Youlong Lyu^1,2,*, Ying Chu³, Qingpeng Qiu³, Jie Zhang^1,2, Jutao Guo⁴

CMC-Computers, Materials & Continua, Vol.85, No.2, pp. 3393-3418, 2025, DOI:10.32604/cmc.2025.068157 - 23 September 2025

Abstract In intelligent manufacturing processes such as aerospace production, computer numerical control (CNC) machine tools require real-time optimization of process parameters to meet precision machining demands. These dynamic operating conditions increase the risk of fatigue damage in CNC machine tool bearings, highlighting the urgent demand for rapid and accurate fault diagnosis methods that can maintain production efficiency and extend equipment uptime. However, varying conditions induce feature distribution shifts, and scarce fault samples limit model generalization. Therefore, this paper proposes a causal-Transformer-based meta-learning (CTML) method for bearing fault diagnosis in CNC machine tools, comprising three core modules:… More >

Mianyue Yang^1,*, Huasheng Sun¹, Weigao Sheng²

CMC-Computers, Materials & Continua, Vol.85, No.2, pp. 2749-2761, 2025, DOI:10.32604/cmc.2025.068009 - 23 September 2025

Abstract The shear pin of the friction pendulum bearing (FPB) can be made of 40Cr steel. In conceptual design, the optimal cut-off point of the shear pin is predetermined, guiding the design of bridges isolated by FPBs to maximize their isolation performance. Current researches on the shear pins are mainly based on linear elastic models, neglecting their plasticity, damage, and fracture mechanical properties. To accurately predict its cutoff behavior, the elastic-plastic degradation model of 40Cr steel is indeed calibrated. For this purpose, the Ramberg-Osgood model, the Bao-Wierzbicki damage initiation criterion, and the linear damage evolution criterion… More >

Yingyong Zou^*, Xingkui Zhang, Tao Liu, Yu Zhang, Long Li, Wenzhuo Zhao

CMC-Computers, Materials & Continua, Vol.83, No.3, pp. 4699-4723, 2025, DOI:10.32604/cmc.2025.062625 - 19 May 2025

Abstract High-speed train engine rolling bearings play a crucial role in maintaining engine health and minimizing operational losses during train operation. To solve the problems of low accuracy of the diagnostic model and unstable model due to the influence of noise during fault detection, a rolling bearing fault diagnosis model based on cross-attention fusion of WDCNN and BILSTM is proposed. The first layer of the wide convolutional kernel deep convolutional neural network (WDCNN) is used to extract the local features of the signal and suppress the high-frequency noise. A Bidirectional Long Short-Term Memory Network (BILSTM) is… More >

Yongfeng Tai¹, Xingyu Yan², Xiangyi Geng³, Lin Mu⁴, Mingshun Jiang², Faye Zhang^2,*

Structural Durability & Health Monitoring, Vol.19, No.2, pp. 365-383, 2025, DOI:10.32604/sdhm.2024.053998 - 15 January 2025

Abstract The remaining useful life prediction of rolling bearing is vital in safety and reliability guarantee. In engineering scenarios, only a small amount of bearing performance degradation data can be obtained through accelerated life testing. In the absence of lifetime data, the hidden long-term correlation between performance degradation data is challenging to mine effectively, which is the main factor that restricts the prediction precision and engineering application of the residual life prediction method. To address this problem, a novel method based on the multi-layer perception neural network and bidirectional long short-term memory network is proposed. Firstly,… More >

Chaozhi Cai^*, Tiexin Xu, Jianhua Ren, Yingfang Xue

Structural Durability & Health Monitoring, Vol.19, No.1, pp. 125-144, 2025, DOI:10.32604/sdhm.2024.052813 - 15 November 2024

Abstract A bearing fault diagnosis method based on the Markov transition field (MTF) and SEnet (SE)-IShufflenetV2 model is proposed in this paper due to the problems of complex working conditions, low fault diagnosis accuracy, and poor generalization of rolling bearing. Firstly, MTF is used to encode one-dimensional time series vibration signals and convert them into time-dependent and unique two-dimensional feature images. Then, the generated two-dimensional dataset is fed into the SE-IShufflenetV2 model for training to achieve fault feature extraction and classification. This paper selects the bearing fault datasets from Case Western Reserve University and Paderborn University… More >

Kai Wang^1,2,3, Lihua Yang^1,2,3,*

The International Conference on Computational & Experimental Engineering and Sciences, Vol.29, No.3, pp. 1-1, 2024, DOI:10.32604/icces.2024.011018

Abstract Although offset-halves journal bearings (OHJBs) are widely used in marine powertrains, the research on nonlinear tribo-dynamics is still limited, particularly under dynamic loading. To overcome such limitations, this study proposes a novel dynamic model that couples the influences of step load and thermoelastohydrodynamic (TEHD) effect. Based on the numerical model, a transient TEHD analysis for dynamically loaded OHJBs is done. Moreover, a modified stability criterion is developed. Nonlinear behaviors and transient stability of OHJBs under step load are systematically studied. The correlations of bearing characteristics such as the maximum film temperature, minimum film thickness, maximum More >

Sen-Hui Wang^1,2,*, Xi Kang¹, Cheng Wang¹, Tian-Bing Ma¹, Xiang He², Ke Yang^2,*

CMES-Computer Modeling in Engineering & Sciences, Vol.140, No.2, pp. 1405-1427, 2024, DOI:10.32604/cmes.2024.049281 - 20 May 2024

Abstract Accurately predicting the remaining useful life (RUL) of bearings in mining rotating equipment is vital for mining enterprises. This research aims to distinguish the features associated with the RUL of bearings and propose a prediction model based on these selected features. This study proposes a hybrid predictive model to assess the RUL of rolling element bearings. The proposed model begins with the pre-processing of bearing vibration signals to reconstruct sixty time-domain features. The hybrid model selects relevant features from the sixty time-domain features of the vibration signal by adopting the RReliefF feature selection algorithm. Subsequently,… 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 - 22 March 2024

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 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 - 17 November 2023

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 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 - 07 September 2023

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… More > Graphic Abstract