The Fault Diagnosis of Rolling Bearing Based on Improved Deep Forest

Qin, Xiwen; Xu, Dingxin; Dong, Xiaogang; Cui, Xueteng; Zhang, Siqi

doi:10.1155/2021/9933137

Cited by 12 publications

(6 citation statements)

References 38 publications

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“…Yin et al (2020) proposed WG-CNN based on adversarial neural networks and convolutional neural networks to enhance the model effect by using generators and antagonists. Qin et al (2021) proposed a rolling bearing fault diagnosis method based on the improved deep forest algorithm. Lv et al (2021) proposed a fault diagnosis method of rolling bearing based on multiscale convolutional neural network (MCNN) and decision fusion, it can extract deep general features of vibration signals by MCNN and obtained robust fault diagnosis results based on the decision fusion model although the above studies have realized the diagnosis of bearings fault.…”

Section: Multilabel Fault Diagnosis Model 401mentioning

confidence: 99%

The multilabel fault diagnosis model of bearing based on integrated convolutional neural network and gated recurrent unit

Han

Zhang

et al. 2021

IJICC

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PurposeIntelligent diagnosis of equipment faults can effectively avoid the shutdown caused by equipment faults and improve the safety of the equipment. At present, the diagnosis of various kinds of bearing fault information, such as the occurrence, location and degree of fault, can be carried out by machine learning and deep learning and realized through the multiclassification method. However, the multiclassification method is not perfect in distinguishing similar fault categories and visual representation of fault information. To improve the above shortcomings, an end-to-end fault multilabel classification model is proposed for bearing fault diagnosis.Design/methodology/approachIn this model, the labels of each bearing are binarized by using the binary relevance method. Then, the integrated convolutional neural network and gated recurrent unit (CNN-GRU) is employed to classify faults. Different from the general CNN networks, the CNN-GRU network adds multiple GRU layers after the convolutional layers and the pool layers.FindingsThe Paderborn University bearing dataset is utilized to demonstrate the practicability of the model. The experimental results show that the average accuracy in test set is 99.7%, and the proposed network is better than multilayer perceptron and CNN in fault diagnosis of bearing, and the multilabel classification method is superior to the multiclassification method. Consequently, the model can intuitively classify faults with higher accuracy.Originality/valueThe fault labels of each bearing are labeled according to the failure or not, the fault location, the damage mode and the damage degree, and then the binary value is obtained. The multilabel problem is transformed into a binary classification problem of each fault label by the binary relevance method, and the predicted probability value of each fault label is directly output in the output layer, which visually distinguishes different fault conditions.

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Section: Multilabel Fault Diagnosis Model 401mentioning

confidence: 99%

The multilabel fault diagnosis model of bearing based on integrated convolutional neural network and gated recurrent unit

Han

Zhang

et al. 2021

IJICC

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show abstract

“…This method focuses on evaluating feature extraction and fault identification from original vibration signals, while representing features in the time-domain, frequency-domain, and time-frequency-domain. Qin et al [13] introduced an improved deep forest algorithm. This method involves modifying the cascading mode based on multi-granularity scanning outputs and updating classifiers in the cascading stage to enhance classifier diversity and performance.…”

Section: Introductionmentioning

confidence: 99%

Dual-path multi-scale attention residual network for fault diagnosis of rolling bearings under complex operating conditions

Deng,

Zhang,

Zhao

et al. 2024

Meas. Sci. Technol.

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Rolling bearing faults inevitably occur during the long-term continuous operation of rotating machinery. Therefore, fault diagnosis is greatly important for ensuring the normal and safe operation of rolling bearings. However, the complexity and diversity of working conditions of rolling bearings present a significant challenge in extracting fault characteristics accurately, which further affects the ultimate fault diagnosis results. In this article, we propose a new model, called dual-path multi-scale attention residual network (DPMARN), for diagnosing bearing faults under complex operating conditions. DPMARN can effectively capture the feature-feature correlation information at different scales, which is more beneficial for fusing fault features at different scales to improve the model’s performance. The main contributions of this work are summarized as follows: (1) The designed dual-path network model which incorporates parallel multi-scale branches of convolutional kernels and serially connects skip-layer multi-scale branches can integrate both low-frequency and high-frequency information and enhance the multi-scale feature extraction and complex data representation abilities. (2) The SE attention mechanism is embedded into the residual blocks to improve the ability of learning feature correlations and utilizing feature information effectively, which is helpful for extracting important fault characteristics. Extensive experiments conducted on two public bearing datasets demonstrate the superior performance of the DPMARN model for addressing the complex fault diagnosis problem. These results indicate that our proposed approach provides an effective solution for fault diagnosis of rolling bearings under complex operating conditions.

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“…Zhang et al conducted a comparative study of various tree-based fault diagnosis models to demonstrate the accuracy of gcForest in creating a diagnosis with small samples [16]. It has been applied to hyperspectral image classification and in bearing fault diagnosis and other fields [17,18], achieving good results. Xu et al utilized a hybrid learning model of CNN and gcforest to diagnose bearing faults [19].…”

Section: Introductionmentioning

confidence: 99%

Intelligent Mechanical Fault Diagnosis Method for High-Voltage Circuit Breakers Based on Grey Wolf Optimization and Multi-Grained Cascade Forest Algorithms

Xu,

Yan,

Sui

et al. 2024

Applied Sciences

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High-voltage circuit breakers (HVCBs) handle the important tasks of controlling and safeguarding electricity networks. In the case of insufficient data samples, improving the accuracy of the traditional HVCB mechanical fault diagnosis method is difficult, so it poses challenges in meeting performance requirements for mechanical fault diagnosis. In this study, a HVCB fault diagnosis method is introduced. It utilizes a combination of grey wolf optimization (GWO) and multi-grained cascade forest (gcForest) algorithms to resolve these issues and improve the accuracy of HVCB mechanical fault diagnosis. To simplify the original vibration signal, the input feature quantity for the fault diagnosis method is obtained by calculating the energy entropy of the wavelet packet decomposition. The GWO algorithm is employed to optimize the parameters of the gcForest model, leading to identification of the optimum parameter configuration. Subsequently, the diagnostic effect in the case of a small sample size was analyzed through a VS1 vacuum circuit breaker example, and the accuracy reached 95.89%. In the case of unbalanced samples, further analysis and comparison with different methods confirm the feasibility and efficiency of the combination of GWO and gcForest algorithms. This study provides an effective solution for the diagnosis of mechanical faults in HVCBs.

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The Fault Diagnosis of Rolling Bearing Based on Improved Deep Forest

Cited by 12 publications

References 38 publications

The multilabel fault diagnosis model of bearing based on integrated convolutional neural network and gated recurrent unit

The multilabel fault diagnosis model of bearing based on integrated convolutional neural network and gated recurrent unit

Dual-path multi-scale attention residual network for fault diagnosis of rolling bearings under complex operating conditions

Intelligent Mechanical Fault Diagnosis Method for High-Voltage Circuit Breakers Based on Grey Wolf Optimization and Multi-Grained Cascade Forest Algorithms

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