Siyuan Liu^1,*, Jinying Huang², Jiancheng Ma¹, Licheng Jing², Yuxuan Wang²

CMC-Computers, Materials & Continua, Vol.79, No.1, pp. 761-777, 2024, DOI:10.32604/cmc.2024.049484

Abstract Recent research in cross-domain intelligence fault diagnosis of machinery still has some problems, such as relatively ideal speed conditions and sample conditions. In engineering practice, the rotational speed of the machine is often transient and time-varying, which makes the sample annotation increasingly expensive. Meanwhile, the number of samples collected from different health states is often unbalanced. To deal with the above challenges, a complementary-label (CL) adversarial domain adaptation fault diagnosis network (CLADAN) is proposed under time-varying rotational speed and weakly-supervised conditions. In the weakly supervised learning condition, machine prior information is used for sample annotation via cost-friendly complementary label learning.… More >

Xukai Ren^1,2,*, Huanwei Yu², Xianfeng Chen², Yantong Tang², Guobiao Wang^1,*, Xiyong Du²

CMES-Computer Modeling in Engineering & Sciences, Vol.140, No.1, pp. 647-663, 2024, DOI:10.32604/cmes.2024.048782

Abstract Stirred reactors are key equipment in production, and unpredictable failures will result in significant economic losses and safety issues. Therefore, it is necessary to monitor its health state. To achieve this goal, in this study, five states of the stirred reactor were firstly preset: normal, shaft bending, blade eccentricity, bearing wear, and bolt looseness. Vibration signals along x, y and z axes were collected and analyzed in both the time domain and frequency domain. Secondly, 93 statistical features were extracted and evaluated by ReliefF, Maximal Information Coefficient (MIC) and XGBoost. The above evaluation results were then fused by D-S evidence… More >

Haomiao Wang¹, Jinxi Wang², Qingmei Sui^2,*, Faye Zhang², Yibin Li¹, Mingshun Jiang², Phanasindh Paitekul³

Structural Durability & Health Monitoring, Vol.18, No.2, pp. 91-110, 2024, DOI:10.32604/sdhm.2023.041522

Abstract Due to their robust learning and expression ability for complex features, the deep learning (DL) model plays a vital role in bearing fault diagnosis. However, since there are fewer labeled samples in fault diagnosis, the depth of DL models in fault diagnosis is generally shallower than that of DL models in other fields, which limits the diagnostic performance. To solve this problem, a novel transfer residual Swin Transformer (RST) is proposed for rolling bearings in this paper. RST has 24 residual self-attention layers, which use the hierarchical design and the shifted window-based residual self-attention. Combined with transfer learning techniques, the… More >

Kwok Tai Chui^1,*, Brij B. Gupta^2,3,4,5,6,*, Varsha Arya^7,8,9, Miguel Torres-Ruiz¹⁰

CMC-Computers, Materials & Continua, Vol.78, No.1, pp. 1363-1379, 2024, DOI:10.32604/cmc.2023.046762

Abstract The visions of Industry 4.0 and 5.0 have reinforced the industrial environment. They have also made artificial intelligence incorporated as a major facilitator. Diagnosing machine faults has become a solid foundation for automatically recognizing machine failure, and thus timely maintenance can ensure safe operations. Transfer learning is a promising solution that can enhance the machine fault diagnosis model by borrowing pre-trained knowledge from the source model and applying it to the target model, which typically involves two datasets. In response to the availability of multiple datasets, this paper proposes using selective and adaptive incremental transfer learning (SA-ITL), which fuses three… More >

Jiajie He^1,2, Fuzheng Liu³, Xiangyi Geng³, Xifeng Liang¹, Faye Zhang^3,*, Mingshun Jiang³

Structural Durability & Health Monitoring, Vol.18, No.1, pp. 37-54, 2024, DOI:10.32604/sdhm.2023.029428

Abstract Incomplete fault signal characteristics and ease of noise contamination are issues with the current rolling bearing early fault diagnostic methods, making it challenging to ensure the fault diagnosis accuracy and reliability. A novel approach integrating enhanced Symplectic geometry mode decomposition with cosine difference limitation and calculus operator (ESGMD-CC) and artificial fish swarm algorithm (AFSA) optimized extreme learning machine (ELM) is proposed in this paper to enhance the extraction capability of fault features and thus improve the accuracy of fault diagnosis. Firstly, SGMD decomposes the raw vibration signal into multiple Symplectic geometry components (SGCs). Secondly, the iterations are reset by the… More >

Chaoqian He^1,2, Runfang Hao^1,2,*, Kun Yang^1,2, Zhongyun Yuan^1,2, Shengbo Sang^1,2, Xiaorui Wang^1,2

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 >

Xiangqun Li^1,*, Jiawen Liang², Jinyu Zhu², Shengping Shi², Fangyu Ding², Jianpeng Sun², Bo Liu²

Energy Engineering, Vol.121, No.1, pp. 203-219, 2024, DOI:10.32604/ee.2023.029295

Abstract To solve the problems of a few optical fibre line fault samples and the inefficiency of manual communication optical fibre fault diagnosis, this paper proposes a communication optical fibre fault diagnosis model based on variational modal decomposition (VMD), fuzzy entropy (FE) and fuzzy clustering (FC). Firstly, based on the OTDR curve data collected in the field, VMD is used to extract the different modal components (IMF) of the original signal and calculate the fuzzy entropy (FE) values of different components to characterize the subtle differences between them. The fuzzy entropy of each curve is used as the feature vector, which… More >

Jinxi Guo¹, Kai Chen^1,2, Jiehui Liu¹, Yuhao Ma², Jie Wu^2,*, 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 >

Cuihong Li¹, Qiuwei Yang^2,3,*, Xi Peng^2,3

CMES-Computer Modeling in Engineering & Sciences, Vol.138, No.3, pp. 2769-2791, 2024, DOI:10.32604/cmes.2023.030908

Abstract Shear-type structures are common structural forms in industrial and civil buildings, such as concrete and steel frame structures. Fault diagnosis of shear-type structures is an important topic to ensure the normal use of structures. The main drawback of existing damage assessment methods is that they require accurate structural finite element models for damage assessment. However, for many shear-type structures, it is difficult to obtain accurate FEM. In order to avoid finite element modeling, a model-free method for diagnosing shear structure defects is developed in this paper. This method only needs to measure a few low-order vibration modes of the structure.… More >

Youming Guo, Qinmu Wu^*

CMC-Computers, Materials & Continua, Vol.77, No.2, pp. 1489-1514, 2023, DOI:10.32604/cmc.2023.042313

Abstract Permanent magnet synchronous motor (PMSM) is widely used in various production processes because of its high efficiency, fast reaction time, and high power density. With the continuous promotion of new energy vehicles, timely detection of PMSM faults can significantly reduce the accident rate of new energy vehicles, further enhance consumers’ trust in their safety, and thus promote their popularity. Existing fault diagnosis methods based on deep learning can only distinguish different PMSM faults and cannot interpret and analyze them. Convolutional neural networks (CNN) show remarkable accuracy in image data analysis. However, due to the “black box” problem in deep learning… More >