一种基于支矢量机器的方法,用于在工业定制车辆中监控螺栓松动
Simone Carone1, Giovanni Pappalettera1, Caterina Casavola1
1Department of Mechanics, Mathematics and Management, Polytechnic University of Bari, Via Orabona n. 4, 70125 Bari, Italy.
Sensors (Basel, Switzerland)
|June 10, 2023
概括
机器学习使用振动分析准确地检测旋转关节中的螺栓松动. 一个单一的支向量机器模型与四个加速度计实现了92.4%的准确性,改善了结构完整性的监测.
科学领域:
- 工程 工程师 工程师 工程师
- 机器学习 机器学习
- 状态监控 状态监控
背景情况:
- 机器学习增强了机器状态监控,用于更高水平的故障诊断.
- 使用定制设备的工业环境挑战了传统的监控方法.
- 螺栓连接对于结构完整性至关重要,但螺栓松动的检测研究不足.
研究的目的:
- 为了研究基于振动的螺栓松动在定制下水道清洁车的旋转关节的检测.
- 为了评估支持向量机 (SVM) 对此特定应用的有效性.
- 确定最佳的加速度计配置和模型策略,以可靠地检测故障.
主要方法:
- 利用来自自定义下水道清洁车辆变速箱的振动数据.
- 使用支持矢量机 (SVM) 进行螺栓松动的分类.
- 分析了加速度计数量和位置 (上游/下游) 的影响.
- 将单个SVM模型与特定条件模型进行比较.
主要成果:
- 一个单一的SVM模型利用四个加速度计的数据实现了92.4%的整体准确性.
- 在联合检测可靠性的上游和下游安装加速度计.
- 该研究确定了一种有效的方法来监测旋转关节的健康状况.
结论:
- 使用SVM的基于振动的检测是识别旋转关节中螺栓松动的可靠方法.
- 一个具有战略位置的加速度计的通用SVM模型提供了强大的故障检测.
- 这项研究有助于维护关键工业组件的结构完整性.
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