Abstract Recently, prognostic and health management (PHM) has become a prominent field in modern industry. The rotate vector (RV) reducer is one of the widely used mechanical components in industrial systems, specifically in robots. The RV reducer is known for its unique characteristics of small size, efficient speed transmission, and high torsion. The RV reducer is prone to several kinds of faults, due to its continuous operation in an industrial robot. To keep the operation smooth and steady, timely PHM of the RV reducer has become essential. Previously, the RV reducer fault was diagnosed via various techniques, such as ferrography analysis, vibration analysis, and acoustic emission analysis. However, these conventional techniques have various issues. To resolve those issues, we introduce a novel approach to use the embedded electrical current system for the fault detection of the RV reducer. However, this is quite complicated to investigate mechanical fault using an electrical current signature, since the RV reducer is not an integral part of the electric motor, and finding a fault pattern in faulty components needs thorough examination. We therefore focus on the application of machine learning (ML) for fault classifications. We present an approach for feature extraction, feature selection, and feature reduction using the information obtained from the motor current signature analysis to create an ML-based fault classification system with distinguishable prominent features. Finally, the authenticity of the presented approach is justified via the improved values of evaluating parameters, such as accuracy, specificity, and sensitivity, for ML classifiers.

中文翻译：

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使用电流特征分析的基于机器学习的机器人 RV 减速器的机械故障检测：一种数据驱动的方法

摘要 近年来，预后与健康管理（PHM）已成为现代工业的一个重要领域。旋转矢量（RV）减速器是工业系统中广泛使用的机械部件之一，特别是在机器人中。RV减速机以体积小、变速高效、扭力大等独特特性而著称。RV减速器由于在工业机器人中连续运行，容易出现多种故障。为了保持运行平稳稳定，RV减速器的及时PHM变得至关重要。以前，RV减速器故障是通过多种技术诊断的，例如铁谱分析、振动分析和声发射分析。然而，这些传统技术具有各种问题。为了解决这些问题，我们介绍了一种使用嵌入式电流系统对 RV 减速器进行故障检测的新方法。然而，使用电流特征来调查机械故障是相当复杂的，因为 RV 减速器不是电动机的一个组成部分，并且需要彻底检查故障部件中的故障模式。因此，我们专注于将机器学习 (ML) 应用于故障分类。我们提出了一种使用从电机电流特征分析中获得的信息进行特征提取、特征选择和特征缩减的方法，以创建具有可区分突出特征的基于 ML 的故障分类系统。最后，所提出方法的真实性通过评估参数的改进值来证明，例如准确性、特异性、