Remaining fatigue life estimations are not trivial problems for most engineering applications. They could even become quite challenging for general multistep load spectrums, particularly when elastoplastic stresses and strains are involved. In such case, the remaining life predictions could be very sensitive to the chosen damage indicators, leading to complex and non-uniform processes to model non-linear behavior of the materials which exhibit different characteristics for fatigue damage accumulation. To overcome this problem, a data-driven model based upon the kernel-extreme learning machine is proposed to estimate the remaining life of materials under two-step loading. Different from conventional empirical damage models, the proposed model can automatically acquire the optimal mapping relationship from the training samples, which is a quite versatile and flexible method to mathematically describe the indications of fatigue damage mechanism. Moreover, to maintain the basic physical rationality and good measurability, the input variables can also be referenced from conventional damage models. Extensive experimental results of nine materials under two-step loading are collected from the open literature and are used to validate the proposed model. It is shown that the proposed model can provide a much better estimation of the remaining life against the other three conventional models and the standard extreme learning machine (ELM)-based model.

对于大多数工程应用而言，剩余的疲劳寿命估算并不是一个简单的问题。对于一般的多步载荷谱，甚至在涉及弹塑性应力和应变时，它们甚至可能变得非常具有挑战性。在这种情况下，剩余寿命预测可能对所选的损伤指标非常敏感，从而导致对材料的非线性行为进行建模的复杂且不一致的过程，这些材料表现出不同的疲劳损伤累积特性。为了克服这个问题，提出了一种基于核极限学习机的数据驱动模型，用于估计两步加载下材料的剩余寿命。与传统的经验损坏模型不同，该模型可以从训练样本中自动获取最佳映射关系，这是一种非常通用且灵活的方法，可以用数学方法描述疲劳损伤机理的迹象。此外，为了保持基本的物理合理性和良好的可测量性，还可以从常规损坏模型中引用输入变量。从公开文献中收集了九种材料在两步加载下的广泛实验结果，并用于验证所提出的模型。结果表明，相对于其他三个常规模型和基于标准极限学习机（ELM）的模型，所提出的模型可以提供更好的剩余寿命估计。输入变量也可以从常规损坏模型中引用。从公开文献中收集了九种材料在两步加载下的广泛实验结果，并用于验证所提出的模型。结果表明，相对于其他三个常规模型和基于标准极限学习机（ELM）的模型，所提出的模型可以提供更好的剩余寿命估计。输入变量也可以从常规损坏模型中引用。从公开文献中收集了九种材料在两步加载下的广泛实验结果，并用于验证所提出的模型。结果表明，相对于其他三个常规模型和基于标准极限学习机（ELM）的模型，所提出的模型可以提供更好的剩余寿命估计。