Abstract
This study proposes a method for diagnosing faults in turbomachines using machine learning techniques. In this study, a support vector machine-SVM algorithm is proposed for fault diagnosis of rotor rotation imbalance. Recently, support vector machines (SVMs) have become one of the most popular classification methods in vibration analysis technology. Axis unbalance defect is classified using support vector machines. The experimental data is derived from the turbomachine model of the rigid-shaft rotor and the flexible bearings, and the experimental setup for vibration analysis. Several situations of unbalance defects have been successfully detected.
Highlights
The integration of mechanical, digital and computer systems is constantly growing in modern industry
Samanta [9] has proposed a study to compare the performance of gear fault detection using artificial neural networks (ANNs) and support vector machines (SMVs)
The contribution of this work is the development of an automatic predictive maintenance model for the diagnosis of incipient failures in rotary machines, by means of a machine learning model, based in support vector machines (SVMs) classification methods, which classifies the existence of one or more unbalance conditions of a rotary machine
Summary
The integration of mechanical, digital and computer systems is constantly growing in modern industry. The rotors suffer abrasions and fatigue, due to their continuous use, hampering their operation over time It has a robust structure, any imperfections, minimum that they are, compromise its performance. Aydin et al [4] have used support vector machines (SVM) classification methods to detect broken rotor bar faults. Samanta [9] has proposed a study to compare the performance of gear fault detection using artificial neural networks (ANNs) and support vector machines (SMVs). The contribution of this work is the development of an automatic predictive maintenance model for the diagnosis of incipient failures in rotary machines, by means of a machine learning model, based in support vector machines (SVMs) classification methods, which classifies the existence of one or more unbalance conditions of a rotary machine
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