Learning local discriminative representations via extreme learning machine for machine fault diagnosis

Li, Yue; Zeng, Yijie; Qing, Yuanyuan; Huang, Guang-Bin

doi:10.1016/j.neucom.2020.05.021

Cited by 33 publications

(10 citation statements)

References 27 publications

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“…Compared with traditional methods [24], extreme learning machines (ELMs) [25,26] are effective and simple learning methods. The random generation of the ELM's hidden nodes increases its learning speed [27,28]. Taking the advantages of ELM into consideration (high learning efficiency, conceptual simplicity, and good generalization capacity) [29,30], ELMs can be used as the base classifier for designing ensemble classifiers.…”

Section: Introductionmentioning

confidence: 99%

Ensemble Classifier Design Based on Perturbation Binary Salp Swarm Algorithm for Classification

Zhu¹,

Xia²,

He³

et al. 2023

Computer Modeling in Engineering &Amp; Sciences

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Multiple classifier system exhibits strong classification capacity compared with single classifiers, but they require significant computational resources. Selective ensemble system aims to attain equivalent or better classification accuracy with fewer classifiers. However, current methods fail to identify precise solutions for constructing an ensemble classifier. In this study, we propose an ensemble classifier design technique based on the perturbation binary salp swarm algorithm (ECDPB). Considering that extreme learning machines (ELMs) have rapid learning rates and good generalization ability, they can serve as the basic classifier for creating multiple candidates while using fewer computational resources. Meanwhile, we introduce a combined diversity measure by taking the complementarity and accuracy of ELMs into account; it is used to identify the ELMs that have good diversity and low error. In addition, we propose an ECDPB with powerful optimizing ability; it is employed to find the optimal subset of ELMs. The selected ELMs can then be used to form an ensemble classifier. Experiments on 10 benchmark datasets have been conducted, and the results demonstrate that the proposed ECDPB delivers superior classification capacity when compared with alternative methods.

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Section: Introductionmentioning

confidence: 99%

Ensemble Classifier Design Based on Perturbation Binary Salp Swarm Algorithm for Classification

Zhu¹,

Xia²,

He³

et al. 2023

Computer Modeling in Engineering &Amp; Sciences

View full text Add to dashboard Cite

show abstract

“…Compared with the traditional FNN algorithm, the advantages of ELM in terms of efficiency and generalization performance have been proven on a wide range of issues in different fields [ 9 ]. The ELM possesses a faster learning and better generalization performance [ 10 , 11 , 12 ] than traditional gradient-based learning algorithms [ 13 ]. Due to efficient learning ability of ELM, it is widely used in classification, regression problems, etc.…”

Section: Introductionmentioning

confidence: 99%

A Hybrid Method Based on Extreme Learning Machine and Wavelet Transform Denoising for Stock Prediction

Wang

2021

Entropy

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The trend prediction of the stock is a main challenge. Accidental factors often lead to short-term sharp fluctuations in stock markets, deviating from the original normal trend. The short-term fluctuation of stock price has high noise, which is not conducive to the prediction of stock trends. Therefore, we used discrete wavelet transform (DWT)-based denoising to denoise stock data. Denoising the stock data assisted us to eliminate the influences of short-term random events on the continuous trend of the stock. The denoised data showed more stable trend characteristics and smoothness. Extreme learning machine (ELM) is one of the effective training algorithms for fully connected single-hidden-layer feedforward neural networks (SLFNs), which possesses the advantages of fast convergence, unique results, and it does not converge to a local minimum. Therefore, this paper proposed a combination of ELM- and DWT-based denoising to predict the trend of stocks. The proposed method was used to predict the trend of 400 stocks in China. The prediction results of the proposed method are a good proof of the efficacy of DWT-based denoising for stock trends, and showed an excellent performance compared to 12 machine learning algorithms (e.g., recurrent neural network (RNN) and long short-term memory (LSTM)).

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“…Once bearings are damaged, it will inevitably cause mechanical equipment to stop work, bringing about economic loss and even causing personnel casualties [ 1 ]. Therefore, accurate diagnosis of bearing faults is of great significance in ensuring the safe and reliable operation of mechanical equipment [ 2 ]. That is, it is very valuable to develop effective bearing fault diagnosis technology for the field of mechanical health monitoring.…”

Section: Introductionmentioning

confidence: 99%

Reliable Fault Diagnosis of Bearings Using an Optimized Stacked Variational Denoising Auto-Encoder

Yan

She

et al. 2021

Entropy

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Variational auto-encoders (VAE) have recently been successfully applied in the intelligent fault diagnosis of rolling bearings due to its self-learning ability and robustness. However, the hyper-parameters of VAEs depend, to a significant extent, on artificial settings, which is regarded as a common and key problem in existing deep learning models. Additionally, its anti-noise capability may face a decline when VAE is used to analyze bearing vibration data under loud environmental noise. Therefore, in order to improve the anti-noise performance of the VAE model and adaptively select its parameters, this paper proposes an optimized stacked variational denoising autoencoder (OSVDAE) for the reliable fault diagnosis of bearings. Within the proposed method, a robust network, named variational denoising auto-encoder (VDAE), is, first, designed by integrating VAE and a denoising auto-encoder (DAE). Subsequently, a stacked variational denoising auto-encoder (SVDAE) architecture is constructed to extract the robust and discriminative latent fault features via stacking VDAE networks layer on layer, wherein the important parameters of the SVDAE model are automatically determined by employing a novel meta-heuristic intelligent optimizer known as the seagull optimization algorithm (SOA). Finally, the extracted latent features are imported into a softmax classifier to obtain the results of fault recognition in rolling bearings. Experiments are conducted to validate the effectiveness of the proposed method. The results of analysis indicate that the proposed method not only can achieve a high identification accuracy for different bearing health conditions, but also outperforms some representative deep learning methods.

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Learning local discriminative representations via extreme learning machine for machine fault diagnosis

Cited by 33 publications

References 27 publications

Ensemble Classifier Design Based on Perturbation Binary Salp Swarm Algorithm for Classification

Ensemble Classifier Design Based on Perturbation Binary Salp Swarm Algorithm for Classification

A Hybrid Method Based on Extreme Learning Machine and Wavelet Transform Denoising for Stock Prediction

Reliable Fault Diagnosis of Bearings Using an Optimized Stacked Variational Denoising Auto-Encoder

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