The monitoring of rotating machinery is an essential task in today’s production processes. Currently, several machine learning and deep learning-based modules have achieved excellent results in fault detection and diagnosis. Nevertheless, to further increase user adoption and diffusion of such technologies, users and human experts must be provided with explanations and insights by the modules. Another issue is related, in most cases, with the unavailability of labeled historical data that makes the use of supervised models unfeasible. Therefore, a new approach for fault detection and diagnosis in rotating machinery is here proposed. The methodology consists of three parts: feature extraction, fault detection and fault diagnosis. In the first part, the vibration features in the time and frequency domains are extracted. Secondly, in the fault detection, the presence of fault is verified in an unsupervised manner based on anomaly detection algorithms. The modularity of the methodology allows different algorithms to be implemented. Finally, in fault diagnosis, Shapley Additive Explanations (SHAP), a technique to interpret black-box models, is used. Through the feature importance ranking obtained by the model explainability, the fault diagnosis is performed. Two tools for diagnosis are proposed, namely: unsupervised classification and root cause analysis. The effectiveness of the proposed approach is shown on three datasets containing different mechanical faults in rotating machinery. The study also presents a comparison between models used in machine learning explainability: SHAP and Local Depth-based Feature Importance for the Isolation Forest (Local-DIFFI). Lastly, an analysis of several state-of-art anomaly detection algorithms in rotating machinery is included.

旋转机械的监控是当今生产过程中的一项基本任务。目前，多个基于机器学习和深度学习的模块在故障检测和诊断方面取得了优异的成绩。然而，为了进一步提高用户对此类技术的采用和传播，模块必须向用户和人类专家提供解释和见解。在大多数情况下，另一个问题与标记历史数据的不可用有关，这使得使用监督模型不可行。因此，这里提出了一种新的旋转机械故障检测和诊断方法。该方法由三部分组成：特征提取、故障检测和故障诊断。第一部分提取时域和频域的振动特征。第二，在故障检测中，基于异常检测算法以无监督的方式验证故障的存在。该方法的模块化允许实现不同的算法。最后，在故障诊断中，使用了一种解释黑盒模型的技术 Shapley Additive Explanations (SHAP)。通过模型可解释性得到的特征重要性排序，进行故障诊断。提出了两种诊断工具，即：无监督分类和根本原因分析。在包含旋转机械中不同机械故障的三个数据集上显示了所提出方法的有效性。该研究还对机器学习可解释性中使用的模型进行了比较：SHAP 和基于局部深度的隔离森林特征重要性 (Local-DIFFI)。最后，