Abstract With the fourth industrial revolution, which has become increasingly widespread in the manufacturing industry, traditional maintenance has been replaced by the industrial internet of things (IIoT) based on condition monitoring system (CMS). The IIoT concept provides easier and reliable maintenance. Unlike traditional maintenance, IIoT systems that perform real-time monitoring can provide great advantages to the company by notifying the related maintenance team members of the factory before a serious failure occurs. It is very important to detect faulty bearings before they reach the critical level during the rotation. In this study, an industry 4.0 compatible, IIoT based and low-cost CMS was created and it consists of three main parts. Firstly experimental setup, secondly IIoT based condition monitoring application (CMA) and finally machine learning (ML) models and their evaluation. The experimental setup contains mechanical and electronic materials. Although the most common method used in the classification of bearing damage is vibration data, it observed that characteristics such as sound level, current, rotational speed, and temperature should be included in the data set in order to increase the success of the classification. All these data were collected from the setup, which is 6203 type bearing connected to the universal motor shaft. The designed CMA provides real-time monitoring and recording of the data, which comes wirelessly from the setup, on a mobile device that has an Android operating system. The CMA can also send SMS and e-mail notifications to maintenance team supervisors over mobile devices in case critical thresholds are exceeded. Lastly, the data collected from the experimental setup was modeled for classification with popular ML algorithms such as support vector machine (SVM), linear discrimination analysis (LDA), random forest (RF), decision tree (DT), and k-nearest neighbor (kNN). The models were evaluated with accuracy, precision, TPR, TNR, FPR, FNR, F1 score and, Kappa metrics. During the evaluation of all models, it was observed that with the increase in the number of features in the data set, the accuracy, sensitivity, TPR, TNR, F1 score and Kappa metrics increased above 99% at 95% confidence interval, and FPR and FNR metrics fell below 1%. Although ML models gave successful results, LDA and DT models gave results much faster than others did. On the other hand, the classification success of the LDA model is relatively low. However, DT model is the optimum choice for CMS due to its convenience in determining threshold values, and its ability to give fast and acceptable classification rates.

中文翻译：

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流行的机器学习算法在预测性维护中的实验应用和基于 IIoT 的状态监测系统的设计

摘要 随着第四次工业革命在制造业中的日益普及，传统的维护已经被基于状态监测系统（CMS）的工业物联网（IIoT）所取代。IIoT 概念提供更简单可靠的维护。与传统维护不同，执行实时监控的 IIoT 系统可以在发生严重故障之前通知工厂的相关维护团队成员，从而为公司提供巨大优势。在旋转过程中达到临界水平之前检测故障轴承非常重要。在本研究中，创建了一个兼容工业 4.0、基于 IIoT 的低成本 CMS，它由三个主要部分组成。首先是实验设置，其次是基于 IIoT 的状态监测应用程序 (CMA)，最后是机器学习 (ML) 模型及其评估。实验装置包含机械和电子材料。尽管轴承损坏分类中最常用的方法是振动数据，但据观察，数据集中应包括声级、电流、转速和温度等特征，以提高分类的成功率。所有这些数据都是从设置中收集的，该设置是连接到通用电机轴的 6203 型轴承。设计的 CMA 可在具有 Android 操作系统的移动设备上实时监控和记录来自设置的无线数据。如果超过关键阈值，CMA 还可以通过移动设备向维护团队主管发送 SMS 和电子邮件通知。最后，对从实验设置收集的数据进行建模以使用流行的 ML 算法进行分类，例如支持向量机 (SVM)、线性判别分析 (LDA)、随机森林 (RF)、决策树 (DT) 和 k 最近邻(kNN)。使用准确度、精确度、TPR、TNR、FPR、FNR、F1 分数和 Kappa 指标对模型进行评估。在对所有模型的评估过程中，观察到随着数据集中特征数量的增加，准确率、灵敏度、TPR、TNR、F1 得分和 Kappa 指标在 95% 置信区间提高到 99% 以上，FPR FNR 指标下降到 1% 以下。尽管 ML 模型给出了成功的结果，LDA 和 DT 模型给出的结果比其他模型快得多。另一方面，LDA模型的分类成功率相对较低。然而，DT 模型是 CMS 的最佳选择，因为它可以方便地确定阈值，并且能够提供快速且可接受的分类率。