Cloud ensemble learning for fault diagnosis of rolling bearings with stochastic configuration networks

Dai, Wei; Liu, Jiang; Wang, Lanhao

doi:10.1016/j.ins.2023.119991

Cited by 8 publications

(1 citation statement)

References 42 publications

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“…Other approaches focus on feature decomposition, with an emphasis on effective feature extraction. For example, Wang et al [ 9 ] proposed a framework integrating singular spectrum decomposition (SSD) to generate practical spectral components, along with a neural network configured by a stochastic configuration network, which was also utilized in [ 10 ]. Li et al [ 11 ] developed data augmentation methods called variational mode reconstruction (VMR) to enrich training data, with the augmented dataset subsequently used to train a deep residual shrinkage network.…”

Section: Introductionmentioning

confidence: 99%

Failure Mode Classification for Rolling Element Bearings Using Time-Domain Transformer-Based Encoder

Vu,

Hiraga,

Miura

et al. 2024

Sensors

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In this paper, we propose a Transformer-based encoder architecture integrated with an unsupervised denoising method to learn meaningful and sparse representations of vibration signals without the need for data transformation or pre-trained data. Existing Transformer models often require transformed data or extensive computational resources, limiting their practical adoption. We propose a simple yet competitive modification of the Transformer model, integrating a trainable noise reduction method specifically tailored for failure mode classification using vibration data directly in the time domain without converting them into other domains or images. Furthermore, we present the key architectural components and algorithms underlying our model, emphasizing interpretability and trustworthiness. Our model is trained and validated using two benchmark datasets: the IMS dataset (four failure modes) and the CWRU dataset (four and ten failure modes). Notably, our model performs competitively, especially when using an unbalanced test set and a lightweight architecture.

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Section: Introductionmentioning

confidence: 99%

Failure Mode Classification for Rolling Element Bearings Using Time-Domain Transformer-Based Encoder

Vu,

Hiraga,

Miura

et al. 2024

Sensors

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Diagnosis of incipient faults in wind turbine bearings based on ICEEMDAN–IMCKD

Li,

Han

2024

Int Journal of Mech Sys Dyn

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To address the difficulty in extracting early fault feature signals of rolling bearings, this paper proposes a novel weak fault diagnosis method for rolling bearings. This method combines the Improved Complementary Ensemble Empirical Mode Decomposition with Adaptive Noise (ICEEMDAN) and the Improved Maximum Correlated Kurtosis Deconvolution (IMCKD). Utilizing the kurtosis criterion, the intrinsic mode functions obtained through ICEEMDAN are reconstructed and denoised using IMCKD, which significantly reduces noise in the measured signal. This approach maximizes the energy amplitude at the fault characteristic frequency, facilitating fault feature identification. Experimental studies on two test benches demonstrate that this method effectively reduces noise interference and highlights the fault frequency components. Compared with traditional methods, it significantly improves the signal‐to‐noise ratio and more accurately identifies fault features, meeting the requirements for discriminating rolling bearing faults. The method proposed in this study was applied to the measured vibration signals of the gearbox bearings in the new high‐speed wire department of a Long Products Mill. It successfully extracted weak characteristic information of early bearing faults, achieving the expected diagnostic results. This further validates the effectiveness of the ICEEMDAN–IMCKD method in practical engineering applications, demonstrating significant engineering value for detecting and extracting weak impact characteristics in rolling bearings.

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Proactive failure warning for wind power forecast models based on volatility indicators analysis

Chen,

Lin,

Zhang

et al. 2024

Energy

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Cloud ensemble learning for fault diagnosis of rolling bearings with stochastic configuration networks

Cited by 8 publications

References 42 publications

Failure Mode Classification for Rolling Element Bearings Using Time-Domain Transformer-Based Encoder

Failure Mode Classification for Rolling Element Bearings Using Time-Domain Transformer-Based Encoder

Diagnosis of incipient faults in wind turbine bearings based on ICEEMDAN–IMCKD

Proactive failure warning for wind power forecast models based on volatility indicators analysis

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