particle swarm optimization and an improved KELM for rolling bearing fault diagnosis under variable conditions. The KELM model contains two essential parameters: regularization parameter and kernel parameter.…”

Section: Introductionmentioning

confidence: 99%

SWT-KELM-based rolling bearing fault diagnosis method under noise conditions with different SNRs

Wang¹,

Ye²,

Jiang³

et al. 2022

Meas. Sci. Technol.

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Most existing studies realize bearing fault diagnosis tasks in labs with weak noise. However, field noise is so heavy under actual conditions that some methods may suffer from degradation or failure. To solve this problem, a fault diagnosis framework is proposed based on synchrosqueezing wavelet transform and kernel extreme learning machine (SWT-KELM). First, vibration signals are collected, and white Gaussian noise is added. Second, SWT is employed for signal decomposition in the time-frequency domain, and inverse SWT (ISWT) is applied for sub-signal reconstruction. Sub-signals with high correlation coefficients are selected for further feature extraction, specifically by singular value decomposition (SVD) to obtain singular values as the fault feature. Third, the KELM model, in which the beetle antennae search algorithm (BAS) is employed for parameters optimization, is constructed to classify the faults. For verification, the proposed method is implemented on the Case Western Reserve University (CWRU) dataset and Lab-625 dataset, and the results show that it maintains satisfactory outcomes on original and noise-contaminated data. Specifically, under noise conditions, the accuracies of the two datasets average at 96% and 83%, respectively, indicating the robustness and generalization of the method compared to other methods.

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“…Compared with ELM, KELM only needs to select the kernel function and its related parameters to obtain the output weight, which has fewer adjustable parameters, better generalization performance, etc. (Qin et al 2017;Su et al 2021) applied it to the diagnosis of rolling bearings then gained better accuracy. On the other hand, kernel function makes the KELM be very sensitive to parameter settings.…”

Section: Introductionmentioning

confidence: 99%

Multi-fault diagnosis of rolling bearing using two-dimensional feature vector of WP-VMD and PSO-KELM algorithm

jiang

Li²,

Shen

2022

Soft Comput

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In order to achieve accurate fault diagnosis of rolling bearing under random noise, a new fault diagnosis method based on wavelet packet-variational mode decomposition (WP-VMD) and kernel extreme learning machine (KELM) optimized by particle swarm optimization (PSO) is proposed in this paper. Firstly, the time-frequency domain feature vectors of the original rolling bearing fault signals are effectively obtained by preprocessing of WMD and decomposition and reconstruction of VMD.Then, the extracted two-dimensional feature vector is input into the KELM neural network for fault identification, and combined with PSO, KELM parameters were optimized. The experimental results show that the proposed method can effectively diagnose the rolling bearing under random noise, with the features of fast speed, stable performance and high accuracy. By comparison, this paper obtains better accuracy and real-time performance with fewer features, which provides a simple and efficient solution for fault diagnosis of rolling bearings.

show abstract

A novel hybrid method based on KELM with SAPSO for fault diagnosis of rolling bearing under variable operating conditions

Cited by 25 publications

References 27 publications

Rolling bearing diagnosis method based on improved standardized variable distance fusion hierarchical state space correlation entropy

Rolling bearing diagnosis method based on improved standardized variable distance fusion hierarchical state space correlation entropy

SWT-KELM-based rolling bearing fault diagnosis method under noise conditions with different SNRs

Multi-fault diagnosis of rolling bearing using two-dimensional feature vector of WP-VMD and PSO-KELM algorithm

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