Home / Advanced Search

  • Title/Keywords

  • Author/Affliations

  • Journal

  • Article Type

  • Start Year

  • End Year

Update SearchingClear
  • Articles
  • Online
Search Results (6)
  • Open Access


    Predicting Reliability and Remaining Useful Life of Rolling Bearings Based on Optimized Neural Networks

    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

    Predicting Reliability and Remaining Useful Life of Rolling Bearings Based on Optimized Neural Networks

  • Open Access


    Dense-Structured Network Based Bearing Remaining Useful Life Prediction System

    Ping-Huan Kuo1,2, Ting-Chung Tseng1, Po-Chien Luan2, Her-Terng Yau1,2,*

    CMES-Computer Modeling in Engineering & Sciences, Vol.133, No.1, pp. 133-151, 2022, DOI: 10.32604/cmes.2022.020350

    Abstract This work is focused on developing an effective method for bearing remaining useful life predictions. The method is useful in accurately predicting the remaining useful life of bearings so that machine damage, production outage, and human accidents caused by unexpected bearing failure can be prevented. This study uses the bearing dataset provided by FEMTO-ST Institute, Besançon, France. This study starts with the exploration of neural networks, based on which the biaxial vibration signals are modeled and analyzed. This paper introduces pre-processing of bearing vibration signals, neural network model training and adjustment of training data. The model is trained by optimizing… More >

  • Open Access


    A TimeImageNet Sequence Learning for Remaining Useful Life Estimation of Turbofan Engine in Aircraft Systems

    S. Kalyani*, K. Venkata Rao, A. Mary Sowjanya

    Structural Durability & Health Monitoring, Vol.15, No.4, pp. 317-334, 2021, DOI:10.32604/sdhm.2021.016975

    Abstract Internet of Things systems generate a large amount of sensor data that needs to be analyzed for extracting useful insights on the health status of the machine under consideration. Sensor data of all possible states of a system are used for building machine learning models. These models are further used to predict the possible downtime for proactive action on the system condition. Aircraft engine data from run to failure is used in the current study. The run to failure data includes states like new installation, stable operation, first reported issue, erroneous operation, and final failure. In the present work, the… More >

  • Open Access


    Effective Latent Representation for Prediction of Remaining Useful Life

    Qihang Wang, Gang Wu*

    Computer Systems Science and Engineering, Vol.36, No.1, pp. 225-237, 2021, DOI:10.32604/csse.2021.014100

    Abstract AI approaches have been introduced to predict the remaining useful life (RUL) of a machine in modern industrial areas. To apply them well, challenges regarding the high dimension of the data space and noisy data should be met to improve model efficiency and accuracy. In this study, we propose an end-to-end model, termed ACB, for RUL predictions; it combines an autoencoder, convolutional neural network (CNN), and bidirectional long short-term memory. A new penalized root mean square error loss function is included to avoid an overestimation of the RUL. With the CNN-based autoencoder, a high-dimensional data space can be mapped into… More >

  • Open Access


    Robust Remaining Useful Life Estimation Based on an Improved Unscented Kalman Filtering Method

    Shenkun Zhao, Chao Jiang*, Zhe Zhang, Xiangyun Long

    CMES-Computer Modeling in Engineering & Sciences, Vol.123, No.3, pp. 1151-1173, 2020, DOI:10.32604/cmes.2020.08867

    Abstract In the Prognostics and Health Management (PHM), remaining useful life (RUL) is very important and utilized to ensure the reliability and safety of the operation of complex mechanical systems. Recently, unscented Kalman filtering (UKF) has been applied widely in the RUL estimation. For a degradation system, the relationship between its monitored measurements and its degradation states is assumed to be nonlinear in the conventional UKF. However, in some special degradation systems, their monitored measurements have a linear relation with their degradation states. For these special problems, it may bring estimation errors to use the UKF method directly. Besides, many uncertain… More >

  • Open Access


    Remaining Useful Life Prediction of Rolling Bearings Based on Recurrent Neural Network

    Yimeng Zhai1, Aidong Deng1,*, Jing Li1,2, 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 >

Displaying 1-10 on page 1 of 6. Per Page