With the advent of Industry 4.0, making accurate predictions of the remaining useful life (RUL) of industrial components has become a crucial aspect in predictive maintenance (PdM). To this aim, various Deep Neural Network (DNN) models have been proposed in the recent literature. However, while the architectures of these models have a large impact on their performance, they are usually determined empirically. To exclude the time-consuming process and the unnecessary computational cost of manually engineering these models, we present a Neural Architecture Search (NAS) technique based on an Evolutionary Algorithm (EA) applied to optimize the architecture of a DNN used to predict the RUL. The EA explores the combinatorial parameter space of a multi-head Convolutional Neural Network with Long Short Term Memory (CNN-LSTM) to search for the best architecture. In particular, our method requires minimum computational resources by making use of an early stopping policy and a history of the evaluated architectures. We dub the proposed method ENAS-PdM. To our knowledge, this is the first work where an EA-based NAS is used to optimize a CNN-LSTM architecture in the field of PdM. In our experiments, we use the well-established Commercial Modular Aero-Propulsion System Simulation (C-MAPSS) dataset from NASA. Compared to the current state-of-the-art, our method obtains better results in terms of two different metrics, RMSE and Score, when aggregating across all the C-MAPSS sub-datasets. Without aggregation, we achieve lower RMSE in 3 out of 4 sub-datasets. Our experimental results verify that the proposed method is a reliable tool for obtaining state-of-the-art RUL predictions and as such it can have a strong impact in several industrial applications, especially those with limited available computing power.

随着工业4.0的到来，对工业组件的剩余使用寿命（RUL）进行准确的预测已成为预测性维护（PdM）的关键方面。为了这个目的，在最近的文献中已经提出了各种深度神经网络（DNN）模型。但是，尽管这些模型的体系结构对其性能有很大影响，但通常是凭经验确定的。为了排除手动设计这些模型的耗时过程和不必要的计算成本，我们提出了一种基于进化算法（EA）的神经体系结构搜索（NAS）技术，该技术用于优化用于预测RUL的DNN的体系结构。EA探索具有长短期记忆（CNN-LSTM）的多头卷积神经网络的组合参数空间，以寻找最佳架构。特别地，我们的方法通过利用早期停止策略和评估架构的历史记录，需要最少的计算资源。我们对提议的方法ENAS-PdM进行配音。据我们所知，这是基于EA的NAS用于优化PdM领域的CNN-LSTM体系结构的第一项工作。在我们的实验中，我们使用来自NASA的成熟的商业模块化航空推进系统仿真（C-MAPSS）数据集。与当前的最新技术相比，当对所有C-MAPSS子数据集进行汇总时，我们的方法在两种不同的指标（RMSE和得分）方面获得了更好的结果。没有聚合，我们在4个子数据集中有3个实现了较低的RMSE。我们的实验结果证明，所提出的方法是获得最新RUL预测的可靠工具，因此，它对几种工业应用（尤其是计算能力有限的工业应用）可能会产生重大影响。