Deep learning for bearing fault diagnosis under different working loads and non-fault location point

Wang, Chong-Yu; Xie, Yuanyuan; Zhang, Di

doi:10.1177/1461348419889511

Cited by 12 publications

(11 citation statements)

References 25 publications

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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.…”

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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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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“…Comparing the transfer results of SVM, RF, TCA, JDA, TiCNN (Zhang et al, 2018), ResNet (Zhang et al, 2019), DDC (Wang et al, 2021), and ours on several tasks. As shown in Table 3, TiCNN and ResNet methods reach the lowest accuracy.…”

Section: The Influence Of Batch Size and The Comparison Of Different ...mentioning

confidence: 99%

“…Furthermore, this article uses the deep convolution residual network proposed by Kaiming et al (2016) as standard and then designs the network on this basis. Research on fault diagnosis and the residual network has been proved to be effective in bearing (Zhang et al, 2019), gear (Ma et al, 2019), transfer fault diagnosis (Wang et al, 2021), and so on.…”

Section: Introductionmentioning

confidence: 99%

A sub-domain adaptive transfer learning base on residual network for bearing fault diagnosis

Wang

Zhu

Liu

et al. 2021

Journal of Vibration and Control

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Bearing fault diagnosis is an important research field for rotating machinery health monitoring. Recently, many intelligent fault diagnosis methods driven by big data, such as transfer learning, have been studied. However, there are two shortcomings for the prior transfer learning method in industry application. First, it is necessary to design a complex loss function to enhance the similarity between the two domains further. Second, previous studies required big data both in source and target task, without considering the lack of sufficient training samples. Inspired by relevant research work, this article proposes a local joint distribution discrepancy to increase similar features. A sub-domain adaptive transfer learning is designed to detect bearing faults based on the residual network. Two kinds of transfer experiments are designed to verify the method effectiveness. After that, the impact of small training samples and noise on the results is explored. The proposed method reaches high accuracy.

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“…Bearing has been broadly used in rotating machinery and plays a significant role in the mechanical system in under complex and variable surroundings. Fault diagnosis (FD) of bearing is essential to reduce the incidence of catastrophic failures and heavy economic losses, and bearing FD has attracted increasing attention from both academia and industry fields [1][2][3].…”

Section: Introductionmentioning

confidence: 99%

Bearing Fault Diagnosis Under Small Data Set Condition: A Bayesian Network Method With Transfer Learning for Parameter Estimation

et al. 2022

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Bearings are broadly applied in various types of industrial systems. Fault diagnosis, as a promising way for reliability of modern industrial internet of thing applications, has attracted increasing attention from both academia and industry fields. Being ideal modeling and inference tool in uncertainty situations, Bayesian network (BN) is becoming increasingly popular in many systems. However, in practical uncertain and complicated engineering surroundings, it's difficult or expensive to collect massive labeled fault data for the sake of fault diagnosis model learning. To address the issue of BN parameter learning under small data set conditions, this paper proposes a Varying Coefficient Transfer Learning (VCTL) algorithm based on aggregation and transfer learning, that considers both knowledge from the resource domain and the resource relevance contributions. The balancing weight function is designed to determine whether the learning task in the resource domain is activated. Relevance weight factors are proposed to measure the relevance of resource and target parameters quantitatively, by combing parameter information from resource domains with those obtained from the target domain, using maximum a posterior (MAP) or maximum likelihood estimation (MLE). Finally, the target parameters are aggregated with both the target initial parameters and the parameter knowledge from the resource domain. Based on VCTL, a bearing fault diagnosis approach is proposed and verified. The experimental results show that, under the condition of the small data set, learning accuracy of VCTL algorithm with varying coefficient aggregation is better than MLE algorithm, MAP algorithm or state-of-the-art parameter transfer method, local linear pooling transfer learning (LoLP) algorithm. Under the condition of sufficient data set, learning accuracy of VCTL algorithm approaches the classical MLE or MAP, and the correctness of the proposed algorithm is verified. Moreover, we illustrate the successful application to real-world bearing fault diagnosis case with VCTL, where we had access to expert-provided resource knowledge and real fault diagnosis data.

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Deep learning for bearing fault diagnosis under different working loads and non-fault location point

Cited by 12 publications

References 25 publications

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

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

A sub-domain adaptive transfer learning base on residual network for bearing fault diagnosis

Bearing Fault Diagnosis Under Small Data Set Condition: A Bayesian Network Method With Transfer Learning for Parameter Estimation

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