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Applying Hierarchical Bayesian Neural Network in Failure Time Prediction

机译：贝叶斯神经网络在故障时间预测中的应用

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摘要

With the rapid technology development and improvement, the product failure time prediction becomes an even harder task because only few failures in the product life tests are recorded. The classical statistical model relies on the asymptotic theory and cannot guarantee that the estimator has the finite sample property. To solve this problem, we apply the hierarchical Bayesian neural network (HBNN) approach to predict the failure time and utilize the Gibbs sampler of Markov chain Monte Carlo (MCMC) to estimate model parameters. In this proposed method, the hierarchical structure is specified to study the heterogeneity among products. Engineers can use the heterogeneity estimates to identify the causes of the quality differences and further enhance the product quality. In order to demonstrate the effectiveness of the proposed hierarchical Bayesian neural network model, the prediction performance of the proposed model is evaluated using multiple performance measurement criteria. Sensitivity analysis of the proposed model is also conducted using different number of hidden nodes and training sample sizes. The result shows that HBNN can provide not only the predictive distribution but also the heterogeneous parameter estimates for each path.
机译：随着技术的快速发展和进步，产品故障时间的预测变得更加艰巨，因为产品寿命测试中仅记录了很少的故障。经典的统计模型依赖于渐近理论，不能保证估计量具有有限的样本性质。为了解决这个问题，我们应用了分层贝叶斯神经网络（HBNN）方法来预测故障时间，并利用马尔可夫链蒙特卡洛（MCMC）的Gibbs采样器来估计模型参数。在该方法中，指定层次结构来研究产品之间的异质性。工程师可以使用异质性估算来确定质量差异的原因，并进一步提高产品质量。为了证明所提出的分层贝叶斯神经网络模型的有效性，使用多种性能测量标准对所提出模型的预测性能进行了评估。还使用不同数量的隐藏节点和训练样本大小对提议的模型进行了敏感性分析。结果表明，HBNN不仅可以提供预测分布，还可以为每个路径提供异构参数估计。

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  • 来源
    《Mathematical Problems in Engineering》 |2012年第5期|p.32.1-32.11|共11页
  • 作者

    Ling-Jing Kao; Hsin-Fen Chen;

  • 作者单位

    Department of Business Management, National Taipei University of Technology, 10608, Taiwan;

    Graduate Institute of Industrial and Business Management, National Taipei University of Technology, 10608, Taiwan;

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