Early Fault Detection Method of Rolling Bearing Based on MCNN and GRU Network with an Attention Mechanism

Zhang, Xiaochen; Cong, Yiwen; Yuan, Zhe; Tian, Zhen; Bai, Xiaotian

doi:10.1155/2021/6660243

Cited by 31 publications

(16 citation statements)

References 31 publications

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“…Moreover, the DBN and RNN cannot deal with spatial information because they generally require one-dimensional data. The CNN is usually combined with them, such as the DBN stacked by convolutional RBMs [20], convolutional LSTM [21], and multiscale CNN-GRU with attention mechanism (MCNN-AGRU) [22], whereas their efficiency and accuracy still have room for further improvement.…”

Section: Introductionmentioning

confidence: 99%

An Engine Fault Detection Method Based on the Deep Echo State Network and Improved Multi-Verse Optimizer

Zhang

et al. 2022

Energies

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This paper aims to develop an efficient pattern recognition method for engine fault end-to-end detection based on the echo state network (ESN) and multi-verse optimizer (MVO). Bispectrum is employed to transform the one-dimensional time-dependent vibration signal into a two-dimensional matrix with more impact features. A sparse input weight-generating algorithm is designed for the ESN. Furthermore, a deep ESN model is built by fusing fixed convolution kernels and an autoencoder (AE). A novel traveling distance rate (TDR) and collapse mechanism are studied to optimize the local search of the MVO and speed it up. The improved MVO is employed to optimize the hyper-parameters of the deep ESN for the two-dimensional matrix recognition. The experiment result shows that the proposed method can obtain a recognition rate of 93.10% in complex engine faults. Compared with traditional deep belief networks (DBNs), convolutional neural networks (CNNs), the long short-term memory (LSTM) network, and the gated recurrent unit (GRU), this novel method displays superior performance and could benefit the fault end-to-end detection of rotating machinery.

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Section: Introductionmentioning

confidence: 99%

An Engine Fault Detection Method Based on the Deep Echo State Network and Improved Multi-Verse Optimizer

Zhang

et al. 2022

Energies

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“…They verified that the technique has high diagnostic accuracy, stability, and speed. Zhang et al [ 23 ] proposed an early fault detection method for rolling bearings based on a multiscale convolutional neural network and a gated circular unit network (MCNN-AGRU), with an attention mechanism which uses a multiscale data-processing method to make the features extracted by CNN more robust. Hou et al [ 24 ] proposed a multiscale convolutional neural network bearing fault diagnosis method based on wavelet transform and a one-dimensional convolutional neural network.…”

Section: Introductionmentioning

confidence: 99%

Remaining Useful Life Prediction Model for Rolling Bearings Based on MFPE–MACNN

Wang

Zhang

et al. 2022

Entropy

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Aiming to resolve the problem of redundant information concerning rolling bearing degradation characteristics and to tackle the difficulty faced by convolutional deep learning models in learning feature information in complex time series, a prediction model for remaining useful life based on multiscale fusion permutation entropy (MFPE) and a multiscale convolutional attention neural network (MACNN) is proposed. The original signal of the rolling bearing was extracted and decomposed by resonance sparse decomposition to obtain the high-resonance and low-resonance components. The multiscale permutation entropy of the low-resonance component was calculated. Moreover, the locally linear-embedding algorithm was used for dimensionality reduction to remove redundant information. The multiscale convolution module was constructed to learn the feature information at different time scales. The attention module was used to fuse the feature information and input it into the remaining useful life prediction module for evaluation. The appropriate network structure and parameter configuration were determined, and a multiscale convolutional attention neural network was designed to determine the remaining useful life prediction model. The results show that the method demonstrates effectiveness and superiority in degrading the feature information representation and improving the remaining useful life prediction accuracy compared with other models.

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“…During the past three decades, diagnostic techniques for the detection of gearbox defects have been intensively researched, including those reported by Dalpiaz et al [10], Ma et al [11], Mohammed et al [12], Saxena et al [13], Wu et al [14], and Li [15]. Rezaei et al [16] detected multicrack locations and lengths from transmission-error ratios.…”

Section: Introductionmentioning

confidence: 99%

Fault Diagnosis of Crack on Gearbox Using Vibration-Based Approaches

2022

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This study experimentally investigates vibration-based approaches for fault diagnosis of automotive gearboxes. The primary objective is to identify methods that can detect gear-tooth cracks, a common fault in gearboxes. Vibrational signals were supervised on a gearbox test rig under different operating conditions of gears with three symmetrical crack depths (1, 2, and 3 mm). The severity of the gear-tooth cracks was predicted from the vibrational signal dataset using an artificial feedforward multilayer neural network with backpropagation (NNBP). The vibration amplitudes were the greatest when the crack size in the high-speed shaft was 3 mm, and the root mean square of its vibration speed was below 3.5 mm/s. The vibration amplitudes of the gearbox increased with increasing depth of the tooth cracks under different operating conditions. The NNBP predicted the states of gear-tooth cracks with an average recognition rate of 80.41% under different conditions. In some cases, the fault degree was difficult to estimate via time-domain analysis as the vibration level increases were small and not easily noticed. Results also showed that when using the same statistical features, the time-domain analysis can better detect crack degree compared to the neural network technique.

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Early Fault Detection Method of Rolling Bearing Based on MCNN and GRU Network with an Attention Mechanism

Cited by 31 publications

References 31 publications

An Engine Fault Detection Method Based on the Deep Echo State Network and Improved Multi-Verse Optimizer

An Engine Fault Detection Method Based on the Deep Echo State Network and Improved Multi-Verse Optimizer

Remaining Useful Life Prediction Model for Rolling Bearings Based on MFPE–MACNN

Fault Diagnosis of Crack on Gearbox Using Vibration-Based Approaches

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