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A Machine Learning Approach for Temporal Vibration Analysis Applied to Predictive Maintenance
| International Journal of Computer Applications |
| Foundation of Computer Science (FCS), NY, USA |
| Volume 175 - Number 12 |
| Year of Publication: 2020 |
| Authors: Roberto Alexandre Dias, Pedro Von Hertwig, Mário De Noronha Neto |
10.5120/ijca2020920625
|
Roberto Alexandre Dias, Pedro Von Hertwig, Mário De Noronha Neto . A Machine Learning Approach for Temporal Vibration Analysis Applied to Predictive Maintenance. International Journal of Computer Applications. 175, 12 ( Aug 2020), 38-42. DOI=10.5120/ijca2020920625
Abstract
The operational condition of a machine affects the quality and efficiency of its work, and letting a problem arrive in a critical state results in negative consequences, which can cause equipment loss and extensive downtime in a factory. The maintenance of machines, therefore, is a concern that came to exist along with the creation of the industry. This work shows the development of a platform that takes advantage of recent advances in sensor technology and machine learning to assist the predictive maintenance process, identifying problems in advance before serious failures can occur. The work proposes a supervisory system which receives high frequency vibration data, stores it, and analyzes the functioning of a machine to classify its behavior as normal or anomalous, generating alerts. The results achieved show that it is appropriate to use machine learning to monitor machines, since well-structured algorithms can detect possible problems before they become apparent to humans.
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