Open AccessRotor Unbalance Kind and Severity Identification by Current Signature Analysis with Adaptative Update to Multiclass Machine Learning Algorithms
Author(s) -
Sergio Avila,
H. M. Schaberle,
Slah Ben Youssef,
Fernando S. Pacheco,
Cesar Alberto Penz
Publication year - 2021
Publication title -
studies in engineering and technology
Language(s) - English
Resource type - Journals
eISSN - 2330-2046
pISSN - 2330-2038
DOI - 10.11114/set.v8i1.5213
Subject(s) - decision tree , support vector machine , stator , computer science , rotor (electric) , machine learning , decision tree learning , artificial intelligence , signature (topology) , identification (biology) , classifier (uml) , data mining , control engineering , algorithm , engineering , mathematics , mechanical engineering , botany , geometry , biology
The health of a rotating electric machine can be evaluated by monitoring electrical and mechanical parameters. As more information is available, it easier can become the diagnosis of the machine operational condition. We built a laboratory test bench to study rotor unbalance issues according to ISO standards. Using the electric stator current harmonic analysis, this paper presents a comparison study among Support-Vector Machines, Decision Tree classifies, and One-vs-One strategy to identify rotor unbalance kind and severity problem – a nonlinear multiclass task. Moreover, we propose a methodology to update the classifier for dealing better with changes produced by environmental variations and natural machinery usage. The adaptative update means to update the training data set with an amount of recent data, saving the entire original historical data. It is relevant for engineering maintenance. Our results show that the current signature analysis is appropriate to identify the type and severity of the rotor unbalance problem. Moreover, we show that machine learning techniques can be effective for an industrial application.