A Two-Phase Machine Learning Approach for Predictive Maintenance of Low Voltage Industrial Motors. Nikfar, M., Bitencourt, J., & Mykoniatis, K. Procedia Computer Science, 200:111–120, January, 2022. ![A Two-Phase Machine Learning Approach for Predictive Maintenance of Low Voltage Industrial Motors [link]](https://bibbase.org/img/filetypes/link.svg "link")
Paper doi abstract bibtex Predictive maintenance and sound operating industrial equipment are essential for nearly any production plant. The absence of a systematic maintenance program and data-driven mindset in making manufacturing decisions may result in serious safety risks, unexpected equipment damages, and financial strain. Condition monitoring and predictive maintenance management systems are commonly used in tandem with the Internet of Things, linking sensors on machines and transmitting the data through a wireless network to a data-logging center that will allow further analysis and support decision making. The system described in this paper measures vibrations using sensors attached to low voltage motors and then utilizes a two-phase machine learning approach for predictive maintenance. In the first phase, we conducted an analysis to look for any abnormal behavior, and in the second phase, we attempted to determine the type of specific faults that may occur. The proposed predictive maintenance system aims to reduce the fault detection time and assist with diagnosing the type of fault occurring. We utilized and tested three machine learning algorithms to detect abnormal motor behavior: support vector machine, backpropagation neural network, and random forest. For predicting the type of specific motor faults that may occur, we used a support vector machine. This two-phase machine learning approach demonstrated promising results in detecting abnormal behavior in low voltage motors. Therefore, integrating this machine learning component as a part of a predictive maintenance system can result in high confidence about the motor condition, reduce maintenance cost, and enhance the safety of the operators and the machines.
@article{nikfar_two-phase_2022,
series = {3rd {International} {Conference} on {Industry} 4.0 and {Smart} {Manufacturing}},
title = {A {Two}-{Phase} {Machine} {Learning} {Approach} for {Predictive} {Maintenance} of {Low} {Voltage} {Industrial} {Motors}},
volume = {200},
issn = {1877-0509},
url = {https://www.sciencedirect.com/science/article/pii/S1877050922002198},
doi = {10.1016/j.procs.2022.01.210},
abstract = {Predictive maintenance and sound operating industrial equipment are essential for nearly any production plant. The absence of a systematic maintenance program and data-driven mindset in making manufacturing decisions may result in serious safety risks, unexpected equipment damages, and financial strain. Condition monitoring and predictive maintenance management systems are commonly used in tandem with the Internet of Things, linking sensors on machines and transmitting the data through a wireless network to a data-logging center that will allow further analysis and support decision making. The system described in this paper measures vibrations using sensors attached to low voltage motors and then utilizes a two-phase machine learning approach for predictive maintenance. In the first phase, we conducted an analysis to look for any abnormal behavior, and in the second phase, we attempted to determine the type of specific faults that may occur. The proposed predictive maintenance system aims to reduce the fault detection time and assist with diagnosing the type of fault occurring. We utilized and tested three machine learning algorithms to detect abnormal motor behavior: support vector machine, backpropagation neural network, and random forest. For predicting the type of specific motor faults that may occur, we used a support vector machine. This two-phase machine learning approach demonstrated promising results in detecting abnormal behavior in low voltage motors. Therefore, integrating this machine learning component as a part of a predictive maintenance system can result in high confidence about the motor condition, reduce maintenance cost, and enhance the safety of the operators and the machines.},
language = {en},
urldate = {2022-03-14},
journal = {Procedia Computer Science},
author = {Nikfar, Mohsen and Bitencourt, Julia and Mykoniatis, Konstantinos},
month = jan,
year = {2022},
keywords = {Predictive maintenance, condition monitoring, low voltage industrial motors, machine learning},
pages = {111--120},
}
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