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 >

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 >

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

Debiao Meng^1,2,3,*, Hongtao Wang¹, Shiyuan Yang¹, Zhiyuan Lv¹, Zhengguo Hu¹, Zihao Wang¹

CMES-Computer Modeling in Engineering & Sciences, Vol.130, No.1, pp. 543-558, 2022, DOI:10.32604/cmes.2022.018123

Abstract In a wind turbine, the rolling bearing is the critical component. However, it has a high failure rate. Therefore, the failure analysis and fault diagnosis of wind power rolling bearings are very important to ensure the high reliability and safety of wind power equipment. In this study, the failure form and the corresponding reason for the failure are discussed firstly. Then, the natural frequency and the characteristic frequency are analyzed. The Empirical Mode Decomposition (EMD) algorithm is used to extract the characteristics of the vibration signal of the rolling bearing. Moreover, the eigenmode function is obtained and then filtered by… More >

Junyu Chen¹, Yunwen Feng^1,*, Cheng Lu^1,2, Chengwei Fei²

CMES-Computer Modeling in Engineering & Sciences, Vol.129, No.2, pp. 1013-1027, 2021, DOI:10.32604/cmes.2021.016980

Abstract As the key component in aeroengine rotor systems, the health status of rolling bearings directly influences the reliability and safety of aeroengine rotor systems. In order to monitor rolling bearing conditions, a fusion fault diagnosis method, namely empirical mode decomposition (EMD)-Mahalanobis distance (E2MD) and improved wavelet threshold (IWT) (E2MD-IWT) for vibrational signals and acoustic emission (AE) signals is developed to improve the diagnostic accuracy of rolling bearings. The IWT method is proposed with a hard wavelet threshold and a soft wavelet threshold. Moreover, it is shown to be effective through numerical simulation. EMD is utilized to process the original AE… More >

Xiaoping Zhao^{1, 4}, Yifei Wang^{2, *}, Yonghong Zhang², Jiaxin Wu¹, Yunqing Shi³

CMC-Computers, Materials & Continua, Vol.64, No.1, pp. 571-587, 2020, DOI:10.32604/cmc.2020.06363

Abstract Stochastic resonance can use noise to enhance weak signals, effectively reducing the effect of noise signals on feature extraction. In order to improve the early fault recognition rate of rolling bearings, and to overcome the shortcomings of lack of interaction in the selection of SR (Stochastic Resonance) method parameters and the lack of validation of the extracted features, an adaptive genetic random resonance early fault diagnosis method for rolling bearings was proposed. compared with the existing methods, the AGSR (Adaptive Genetic Stochastic Resonance) method uses genetic algorithms to optimize the system parameters, and further optimizes the parameters while considering the… More >

Yimeng Zhai¹, Aidong Deng^1,*, Jing Li^1,2, Qiang Cheng¹, Wei Ren³

Journal on Artificial Intelligence, Vol.1, No.1, pp. 19-27, 2019, DOI:10.32604/jai.2019.05817

Abstract In order to acquire the degradation state of rolling bearings and achieve predictive maintenance, this paper proposed a novel Remaining Useful Life (RUL) prediction of rolling bearings based on Long Short Term Memory (LSTM) neural net-work. The method is divided into two parts: feature extraction and RUL prediction. Firstly, a large number of features are extracted from the original vibration signal. After correlation analysis, the features that can better reflect the degradation trend of rolling bearings are selected as input of prediction model. In the part of RUL prediction, LSTM that making full use of the network’s memory in time… More >