Traditionally, prognostics approaches to predictive maintenance have focused on estimating the remaining useful life of the equipment. However, from an industrial point of view, the goal is often not to predict the residual life but to determine the need for a maintenance action at a given time window. This approach allows us to frame the data-driven prognostics problem as a binary classification task rather than a regression one. To address this problem, we propose in this paper to explore the relative strengths and limitations of a set of classifier approaches such as random forests, support vector machines, nearest neighbors, and deep learning techniques. We evaluate the models using metrics such as sensitivity, specificity, accuracy, receiver operating characteristic curve, and F-score. This work’s novelty lies in adopting a modeling approach with a natural probabilistic interpretation of the prognostics exercise. The comparison of an extensive range of classifier models is performed on two real-world datasets from the aeronautics sector. Results indicate that deep learning classifier methods are well suited for this kind of prognostics and can outperform by a significant margin the traditional classification techniques. Importantly, the proposed modeling approach aims to generate an alternative prognostics representation that goes in line with the expectations of aeronautical engineers.

传统上，预测性维护的预测方法侧重于估计设备的剩余使用寿命。然而，从工业的角度来看，目标通常不是预测剩余寿命，而是确定在给定时间窗口内是否需要采取维护措施。这种方法允许我们将数据驱动的预测问题构建为二元分类任务而不是回归任务。为了解决这个问题，我们在本文中建议探索一组分类器方法的相对优势和局限性，例如随机森林、支持向量机、最近邻和深度学习技术。我们使用敏感性、特异性、准确性、接收者操作特征曲线和 F 分数等指标来评估模型。这项工作的新颖之处在于采用了一种对预测练习进行自然概率解释的建模方法。对来自航空领域的两个真实世界数据集进行了广泛的分类器模型的比较。结果表明，深度学习分类器方法非常适合这种预测，并且可以显着优于传统分类技术。重要的是，提议的建模方法旨在生成符合航空工程师期望的替代预测表示。结果表明，深度学习分类器方法非常适合这种预测，并且可以显着优于传统分类技术。重要的是，提议的建模方法旨在生成符合航空工程师期望的替代预测表示。结果表明，深度学习分类器方法非常适合这种预测，并且可以显着优于传统分类技术。重要的是，提议的建模方法旨在生成符合航空工程师期望的替代预测表示。