Reliability and safety analyses are the most important activities for reducing risk of failure events and upgrading availability of manufacturing industries. The traditional statistical models have been currently used; however, the complexity growth and diversity of systems as well as uncertainty of their functions result in extreme difficulties in analyzing the reliability by such models. To overcome such drawbacks, the soft computing techniques are useful alternative for modeling of complex systems and prediction applications. Hence, this paper provides a comparative structure for predicting the operational reliability in automotive manufacturing industry, using soft computing + statistical techniques. The results of comparative structure revealed that the soft computing techniques can estimate the reliability function with the lowest error in all cases. Based on the performance criteria, it was observed that among the soft computing techniques, the Adaptive Neuro-Fuzzy Inference System (ANFIS) model yields better results in most cases and thus can be used for predicting operational reliability, since it predicts the reliability more accurately and precisely than the statistical models. Ultimately, the maintenance intervals based on the ANFIS model are proposed to upgrade the reliability and safety of automotive manufacturing process.

可靠性和安全性分析是减少故障事件风险和提高制造业可用性的最重要活动。目前已使用传统的统计模型。然而，系统的复杂性增长和多样性以及其功能的不确定性导致在用这种模型分析可靠性时极度困难。为了克服这些缺点，软计算技术是对复杂系统和预测应用程序进行建模的有用替代方法。因此，本文提供了使用软计算+统计技术来预测汽车制造业运行可靠性的比较结构。比较结构的结果表明，在所有情况下，软计算技术都可以以最小的误差来估计可靠性函数。根据性能标准，可以发现，在软计算技术中，自适应神经模糊推理系统（ANFIS）模型在大多数情况下可产生更好的结果，因此可用于预测操作可靠性，因为它可以更准确地预测可靠性。并比统计模型更精确。最终，提出了基于ANFIS模型的维护间隔，以提高汽车制造过程的可靠性和安全性。自适应神经模糊推理系统（ANFIS）模型在大多数情况下可产生更好的结果，因此可用于预测运行可靠性，因为它比统计模型更准确，更准确地预测可靠性。最终，提出了基于ANFIS模型的维护间隔，以提高汽车制造过程的可靠性和安全性。自适应神经模糊推理系统（ANFIS）模型在大多数情况下可产生更好的结果，因此可用于预测运行可靠性，因为它比统计模型更准确，更准确地预测可靠性。最终，提出了基于ANFIS模型的维护间隔，以提高汽车制造过程的可靠性和安全性。