A cloud-based condition monitoring system for fault detection in rotating machines using PROFINET process data

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%. (C) 2021 Elsevier B.V. All rights reserved.

Automated summary

This study presents a cloud-based condition monitoring system for fault detection and identification in rotating machines, using data mining techniques with high accuracy and robustness, as well as reducing total execution time.