The experimental application of popular machine learning algorithms on predictive maintenance and the design of IIoT based condition monitoring system

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revolution, which has become increasingly widespread in the manufacturing industry, traditional maintenance has been replaced by the industrial internet of things (IIoT) based on condition monitoring system (CMS).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.