A convolutional neural network based on a capsule network with strong generalization for bearing fault diagnosis

Zhu, Zhiyu; Peng, Gaoliang; Chen, Yuanhang; Gao, Huijun

doi:10.1016/j.neucom.2018.09.050

Cited by 293 publications

(126 citation statements)

References 14 publications

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“…The complete details of the experimental datasets are described in Table 1. Ten types of time domain waveforms of bearing vibration signals, including normal signal, B fault signals (7,14,21), IR fault signals (7,14,21), and OR fault signals (7,14,21), can be seen in Figure 9.…”

Section: Data Descriptionmentioning

confidence: 99%

“…To keep good performance under different working conditions, the fault diagnosis model needs to have good generalization performance under different load and noise conditions [7].…”

mentioning

confidence: 99%

“…Although there has been no agreement on the source of the generalization ability of deep learning, it is no denying that the deep learning method has better generalization performance than most machine learning methods [21,22]. A large number of generalization experiments have been carried out, and the results show that the deep learning model has better generalization performance than the traditional machine learning algorithm [7,14,15,17,19]. From the previous literature, it can be concluded that the deep learning model has almost replaced the traditional feature extraction method and become the popular algorithm for bearing fault diagnosis.…”

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confidence: 99%

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A Deep Learning Method for Bearing Fault Diagnosis through Stacked Residual Dilated Convolutions

Zhuang

et al. 2019

Applied Sciences

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Real-time monitoring and fault diagnosis of bearings are of great significance to improve production safety, prevent major accidents, and reduce production costs. However, there are three primary concerns in the current research, namely real-time performance, effectiveness, and generalization performance. In this paper, a deep learning method based on stacked residual dilated convolutional neural network (SRDCNN) is proposed for real-time bearing fault diagnosis, which is subtly combined by the dilated convolution, the input gate structure of long short-term memory network (LSTM) and the residual network. In the SRDCNN model, the dilated convolution is used to exponentially increase the receptive field of convolution kernel and extract features from the sample with more points, alleviating the influence of randomness. The input gate structure of LSTM could effectively remove noise and control the entry of information contained in the input sample. Meanwhile, the residual network is introduced to overcome the problem of vanishing gradients caused by the deeper structure of the neural network, hence improving the overall classification accuracy. The experimental results indicate that compared with three excellent models, the proposed SRDCNN model has higher denoising ability and better workload adaptability.

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Section: Data Descriptionmentioning

confidence: 99%

“…To keep good performance under different working conditions, the fault diagnosis model needs to have good generalization performance under different load and noise conditions [7].…”

mentioning

confidence: 99%

mentioning

confidence: 99%

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A Deep Learning Method for Bearing Fault Diagnosis through Stacked Residual Dilated Convolutions

Zhuang

et al. 2019

Applied Sciences

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“…The BPNN model is the most popular one among various neural network algorithms and has been widely employed in different fields of fault diagnosis, such as power electronic system [34], transformer [35], battery [36,37], photovoltaic systems [38,39], etc. However, the BPNN model still has some intrinsic defects, for example, slow convergence speed and over-fitting problem [40][41][42][43]. Fortunately, a large collection of optimization algorithms have been developed to optimize the BPNN model, such as GA [44,45], MEA [46], particle swarm optimization (PSO) [47,48], simulated annealing (SA) [49], bat algorithm (BA) [50,51], etc.…”

Section: Introductionmentioning

confidence: 99%

A Novel Expertise-Guided Machine Learning Model for Internal Fault State Diagnosis of Power Transformers

Zhang

2019

Sustainability

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The fault diagnosis of power transformers is of great significance to improve the reliability of power systems. This paper proposes a novel fault diagnosis method called the expertise-guided machine learning (EGML) model where a genetic algorithm (GA) and a mind evolutionary algorithm (MEA) are used as optimization algorithms. Thereby, two types of EGML models are generated, that is, the GA-EGML model and the MEA-EGML model. In the EGML model, knowledge function replaces the cost function of traditional artificial intelligence algorithms, which can provide additional information for each individual and bring some corrections to the prediction results. To investigate the application potentials of the proposed models in power transformer fault diagnosis, real dissolved gases data are utilized to evaluate the diagnosis performance of the proposed models. Results indicate that the performance of the EGML model outperforms the traditional back propagation neural network (BPNN) model and all other models participating in the comparison. Both the GA-EGML model and MEA-EGML model can be used to diagnose the faults of a power transformer, and the latter is better. In addition, to further investigate the robustness of the proposed models for different data, four scenarios are simulated. Empirical results show that the accuracies of all models decrease in the other three scenarios compared to the baseline scenario, especially in scenario 2. However, the proposed models decline less than the traditional models in scenario 2 and scenario 4, and obtain satisfactory accuracy in all scenarios.

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“…In this paper, we aim at eliminating the influences of rotational speed between measurements automatically. The unique ability of capsule network (CN) in capturing the spatial variation of the data is explored [22]. A capsule network-based approach, in which two-directional signals are collected for training, is proposed.…”

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confidence: 99%

A Fault Diagnostic Scheme Based on Capsule Network for Rolling Bearing under Different Rotational Speeds

Zhang

Wang

2020

Sensors

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Deep learning-based intelligent fault diagnosis methods have attracted increasing attention for their automatic feature extraction ability. However, existing works are usually under the assumption that the training and test dataset share similar distributions, which unfortunately always violates real practice due to the variety of working conditions. In this paper, an end-to-end scheme of joint use of two-direction signals and capsule network (CN) is proposed for fault diagnosis of rolling bearing. With the help of the superior ability of CN in capturing the spatial position information between features, more valuable information can be mined. Aiming to eliminate the influence of different rotational speeds, vertical and horizontal vibration signals are fused as the input to CN, so that invariant features can be extracted automatically from the raw signals. The effectiveness of the proposed method is verified by experimental data of rolling bearing under different rotational speeds and compared with a deep convolutional neural network (DCNN). The results demonstrate that the proposed scheme is able to recognize the fault types of rolling bearing under scenarios of different rotational speeds.

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A convolutional neural network based on a capsule network with strong generalization for bearing fault diagnosis

Cited by 293 publications

References 14 publications

A Deep Learning Method for Bearing Fault Diagnosis through Stacked Residual Dilated Convolutions

A Deep Learning Method for Bearing Fault Diagnosis through Stacked Residual Dilated Convolutions

A Novel Expertise-Guided Machine Learning Model for Internal Fault State Diagnosis of Power Transformers

A Fault Diagnostic Scheme Based on Capsule Network for Rolling Bearing under Different Rotational Speeds

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