An optimal deep sparse autoencoder with gated recurrent unit for rolling bearing fault diagnosis

Zhao, Ke; Jiang, Hongkai; Li, Xingqiu; Wang, Ruixin

doi:10.1088/1361-6501/ab3a59

Cited by 38 publications

(31 citation statements)

References 44 publications

(57 reference statements)

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“…e performance comparison with SVM and KNN models confirmed that the proposed scheme performed much better than the traditional schemes with an accuracy of 96.1%. Zhao et al [68] have constructed an optimal hybrid DL model, which consists of SAE and GRU, to classify the rolling bearing faults more accurately. In addition, they have used the graywolf optimising algorithm to enhance the performance of the model.…”

Section: Autoencoders (Ae)mentioning

confidence: 99%

The Importance of Feature Processing in Deep-Learning-Based Condition Monitoring of Motors

Soother

Daudpoto

Harris

et al. 2021

Mathematical Problems in Engineering

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The advent of deep learning (DL) has transformed diagnosis and prognosis techniques in industry. It has allowed tremendous progress in industrial diagnostics, has been playing a pivotal role in maintaining and sustaining Industry 4.0, and is also paving the way for industry 5.0. It has become prevalent in the condition monitoring of industrial subsystems, a prime example being motors. Motors in various applications start deteriorating due to various reasons. Thus, the monitoring of their condition is of prime importance for sustaining the operation and maintaining efficiency. This paper presents a state-of-the-art review of DL-based condition monitoring for motors in terms of input data and feature processing techniques. Particularly, it reviews the application of various input features for the effectiveness of DL models in motor condition monitoring in the sense of what problems are targeted using these feature processing techniques and how they are addressed. Furthermore, it discusses and reviews advances in DL models, DL-based diagnostic methods for motors, hybrid fault diagnostic techniques, points out important open challenges to these models, and signposts the prospective future directions for DL models. This review will assist researchers in identifying research gaps related to feature processing, so that they may effectively contribute toward the implementation of DL models as applied to motor condition monitoring.

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Section: Autoencoders (Ae)mentioning

confidence: 99%

The Importance of Feature Processing in Deep-Learning-Based Condition Monitoring of Motors

Soother

Daudpoto

Harris

et al. 2021

Mathematical Problems in Engineering

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“…We compared the proposed MLKDCE-PBiLSTM with five advanced methods. They are a DCAE network with five-layer convolutional network [ 15 ], BiLSTM network [ 24 ], LSTM with multiple CNN [ 23 ], MSCNN [ 40 ], LeNet-5 with a new convolutional neural network proposed by Wen [ 41 ]. The six methods adopt the same training strategies in the overall experiments.…”

Section: Performance Verificationmentioning

confidence: 99%

“…Compared with traditional intelligent fault diagnosis methods, the DL network has great performance in feature extraction and fault classification. Common examples of these DL methods include deep belief network [ 12 ], convolutional neural network [ 13 ], long and short-term memory neural network (LSTM) [ 14 ], deep convolutional autoencoder (DCAE) [ 15 ], etc.…”

Section: Introductionmentioning

confidence: 99%

Multilocation and Multiscale Learning Framework with Skip Connection for Fault Diagnosis of Bearing under Complex Working Conditions

Ban

Wang

et al. 2021

Sensors

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Considering various fault states under severe working conditions, the comprehensive feature extraction from the raw vibration signal is still a challenge for the diagnosis task of rolling bearing. To deal with strong coupling and high nonlinearity of the vibration signal, this article proposes a novel multilocation and multikernel scale learning network based on deep convolution encoder (DCE) and bidirectional short-term memory network (BiLSTM). The former multifeature learning network of this article proposed combines the skip connection and the DCE network. The network can automatically extract and fuse global and local features from different network depth and time scales of the raw vibration signal. Then, the former network as the input of the latter network is fed into the feature protection layer for further mining sensitive and complementary features. Consequently, the proposed network scheme can perform well in generalization capability. The performance of the proposed method is verified on the two kinds of bearing datasets. The diagnostic results demonstrate that the proposed method can diagnose multiple fault types more accurately. Also, the method performs better in load and speed adaptation compared with other intelligent fault classification methods.

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“…Wang et al 22 designed a unified neural network structure and achieved good model performance under different working loads. Zhao et al 23 constructed sparse autoencoder with gated recurrent unit and utilized grey wolf optimizer algorithm to optimize the key parameters in order to get better model performance by the experimental and practical bearing dataset. In the article, 24 Hsueh et al transformed original signals to grayscale images and designed a deep convolutional neural network (CNN) model to automatically extract deep feature maps from the grayscale images for fault diagnosis.…”

Section: Related Workmentioning

confidence: 99%

Deep domain adversarial residual neural network for sustainable wind turbine cyber‐physical system fault diagnosis

et al. 2021

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As a popular renewable energy generation technology, wind turbine system has become a critical enabler for building the sustainable cyber‐physical system (CPS). The main shaft bearing is an important part of the wind turbine CPS and often runs under variable working conditions. Thus, the reliable bearing diagnosis method can timely discover the main shaft bearing fault, which reduces the maintenance cost of wind turbines. Inspired by the idea of domain adaptation, we combined domain adversarial neural network and residual network and proposed a novel deep domain adversarial residual neural network (DDA‐RNN) for diagnosing bearing fault and improving model performance on the unlabeled dataset. This proposed software and hardware co‐design method was evaluated by our bearing dataset, which was collected from two wind turbine CPSs from Sanmenxia in Henan Province. Besides, F1 score and accuracy are served as model metrics, which reflect the diagnosis performance. Compared with other methods, the experimental results show that DDA‐RNN can improve model performance. Meanwhile, DDA‐RNN extracts diagnosis knowledge from labeled dataset and improves the model performance on the unlabeled dataset under different working condition. Therefore, the proposed method can be potentially used to benefit many practical scenarios in the future.

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An optimal deep sparse autoencoder with gated recurrent unit for rolling bearing fault diagnosis

Cited by 38 publications

References 44 publications

The Importance of Feature Processing in Deep-Learning-Based Condition Monitoring of Motors

The Importance of Feature Processing in Deep-Learning-Based Condition Monitoring of Motors

Multilocation and Multiscale Learning Framework with Skip Connection for Fault Diagnosis of Bearing under Complex Working Conditions

Deep domain adversarial residual neural network for sustainable wind turbine cyber‐physical system fault diagnosis

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