Fault detection and identification of rolling element bearings with Attentive Dense CNN

Plakias, Spyridon; Boutalis, Yiannis S.

doi:10.1016/j.neucom.2020.04.143

Cited by 73 publications

(26 citation statements)

References 34 publications

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“…In Table 9, it can be noticed that only [34] and the model proposed herein identify 16 classes trying to recognise not only the type of bearing faults but also their severities and so solving a more difficult task. Also, the authors observed that the proposed Attention Neural Stream and the models of [34,47] TA B L E 6 Performance (%) of compared neural models in the Paderborn benchmark data set…”

Section: Comparison With State-of-the-art Methods Using the Cwru Bementioning

confidence: 99%

Extracting spatially global and local attentive features for rolling bearing fault diagnosis in electrical machines using attention stream networks

Karnavas

Plakias

Chasiotis

2021

IET Electric Power Appl

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A health diagnosis mechanism of rolling element bearings is necessary since the most frequent faults in rotating electrical machines occur in the bearing parts. Recently, convolutional neural networks (CNNs) have redefined the state-of-the-art accuracy for bearing fault detection and identification, extracting location invariant feature mappings without the need for prior expert knowledge. With the use of convolution operations as the core of the process, CNNs consider the local spatial coherence of the input. However, one major drawback of the convolutional models is the weakness to capture global information about the input vector and to derive knowledge about the statistical properties of the latter. The authors propose a deep learning (DL) model that concatenates the features that are produced from two neural streams. Each consists of an attention mechanism that intends to learn different representations of the input vector, and so finally to produce a feature mapping that contains global and spatial locally information. Simulation results on two famous rolling element bearings fault detection benchmarks show the effectiveness of the method. In particular, the proposed DL model achieves 99.60% in the Case Western Reserve University bearing data set and 99.10% in the Paderborn University bearing data set. This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.

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Section: Comparison With State-of-the-art Methods Using the Cwru Bementioning

confidence: 99%

Extracting spatially global and local attentive features for rolling bearing fault diagnosis in electrical machines using attention stream networks

Karnavas

Plakias

Chasiotis

2021

IET Electric Power Appl

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“…In order to overcome the shortcomings of the shallow structure, a fault diagnosis algorithm based on stacked LSTM was proposed [14]. A new bearing fault diagnosis method, dense convolutional neural networks (ADCNN), was proposed in [15], which considers the temporal coherence of the data samples by combining dense convolution blocks and attention mechanisms. Multi-scale analysis of data helps to obtain richer features and improve fault diagnosis capabilities.…”

Section: Introductionmentioning

confidence: 99%

DWT-LSTM-Based Fault Diagnosis of Rolling Bearings with Multi-Sensors

Zhang

Liu

et al. 2021

Electronics

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Bearings are widely used in many steam turbine generator sets and other large rotating equipment. With the rapid development of contemporary industry, there is a great number of rotating equipment in various large factories, such as nuclear power plants. As the core component of rotating machinery, the failure of rolling bearings may lead to serious accidents during the industrial production operation. In order to accurately diagnose the fault status of rolling bearings, a novel long short-term memory (LSTM) model with discrete wavelet transformation (DWT) for multi-sensor fault diagnosis is proposed in this paper. The main purpose of this paper is to use the DWT-LSTM model to diagnose the health of rolling bearings. Firstly, the DWT is used to obtain detailed fault information in both different frequency and time scales. Then, the LSTM network is employed to characterize the long-term dependencies hidden in the time series of the fault information. The proposed DWT-LSTM method makes full use of the advantages of feature extraction based on expert experience and deep network learning to discover complex patterns from a large amount of data. Finally, the feasibility and efficiency of the proposed method are illustrated by comparison with the existing methods.

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“…Machine learning methods have achieved good performances for classification of rotating machinery. However, their architecture still lacks multi-layer nonlinear mapping ability, and as a result they cannot fully use previous information for classification, and existing methods need to exhibit better performances for the amount of data in complex conditions [ 11 , 12 , 13 , 14 ].…”

Section: Introductionmentioning

confidence: 99%

“…DL is based on modularized automatically learning network architectures; similarly, convolutional neural networks (CNN) are used to capture impact features of vibration signals [ 8 ]. Densely connected CNNs (DCNNs) are introduced to reinforce the collection learning of vibration features due to the weight reuse and better performance than traditional CNN [ 12 ]. The WDCNN model can achieve a better performance for extraction features; however, when considering a complex condition, it still requires a large amount of data for training and decreases rapidly; although complex DL models can achieve good results for diagnosis, they still consume a lot of computational time and training examples [ 2 , 10 ].…”

Section: Introductionmentioning

confidence: 99%

A Novel Hybrid Deep Learning Method for Fault Diagnosis of Rotating Machinery Based on Extended WDCNN and Long Short-Term Memory

Gao

Kim

2021

Sensors

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Deep learning (DL) plays a very important role in the fault diagnosis of rotating machinery. To enhance the self-learning capacity and improve the intelligent diagnosis accuracy of DL for rotating machinery, a novel hybrid deep learning method (NHDLM) based on Extended Deep Convolutional Neural Networks with Wide First-layer Kernels (EWDCNN) and long short-term memory (LSTM) is proposed for complex environments. First, the EWDCNN method is presented by extending the convolution layer of WDCNN, which can further improve automatic feature extraction. The LSTM then changes the geometric architecture of the EWDCNN to produce a novel hybrid method (NHDLM), which further improves the performance for feature classification. Compared with CNN, WDCNN, and EWDCNN, the proposed NHDLM method has the greatest performance and identification accuracy for the fault diagnosis of rotating machinery.

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Fault detection and identification of rolling element bearings with Attentive Dense CNN

Cited by 73 publications

References 34 publications

Extracting spatially global and local attentive features for rolling bearing fault diagnosis in electrical machines using attention stream networks

Extracting spatially global and local attentive features for rolling bearing fault diagnosis in electrical machines using attention stream networks

DWT-LSTM-Based Fault Diagnosis of Rolling Bearings with Multi-Sensors

A Novel Hybrid Deep Learning Method for Fault Diagnosis of Rotating Machinery Based on Extended WDCNN and Long Short-Term Memory

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