Regardless of dimensions, complexity, or purpose of a mechatronic system, fault detection (FD) represents a critical element to achieve robustness and safety, while reducing risks and maintenance costs. Important requisites for an FD component include flexibility, performance, platform independence, and interpretability. To pursue these goals, this article presents a framework for the FD on electromechanical systems based on a fuzzy inference system. Fuzzy logic (FL) is a powerful modeling tool whose applicability to complex systems, however, can be limited by the effort required to specify the fuzzy inference rules, whose number grows rapidly with the number of input variables and membership functions (MFs). To this purpose, we propose the Fuzzy INDices (F-IND) framework as the foundation for an FD system; F-IND automatically generates the fuzzy rules from the specification of qualitative best and worst cases on the MFs of each input variable, which greatly reduces the burden on model design. This article presents the mathematical model of F-IND, its implementation, and its application to the specific case of FD of electric motors, evaluating its computational performance and the accuracy in both simulated and experimental settings.

中文翻译：

---

通过自动生成模糊指标对机电执行器进行故障检测

无论机电系统的尺寸，复杂性或用途如何，故障检测（FD）都是实现鲁棒性和安全性，同时降低风险和维护成本的关键要素。FD组件的重要要求包括灵活性，性能，平台独立性和可解释性。为了实现这些目标，本文提出了基于模糊推理系统的机电系统FD框架。模糊逻辑（FL）是一种功能强大的建模工具，但是，其对复杂系统的适用性可能会受到指定模糊推理规则所需的工作量的限制，模糊推理规则的数量随输入变量和隶属函数（MF）的数量而迅速增加。为此，我们提出了模糊指数（F-IND）框架作为FD系统的基础。最好 和 最坏的每个输入变量的MF的实例，大大减轻了模型设计的负担。本文介绍了F-IND的数学模型，其实现方法及其在电动汽车FD特定情况下的应用，评估了其在模拟和实验环境下的计算性能和准确性。