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Accurate bearing remaining useful life prediction based on Weibull distribution and artificial neural network

机译：基于Weibull分布和人工神经网络的精确轴承剩余使用寿命预测

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摘要

Accurate remaining useful life (RUL) prediction of critical assets is an important challenge in condition based maintenance to improve reliability and decrease machine's breakdown and maintenance's cost. Bearing is one of the most important components in industries which need to be monitored and the user should predict its RUL. The challenge of this study is to propose an original feature able to evaluate the health state of bearings and to estimate their RUL by Prognostics and Health Management (PHM) techniques. In this paper, the proposed method is based on the data-driven prognostic approach. The combination of Simplified Fuzzy Adaptive Resonance Theory Map (SFAM) neural network and Weibull distribution (WD) is explored. WD is used just in the training phase to fit measurement and to avoid areas of fluctuation in the time domain. SFAM training process is based on fitted measurements at present and previous inspection time points as input. However, the SFAM testing process is based on real measurements at present and previous inspections. Thanks to the fuzzy learning process, SFAM has an important ability and a good performance to learn nonlinear time series. As output, seven classes are defined; healthy bearing and six states for bearing degradation. In order to find the optimal RUL prediction, a smoothing phase is proposed in this paper. Experimental results show that the proposed method can reliably predict the RUL of rolling element bearings (REBs) based on vibration signals. The proposed prediction approach can be applied to prognostic other various mechanical assets.
机译：关键资产的准确剩余使用寿命（RUL）预测是基于状态的维护的一项重要挑战，要提高可靠性并降低机器的故障和维护成本。轴承是行业中最重要的组件之一，需要对其进行监视，用户应预测其RUL。这项研究的挑战是提出一种能够评估轴承健康状况并通过预测和健康管理（PHM）技术评估其RUL的原始功能。本文提出的方法基于数​​据驱动的预测方法。探索了简化的模糊自适应共振理论图（SFAM）神经网络和威布尔分布（WD）的结合。 WD仅在训练阶段使用，以适应测量并避免时域波动。 SFAM培训过程基于当前和先前检查时间点的拟合测量值作为输入。但是，SFAM测试过程是基于当前和以前的检查的实际测量结果。由于模糊学习过程，SFAM具有学习非线性时间序列的重要能力和良好性能。作为输出，定义了七个类。健康的轴承和轴承退化的六个状态。为了找到最优的RUL预测，本文提出了一个平滑阶段。实验结果表明，该方法能够基于振动信号可靠地预测滚动轴承的RUL。所提出的预测方法可以应用于其他各种机械资产的预后。

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  • 来源
    《Mechanical systems and signal processing》 |2015年第5期|150-172|共23页
  • 作者

    Jaouher Ben Ali; Brigitte Chebel-Morello; Lotfi Saidi; Simon Malinowski; Farhat Fnaiech;

  • 作者单位

    University of Tunis, National Higher School of Engineers of Tunis, Laboratory of Signal Image and Energy Mastery (SIME), 5 Avenue Taha Hussein, P.O. Box 56, 1008 Tunis, Tunisia,FEMTO-ST Institute, AS2M Department, UMR CNRS 6174-UFC/ENSMM/UTBM, 25000 Besancon, France;

    FEMTO-ST Institute, AS2M Department, UMR CNRS 6174-UFC/ENSMM/UTBM, 25000 Besancon, France;

    University of Tunis, National Higher School of Engineers of Tunis, Laboratory of Signal Image and Energy Mastery (SIME), 5 Avenue Taha Hussein, P.O. Box 56, 1008 Tunis, Tunisia;

    FEMTO-ST Institute, AS2M Department, UMR CNRS 6174-UFC/ENSMM/UTBM, 25000 Besancon, France;

    University of Tunis, National Higher School of Engineers of Tunis, Laboratory of Signal Image and Energy Mastery (SIME), 5 Avenue Taha Hussein, P.O. Box 56, 1008 Tunis, Tunisia;

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  • 正文语种 eng
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  • 关键词

    Prognostics and Health Management (PHM); Remaining useful life (RUL); Rolling element bearings (REBs); SFAM; Weibull distribution (WD);

    机译：预测与健康管理（PHM）;剩余使用寿命（RUL）;滚动轴承（REB）;SFAM;威布尔分布（WD）;

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