Data-driven fault detection (FD) or diagnosis methods are key technologies to ensure safe operation of rotating machinery. These methods rely on a requisite volume of fault data. However, acquiring fault data from rotating machinery is typically problematic and can be entirely unattainable. The critical challenge is to accurately detect and localize the fault states of rotating machinery under the absence of any fault data. Therefore, a newly shrinkage Mamba relation network (SMRN) with out-of-distribution data (OODD) augmentation is proposed for FD and localization in rotating machinery with zero-faulty data. Firstly, the corresponding sensors are arranged for the key detection locations on the rotating machinery. The data generator is referenced to generate OODD for the health data at each detection locations, assisting in mining of intrinsic state information from health data. Then, feature pairs are built in health data and OODD to reveal inter-state attribute relationships. Finally, the location of faults in rotating machinery is determined by evaluating the similarity between feature pairs at each detection location. The SMRN method effectiveness is verified by using self-built propulsion shaft system experiments and rolling bearing cases. The experimental results show the SMRN method can effectively detect and localize fault state of rotating machinery in multiple fault modes, compound fault scenarios, and variable operating conditions.

中文翻译：

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具有分布外数据增强的收缩曼巴关系网络，用于零故障数据下的旋转机械故障检测和定位


数据驱动的故障检测 （FD） 或诊断方法是确保旋转机械安全运行的关键技术。这些方法依赖于必要的故障数据量。然而，从旋转机械获取故障数据通常是有问题的，并且可能完全无法实现。关键挑战是在没有任何故障数据的情况下准确检测和定位旋转机械的故障状态。因此，提出了一种具有分布外数据 （OODD） 增强的新型收缩曼巴关系网络 （SMRN），用于零故障数据的旋转机械中的 FD 和定位。首先，为旋转机械上的关键检测位置布置相应的传感器。引用数据生成器为每个检测位置的健康数据生成 OODD，从而帮助从健康数据中挖掘内部状态信息。然后，在健康数据和 OODD 中构建特征对，以揭示状态间属性关系。最后，通过评估每个探测位置特征对之间的相似性来确定旋转机械中故障的位置。通过使用自建推进轴系统实验和滚动轴承案例验证了 SMRN 方法的有效性。实验结果表明，SMRN 方法可以有效检测和定位旋转机械在多种故障模式、复合故障场景和可变运行条件下的故障状态。