The reliability of products is a critical issue as it has high economic impacts, especially in current competitive markets. In modern applications, the complex and high-dimensional data of products are collected that can be used for reliability analysis and failure prediction. The existing reliability approaches, however, cannot efficiently model complex covariates and their effects on the time-to-failure of products. In this article, we propose a novel deep learning-based reliability approach to model the complex relationship between the covariates and product failure. To estimate model parameters, neither the traditional deep learning parameter estimation method nor the maximum likelihood estimation method is applicable. To overcome this difficulty, a new model parameter estimation method is developed based on the partial likelihood framework. Furthermore, as there are often only a limited number of samples for real-world reliability problems, a new penalized partial likelihood estimation method is developed for this special circumstance. The developed method is capable of estimating model parameters for censored reliability data. A simulation study is conducted to verify the developed methods. The proposed method is justified by a real-world case study of the reliability analysis of materials. The case study shows that the proposed model outperforms the existing ones.

中文翻译：

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基于深度学习的复杂生存数据可靠性模型

产品的可靠性是一个至关重要的问题，因为它具有很高的经济影响，尤其是在当前竞争激烈的市场中。在现代应用中，会收集产品的复杂和高维数据，这些数据可用于可靠性分析和故障预测。但是，现有的可靠性方法无法有效地对复杂的协变量及其对产品失效时间的影响进行建模。在本文中，我们提出了一种新颖的基于深度学习的可靠性方法来对协变量和产品故障之间的复杂关系进行建模。为了估计模型参数，传统的深度学习参数估计方法和最大似然估计方法均不适用。为了克服这个困难，基于偏似然框架开发了一种新的模型参数估计方法。此外，由于针对现实世界中可靠性问题的样本通常数量有限，因此针对这种特殊情况开发了一种新的惩罚性部分似然估计方法。所开发的方法能够估计用于审查的可靠性数据的模型参数。进行了仿真研究，以验证所开发的方法。通过对材料可靠性分析的实际案例研究证明了该方法的合理性。案例研究表明，所提出的模型优于现有模型。进行了仿真研究，以验证所开发的方法。通过对材料可靠性分析的实际案例研究证明了该方法的合理性。案例研究表明，所提出的模型优于现有模型。进行了仿真研究，以验证所开发的方法。通过对材料可靠性分析的实际案例研究证明了该方法的合理性。案例研究表明，所提出的模型优于现有模型。