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

• Published in: Computers in industry 2021-04, Vol.126, p.103394, Article 103394
• Main Authors: Dias, Andre Luis, Turcato, Afonso Celso, Sestito, Guilherme Serpa, Brandao, Dennis, Nicoletti, Rodrigo
• Format: Article
• Language: English
• Subjects: Cloud computing; Condition monitoring; Feature Selection; PROFINET; Rotating machines; Support vector machine
• ISSN: 0166-3615; 1872-6194
• DOI: 10.1016/j.compind.2021.103394

[Display omitted] •Feature extraction and selection related to PROFINET networks process data, not demanding dedicated sensors for fault detection.•Condition monitoring system provided by a cloud service and internet of things strategy to detect and identify anomalous operation condition of rotating machines.•Proposes a novel strategy for feature selection for unary classification of data sets•Three common faults is rotating machines are investigated: uncoupling, angular and parallel misalignment. 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%.