Predictive maintenance (PdM) advocates for the usage of machine learning technologies to monitor asset's health conditions and plan maintenance activities accordingly. However, according to the specific degradation process, some health-related measures (e.g. temperature) may be not informative enough to reliably assess the health stage. Moreover, each measure needs to be properly treated to extract the information linked to the health stage. Those issues are usually addressed by performing a manual feature engineering, which results in high management cost and poor generalization capability of those approaches. In this work, we address this issue by coupling a health stage classifier with a feature learning mechanism. With feature learning, minimally processed data are automatically transformed into informative features. Many effective feature learning approaches are based on deep learning. With those, the features are obtained as a non-linear combination of the inputs, thus it is difficult to understand the input's contribution to the classification outcome and so the reasoning behind the model. Still, these insights are increasingly required to interpret the results and assess the reliability of the model. In this regard, we propose a feature learning approach able to (i) effectively extract high-quality features by processing different input signals, and (ii) provide useful insights about the most informative domain transformations (e.g. Fourier transform or probability density function) of the input signals (e.g. vibration or temperature). The effectiveness of the proposed approach is tested with publicly available real-world datasets about bearings' progressive deterioration and compared with the traditional feature engineering approach.

预测性维护（PdM）提倡使用机器学习技术来监视资产的健康状况并相应地计划维护活动。但是，根据特定的降解过程，某些与健康相关的措施（例如温度）可能不足以可靠地评估健康状况。此外，需要对每种措施进行适当处理，以提取与健康阶段相关的信息。这些问题通常通过执行手动特征工程来解决，这导致这些方法的管理成本高且泛化能力差。在这项工作中，我们通过将健康阶段分类器与特征学习机制结合来解决此问题。通过特征学习，最少处理的数据会自动转换为信息丰富的特征。许多有效的特征学习方法都是基于深度学习的。有了这些，就可以将这些特征作为输入的非线性组合来获得，因此很难理解输入对分类结果的贡献，因此很难理解模型背后的原因。仍然需要越来越多的洞察力来解释结果并评估模型的可靠性。在这方面，我们提出了一种特征学习方法，该方法能够（i）通过处理不同的输入信号来有效地提取高质量的特征，并且（ii）提供有关以下信息的最有用的域变换（例如傅立叶变换或概率密度函数）的有用见解。输入信号（例如，振动或温度）。所提出方法的有效性已通过公开的有关轴承轴承的真实世界数据集进行了测试。