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 >

Kuosheng Jiang¹, Chengrui Han¹, Yasheng Chang^2,3,*

CMES-Computer Modeling in Engineering & Sciences, Vol.139, No.3, pp. 3329-3352, 2024, DOI:10.32604/cmes.2024.046729

Abstract Angular contact ball bearings have been widely used in machine tool spindles, and the bearing preload plays an important role in the performance of the spindle. In order to solve the problems of the traditional optimal preload prediction method limited by actual conditions and uncertainties, a roller bearing preload test method based on the improved D-S evidence theory multi-sensor fusion method was proposed. First, a novel controllable preload system is proposed and evaluated. Subsequently, multiple sensors are employed to collect data on the bearing parameters during preload application. Finally, a multisensor fusion algorithm is used to make predictions, and a… More >

Qifeng Shan^1,2, Ming Mao², Yushun Li^3,*

Journal of Renewable Materials, Vol.12, No.1, pp. 149-166, 2024, DOI:10.32604/jrm.2023.029445

Abstract A theoretical analysis of upward deflection and midspan deflection of prestressed bamboo-steel composite beams is presented in this study. The deflection analysis considers the influences of interface slippage and shear deformation. Furthermore, the calculation model for flexural capacity is proposed considering the two stages of loading. The theoretical results are verified with 8 specimens considering different prestressed load levels, load schemes, and prestress schemes. The results indicate that the proposed theoretical analysis provides a feasible prediction of the deflection and bearing capacity of bamboo-steel composite beams. For deflection analysis, the method considering the slippage and shear deformation provides better accuracy.… 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 >

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 >

Ali Mahamied¹, Amjad A. Yasin¹, Yazan Alzubi^1,*, Jamal Al Adwan¹, Issa Mahamied²

Structural Durability & Health Monitoring, Vol.17, No.6, pp. 501-519, 2023, DOI:10.32604/sdhm.2023.028686

Abstract Nowadays, an extensive number of studies related to the performance of base isolation systems implemented in regular reinforced concrete structures subjected to various types of earthquakes can be found in the literature. On the other hand, investigations regarding the irregular base-isolated reinforced concrete structures’ performance when subjected to pulse-like earthquakes are very scarce. The severity of pulse-like earthquakes emerges from their ability to destabilize the base-isolated structure by remarkably increasing the displacement demands. Thus, this study is intended to investigate the effects of pulse-like earthquake characteristics on the behavior of low-rise irregular base-isolated reinforced concrete structures. Within the study scope,… More >

Tiantian Liang^*, Runze Wang, Xuxiu Zhang, Yingdong Wang, Jianxiong Yang

Structural Durability & Health Monitoring, Vol.17, No.5, pp. 433-455, 2023, DOI:10.32604/sdhm.2023.029331

Abstract In this study, an optimized long short-term memory (LSTM) network is proposed to predict the reliability and remaining useful life (RUL) of rolling bearings based on an improved whale-optimized algorithm (IWOA). The multi-domain features are extracted to construct the feature dataset because the single-domain features are difficult to characterize the performance degeneration of the rolling bearing. To provide covariates for reliability assessment, a kernel principal component analysis is used to reduce the dimensionality of the features. A Weibull distribution proportional hazard model (WPHM) is used for the reliability assessment of rolling bearing, and a beluga whale optimization (BWO) algorithm is… More > Graphic Abstract

Jialuo Rong^1,2, Shuixiang Xie^3,4, Huijing Geng⁵, Hao Hu^1,2, Shanfa Tang^1,2,*, Yuanpeng Cheng^1,2,*

Energy Engineering, Vol.120, No.8, pp. 1899-1917, 2023, DOI:10.32604/ee.2023.027650

Abstract Due to its extensive use in shale gas exploration and development, oil-based drilling fluids generate large amounts of oil-bearing drill cuttings during the drilling process. The large amount of oil-bearing drill cuttings generated during the drilling process can lead to serious secondary contamination. In this study, a wetting agent FSC-6 with good hydrophobic and oleophobic properties was synthesized to construct an efficient oil removal system. For the first time, the mechanism of this system was analyzed by using the theory of adhesion function, interfacial tension and wettability. At the same time, a combined acoustic-chemical treatment process was applied to the… More >

Vladimir Poplygin^1,*, Chengzhi Qi², Mikhail Guzev³, Evgenii Kozhevnikov¹, Artem Kunitskikh¹, Evgenii Riabokon¹, Mikhail Turbakov¹

FDMP-Fluid Dynamics & Materials Processing, Vol.19, No.6, pp. 1495-1505, 2023, DOI:10.32604/fdmp.2023.022335

Abstract A set of techniques for well treatment aimed to enhance oil recovery are considered in the present study. These are based on the application of elastic waves of various types (dilation-wave, vibro-wave, or other acoustically induced effects). In such a context, a new technique is proposed to predict the effectiveness of the elastic-wave well treatment using the rank distribution according to Zipf’s law. It is revealed that, when the results of elastic wave well treatments are analyzed, groups of wells exploiting various geological deposits can differ in terms of their slope coefficients and free members. As the slope coefficient increases,… More >