Fault classification in three-phase motors based on vibration signal analysis and artificial neural networks
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ORIGINAL ARTICLE
Fault classification in three-phase motors based on vibration signal analysis and artificial neural networks Ronny Francis Ribeiro Junior1,3 • Fabrı´cio Alves de Almeida2 • Guilherme Ferreira Gomes1 Received: 20 December 2019 / Accepted: 14 March 2020 Springer-Verlag London Ltd., part of Springer Nature 2020
Abstract Competition in the industrial environment is increasingly intense, so it is of utmost importance that organizations keep their assets in operation as much as possible (in order to produce more). In this context, there is a need for predictive maintenance, a technique that detects the health of assets in real time, allowing failures to be diagnosed before they can interrupt the operation of the assets, avoiding high financial losses. This study uses a sixteen-motor experimental setup with four different known operating conditions. The vibration signal of these motors, through signal analysis, both in time and frequency domains, is performed to evaluate the types and severities of the defects. An artificial neural network (ANN) is used to classify these defects. Considering the vibration analysis, mechanical faults can be identified quickly and conveniently. For the development of the ANN, it was necessary to perform a preprocessing of the vibration signal (response in time) due to the data size, which overwhelms the network. Thus, statistical data were used to extract key information from the vibration signal. Finally, the neural network created based on this study’s methodology presents extremely reliable results, allowing a quick and robust diagnosis of the motor operating condition. Keywords Predictive maintenance Vibration analysis FFT Artificial neural networks Damage classification
1 Introduction Induction motors present high performance and reliability, playing a critical role in many industrial sectors. However, despite their reliability, they are subject to failure [26]. Being able to classify or predict failures (or operating condition) is a task of great importance and crucial for engineers, especially in the field of maintenance. One of the possible solutions is through the use of artificial neural networks (ANN) based on data from certain engines. An effective ANN is able to efficiently predict the assessed response saving time and maintenance costs. Thus, the general industry’s demand for predictive maintenance products and services is increasing. Predictive & Guilherme Ferreira Gomes [email protected] 1
Mechanical Engineering Institute, Federal University of Itajuba´ – UNIFEI, Itajuba´, Brazil
2
Institute of Industrial Engineering and Management, Federal University of Itajuba´ – NIFEI, Itajuba´, Brazil
3
PS Solutions, Itajuba´, Brazil
maintenance is one that indicates the actual operating conditions of equipment based on elements that report wear or degradation process. Therefore, long-term maintenance costs can be reduced with adequate predictive maintenance techniques [18]. In this context, the v
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