Highlights

  • 【 Cover Article 】 Deng Qingtian's team from Chang'an University: Study on the crack expansion low of laminated porous structure
  • MODELING AND CARRYING CAPACITY ANALYSIS OF CIRCULAR ARC TOOTH PROFILE CIRCULAR ARC TOOTH LINE CYLINDRICAL GEAR
  • RESEARCH ABOUT FAULT DIAGNOSIS OF BEARING BASED ON INSTRINSIC TIME SCALE DECOMPOSITION AND CONVOLUTIONAL NEURAL NETWORK

Quick Entry

  • Author Login Author Login
  • Peer Review Peer Review
  • Editor Login Editor Login

Journal of Mechanical Strength

Journal of Mechanical Strength Journal of Mechanical Strength
  • ISSN: 1001-9669
  • CN: 41-1134/TH
  • Governed by: China Machinery Industry Federation
  • Sponsored by: China Academy of Machinery Zhengzhou Research Institute of Mechanical Engineering Co., Ltd. ；Chinese Mechanical Engineering Society
  • Frequency：Monthly
  • Tel：0371-67710821
  • Address: No.149, Kexue, Avenue, Zhengzhou,Henan, China
  • E-mail：jxqd1975@foxmail.com
  • Latest Articles
  • Online First
  • Archive
更多
Volume 48 期 5，2026 2026年第48卷第5期

  • ·Fatigue·Damage·Fracture·Failure Analysis·

    SONG Debo, QI Wenzhe, QI Jinping

    DOI：10.16579/j.issn.1001.9669.2026.05.001
    摘要：ObjectiveAiming at the problem that wind power bearings are interfered by nonlinear factors and non-Gaussian noise during the monitoring process, and the degradation showing stage characteristics, a model combining adaptive particle filtering with a multi-stage nonlinear Wiener process was constructed.MethodsFirstly, the Wiener process was introduced into the state-space model of particle filtering to enhance the nonlinear expression ability of the model. Meanwhile, the sampling process was adjusted using an adaptive sampling strategy to effectively avoid particle degradation, thus improving the accuracy of the state estimation and correcting the bearing degradation index. Secondly, a degradation model of bearings was established based on the multi-stage nonlinear Wiener process, and the drift coefficients were randomized by taking into account the variability of different individual bearings. The segmentation points of the degradation stages of bearings were determined by the cumulative sum algorithm, and the model parameters were estimated using the maximum likelihood estimation algorithm. Finally, verification and analysis were conducted on the test bench data and the monitoring data of the free-end bearings of the wind turbine, and the remaining life prediction results obtained by the proposed method were compared with those obtained from the degradation index without filtering correction.ResultsThe results show that the proposed method has higher prediction accuracy.  
    关键词：Wind turbine;Rolling bearing;Adaptive particle filtering;Multi-stage nonlinear Wiener process;Residual life prediction   
    37
    |
    6
    |
    0
    <HTML>
    <L-PDF>
    <引用本文> <批量引用> 156705912 false
    更新时间：2026-05-19

    PAN Zuozhou, WU Yiding, HU Yicheng, PAN Xiyu, ZHAO Peng, JIANG Fei

    DOI：10.16579/j.issn.1001.9669.2026.05.002
    摘要：ObjectiveAiming at the problems of harsh working environment and weak early degradation trend of rolling bearings, which lead to great difficulty in fault feature extraction and low prediction accuracy, a reinforced diagnosis model based on cumulative transformation was proposed for the remaining useful life (RUL) prediction of rolling bearings.MethodsFirstly, a feature enhancement method based on cumulative transformation was proposed, where the extracted features were converted into the corresponding cumulative transformation form to improve the sensitivity of the original features. Secondly, a new health index based on cumulative features was constructed, and the continuous trigger mechanism algorithm was used to divide the states of the health index to determine the initial fault occurrence point. Finally, the skip connection module of the ResNet was enhanced and additional calibration channels were added to improve the network’s ability to focus on key degradation features, and then the stacked long short-term memory with Kolmogorov-Arnold network module (KSLSTM) network was used to obtain full spatiotemporal features for the accurate prediction of bearing RUL.ResultsThe results show that the prediction accuracy of bearing RUL in the small-sample training environment is significantly improved by constructing cumulative transformation features and optimizing the network structure, and the effectiveness of the method is verified by simulations and tests.  
    关键词：Rolling-element bearing;Cumulative feature;EResNet;KSLSTM;RUL prediction   
    17
    |
    5
    |
    0
    <HTML>
    <L-PDF>
    <引用本文> <批量引用> 156706070 false
    更新时间：2026-05-19

    DING Mingchao, BIAN Shuguang, CAI Long, HUANG Jiaxing, YANG Lipo, WANG Liming

    DOI：10.16579/j.issn.1001.9669.2026.05.003
    摘要：ObjectiveThe Neuber criterion and the equivalent strain energy density (ESED) criterion serve as fundamental theories for evaluating notch strain and predicting fatigue life. However, few studies have been reported the influence of the remaining thickness at the notch root on the fatigue life prediction results of specimens with surface notches. Optimizing the accuracy of fatigue life prediction for notched components is crucial for assessing the fatigue life of TC4 titanium alloy specimens with surface notches.MethodsFirstly, TC4 titanium alloy specimens with surface notches on the substrate were designed, and fatigue tests were carried out to obtain tested fatigue life values. Secondly, the strains at the notch were calculated using the Neuber criterion and the ESED criterion respectively. Fatigue life prediction was performed in combination with the Manson-Coffin formula, and the fatigue life prediction accuracy based on the two criteria was verified. Finally, the ESED criterion was modified, and the accuracy of fatigue life prediction results based on the revised ESED criterion was analyzed.ResultsFatigue life prediction results based on the Neuber criterion generally fall within the 2 times scatter band, without excessive conservatism. Moreover, as the notch width increases, the predictions shift from conservative to nonconservative. Fatigue life prediction results using the unmodified ESED criterion all lie outside the 8 times scatter band and exhibit significant overprediction. A strong correlation exists between the remaining thickness to radius of curvature at the notch root, and the tested fatigue life. The modified ESED criterion proposed based on this correlation yields fatigue life prediction results that mainly lie within the 2 times scatter band, with overall conservative estimates, and the prediction accuracy and reliability are significantly improved.  
    关键词：Neuber rule;ESED rule;Surface notch;Limited remaining thickness;Fatigue life prediction   
    26
    |
    5
    |
    0
    <HTML>
    <L-PDF>
    <引用本文> <批量引用> 156705597 false
    更新时间：2026-05-19

    YUAN Kai, LI Zhinong, ZHANG Xin, LONG Shengrong, LI Zhe

    DOI：10.16579/j.issn.1001.9669.2026.05.004
    摘要：ObjectiveElectromagnetic testing of wire ropes is often compromised by significant noise interference and the limited accuracy of traditional synchroextracting transforms in estimating instantaneous frequencies under strong noise conditions.MethodsA signal processing method based on higher-order synchroextracting transform (HSET) was proposed. The signal's amplitude and phase were expanded into high-order Taylor series to derive high-order instantaneous frequencies. By combining these frequencies with a delta function, a higher-order synchroextracting operator was developed to retain critical time-frequency information while eliminating redundant energy, followed by signal reconstruction.ResultsThe test results demonstrate that the proposed method effectively reduces spikes and abrupt changes in the detected signal, resulting in a more stable signal profile. Furthermore, it increases signal peaks at singularity points, providing a robust foundation for the quantitative identification of wire rope damage.  
    关键词：Wire rope;Higher-order synchroextracting transform;Time-frequency analysis;Nondestructive testing;Back propagation neural network   
    18
    |
    4
    |
    0
    <HTML>
    <L-PDF>
    <引用本文> <批量引用> 156705951 false
    更新时间：2026-05-19
查看更多
  • Special Issues
更多
Fault diagnosis and monitoring

Fault diagnosis and monitoring

Reliability

Reliability

Gears

Gears

Rolling bearing

Rolling bearing

0