Next Article in Journal
An Amplifier-Less Acquisition Chain for Power Measurements in Series Resonant Inverters
Previous Article in Journal
Design and Fabrication of CMOS Microstructures to Locally Synthesize Carbon Nanotubes for Gas Sensing
Previous Article in Special Issue
A Further Exploration of Multi-Slot Based Spectrum Sensing
Open AccessArticle

Prediction of Motor Failure Time Using An Artificial Neural Network

1
Postgraduate Program in Electrical Engineering and Computing, Mackenzie Presbyterian University, Rua da Consolação, 896, Prédio 30—Consolação, São Paulo 01302-907, Brazil
2
Computer Science Dept., Mackenzie Presbyterian University, Rua da Consolação, 896, Prédio 31—Consolação, São Paulo 01302-907, Brazil
3
EMAE—Metropolitan Company of Water & Energy, Avenida Nossa Senhora do Sabará, 5312—Vila Emir, São Paulo 04447-902, Brazil
*
Author to whom correspondence should be addressed.
Sensors 2019, 19(19), 4342; https://doi.org/10.3390/s19194342
Received: 31 August 2019 / Revised: 30 September 2019 / Accepted: 3 October 2019 / Published: 8 October 2019
(This article belongs to the Special Issue Sensor Technologies for Smart Industry and Smart Infrastructure)
Industry is constantly seeking ways to avoid corrective maintenance so as to reduce costs. Performing regular scheduled maintenance can help to mitigate this problem, but not necessarily in the most efficient way. In the context of condition-based maintenance, the main contributions of this work were to propose a methodology to treat and transform the collected data from a vibration system that simulated a motor and to build a dataset to train and test an Artificial Neural Network capable of predicting the future condition of the equipment, pointing out when a failure can happen. To achieve this goal, a device model was built to simulate typical motor vibrations, consisting of a computer cooler fan and several magnets. Measurements were made using an accelerometer, and the data were collected and processed to produce a structured dataset. The neural network training with this dataset converged quickly and stably, while the tests performed, k-fold cross-validation and model generalization, presented excellent performance. The same tests were performed with other machine learning techniques, to demonstrate the effectiveness of neural networks mainly in their generalizability. The results of the work confirm that it is possible to use neural networks to perform predictive tasks in relation to the conditions of industrial equipment. This is an important area of study that helps to support the growth of smart industries. View Full-Text
Keywords: predictive maintenance; condition-based maintenance; artificial neural network; vibratory analysis; smart industry; industry maintenance predictive maintenance; condition-based maintenance; artificial neural network; vibratory analysis; smart industry; industry maintenance
Show Figures

Figure 1

MDPI and ACS Style

Scalabrini Sampaio, G.; Vallim Filho, A.R.A.; Santos da Silva, L.; Augusto da Silva, L. Prediction of Motor Failure Time Using An Artificial Neural Network. Sensors 2019, 19, 4342.

Show more citation formats Show less citations formats
Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Article Access Map by Country/Region

1
Back to TopTop