This work presents a methodology for a cloud-based condition monitoring system for fault detection and identification in rotating machines, such as uncoupling, angular and parallel misalignment, by data mining PROFINET network and PROFIdrive profile process data. The proposed methodology involves a new strategy for feature selection of unsupervised data set and employs SVM (Support Vector Machine) and OCSVM (One-Class Support Vector Machine) for operation status classification. The present diagnostic system represents a low-cost solution to the manufacturing process of small and medium enterprises, because it does not require dedicated sensors for fault detection and high featured hardware, and it employs an online cloud-based services. The experimental tests resulted in an accuracy between 87.5% and 100%, and high robustness among different operating conditions. In addition, the proposed feature selection strategy reduced the total execution time by 97.5%.

这项工作提出了一种基于云的状态监测系统的方法，该方法可通过数据挖掘PROFINET网络和PROFIdrive轮廓过程数据来检测和识别旋转机械中的故障，例如解耦，角度和平行未对准。所提出的方法涉及一种用于非监督数据集特征选择的新策略，并采用SVM（支持向量机）和OCSVM（一类支持向量机）进行操作状态分类。本诊断系统代表了中小型企业制造过程的低成本解决方案，因为它不需要用于故障检测的专用传感器和功能强大的硬件，并且采用基于在线云的服务。实验测试得出的准确度在87.5％和100％之间，在不同的操作条件下具有很高的鲁棒性。此外，提出的功能选择策略将总执行时间减少了97.5％。