Department of Mechanics, Mathematics and Management, Polytechnic University of Bari, Via Orabona n. 4, 70125 Bari, Italy.
Sensors (Basel). 2023 Jun 5;23(11):5345. doi: 10.3390/s23115345.
Machine learning techniques have progressively emerged as important and reliable tools that, when combined with machine condition monitoring, can diagnose faults with even superior performance than other condition-based monitoring approaches. Furthermore, statistical or model-based approaches are often not applicable in industrial environments with a high degree of customization of equipment and machines. Structures such as bolted joints are a key part of the industry; therefore, monitoring their health is critical to maintaining structural integrity. Despite this, there has been little research on the detection of bolt loosening in rotating joints. In this study, vibration-based detection of bolt loosening in a rotating joint of a custom sewer cleaning vehicle transmission was performed using support vector machines (SVM). Different failures were analyzed for various vehicle operating conditions. Several classifiers were trained to evaluate the influence of the number and location of accelerometers used and to determine the best approach between specific models for each operating condition or a single model for all cases. The results showed that using a single SVM model with data from four accelerometers mounted both upstream and downstream of the bolted joint resulted in more reliable fault detection, with an overall accuracy of 92.4%.
机器学习技术已经逐渐成为重要且可靠的工具，当与机器状态监测相结合时，其故障诊断性能甚至优于其他基于状态监测的方法。此外，在设备和机器高度定制化的工业环境中，统计或基于模型的方法通常不适用。螺栓连接等结构是工业的关键部分；因此，监测它们的健康状况对于保持结构完整性至关重要。尽管如此，对于旋转接头中螺栓松动的检测研究却很少。在这项研究中，使用支持向量机（SVM）对定制污水清洁车辆传动旋转接头中的螺栓松动进行了基于振动的检测。针对不同的车辆运行条件分析了不同的故障。训练了几个分类器来评估使用的加速度计数量和位置的影响，并确定每个运行条件下特定模型之间的最佳方法或所有情况下的单个模型。结果表明，使用单个 SVM 模型，该模型使用安装在螺栓连接上下游的四个加速度计的数据，可实现更可靠的故障检测，整体准确率为 92.4%。
