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  • Open Access


    Bearing Fault Diagnosis with DDCNN Based on Intelligent Feature Fusion Strategy in Strong Noise

    Chaoqian He1,2, Runfang Hao1,2,*, Kun Yang1,2, Zhongyun Yuan1,2, Shengbo Sang1,2, Xiaorui Wang1,2

    CMC-Computers, Materials & Continua, Vol.77, No.3, pp. 3423-3442, 2023, DOI:10.32604/cmc.2023.045718

    Abstract Intelligent fault diagnosis in modern mechanical equipment maintenance is increasingly adopting deep learning technology. However, conventional bearing fault diagnosis models often suffer from low accuracy and unstable performance in noisy environments due to their reliance on a single input data. Therefore, this paper proposes a dual-channel convolutional neural network (DDCNN) model that leverages dual data inputs. The DDCNN model introduces two key improvements. Firstly, one of the channels substitutes its convolution with a larger kernel, simplifying the structure while addressing the lack of global information and shallow features. Secondly, the feature layer combines data from different sensors based on their… More >

  • Open Access


    Bearing Fault Diagnosis Based on Deep Discriminative Adversarial Domain Adaptation Neural Networks

    Jinxi Guo1, Kai Chen1,2, Jiehui Liu1, Yuhao Ma2, Jie Wu2,*, Yaochun Wu2, Xiaofeng Xue3, Jianshen Li1

    CMES-Computer Modeling in Engineering & Sciences, Vol.138, No.3, pp. 2619-2640, 2024, DOI:10.32604/cmes.2023.031360

    Abstract Intelligent diagnosis driven by big data for mechanical fault is an important means to ensure the safe operation of equipment. In these methods, deep learning-based machinery fault diagnosis approaches have received increasing attention and achieved some results. It might lead to insufficient performance for using transfer learning alone and cause misclassification of target samples for domain bias when building deep models to learn domain-invariant features. To address the above problems, a deep discriminative adversarial domain adaptation neural network for the bearing fault diagnosis model is proposed (DDADAN). In this method, the raw vibration data are firstly converted into frequency domain… More >

  • Open Access


    WDBM: Weighted Deep Forest Model Based Bearing Fault Diagnosis Method

    Letao Gao1,*, Xiaoming Wang2, Tao Wang3, Mengyu Chang4

    CMC-Computers, Materials & Continua, Vol.72, No.3, pp. 4741-4754, 2022, DOI:10.32604/cmc.2022.027204

    Abstract In the research field of bearing fault diagnosis, classical deep learning models have the problems of too many parameters and high computing cost. In addition, the classical deep learning models are not effective in the scenario of small data. In recent years, deep forest is proposed, which has less hyper parameters and adaptive depth of deep model. In addition, weighted deep forest (WDF) is proposed to further improve deep forest by assigning weights for decisions trees based on the accuracy of each decision tree. In this paper, weighted deep forest model-based bearing fault diagnosis method (WDBM) is proposed. The WDBM… More >

  • Open Access


    Bearing Fault Diagnosis Method of Wind Turbine Based on Improved Anti-Noise Residual Shrinkage Network

    Xiaolei Li*

    Energy Engineering, Vol.119, No.2, pp. 665-680, 2022, DOI:10.32604/ee.2022.019292

    Abstract Aiming at the difficulty of rolling bearing fault diagnosis of wind turbine under noise environment, a new bearing fault identification method based on the Improved Anti-noise Residual Shrinkage Network (IADRSN) is proposed. Firstly, the vibration signals of wind turbine rolling bearings were preprocessed to obtain data samples divided into training and test sets. Then, a bearing fault diagnosis model based on the improved anti-noise residual shrinkage network was established. To improve the ability of fault feature extraction of the model, the convolution layer in the deep residual shrinkage network was replaced with a Dense-Net layer. To further improve the anti-noise… More >

  • Open Access


    A Novel Method Based on UNET for Bearing Fault Diagnosis

    Dileep Kumar1,*, Imtiaz Hussain Kalwar2, Tanweer Hussain1, Bhawani Shankar Chowdhry1, Sanaullah Mehran Ujjan1, Tayab Din Memon3

    CMC-Computers, Materials & Continua, Vol.69, No.1, pp. 393-408, 2021, DOI:10.32604/cmc.2021.014941

    Abstract Reliability of rotating machines is highly dependent on the smooth rolling of bearings. Thus, it is very essential for reliable operation of rotating machines to monitor the working condition of bearings using suitable fault diagnosis and condition monitoring approach. In the recent past, Deep Learning (DL) has become applicable in condition monitoring of rotating machines owing to its performance. This paper proposes a novel bearing fault diagnosis method based on the processing and analysis of the vibration images. The proposed method is the UNET model that is a recent development in DL models. The model is applied to the 2D… More >

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