A Combination of Fourier Transform and Machine Learning for Fault Detection and Diagnosis of Induction Motors

Nguyen, Van Duc; Zwanenburg, Ewout; Limmer, Steffen; Luijben, Wessel; Bäck, Thomas; Olhofer, Markus

doi:10.1109/dsa52907.2021.00053

Cited by 7 publications

(1 citation statement)

References 42 publications

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“…Several implementations of the Prophet ML models for time-series forecasting have been employed for motor vibration analysis and forecasting, which are reported in this article. Experiments have also been performed with NeuralProphet [20], which is the successor of Prophet, retaining all of Prophet's advantages while improving its accuracy and scalability by including neural network modules. In future research to be conducted, the intention is to fuse the latest advances in DL into the Prophet time-series components that have been developed, thus contributing to forecasting with an eye to improving PdM beyond its state-of-the-art functionality.…”

Section: Machine Learning With Time-seriesmentioning

confidence: 99%

AI-Based Edge Acquisition, Processing and Analytics for Industrial Food Production

Vermesan

Bellmann

Bahr

et al. 2022

Ambient Intelligence and Smart Environments

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This article presents a novel approach to the acquisition, processing, and analytics of industrial food production by employing state-of-the-art artificial intelligence (AI) at the edge. Intelligent Industrial Internet of Things (IIoT) devices are used to gather relevant production parameters of industrial equipment and motors, such as vibration, temperature and current using built-in and external sensors. Machine learning (ML) is applied to measurements of the key parameters of motors and equipment. It runs on edge devices that aggregate sensor data using Bluetooth, LoRaWAN, and Wi-Fi communication protocols. ML is embedded across the edge continuum, powering IIoT devices with anomaly detectors, classifiers, predictors, and neural networks. The ML workflows are automated, allowing them to be easily integrated with more complex production flows for predictive maintenance (PdM). The approach proposes a decentralized ML solution for industrial applications, reducing bandwidth consumption and latency while increasing privacy and data security. The system allows for the continuous monitoring of parameters and is designed to identify potential breakdown situations and alert users to prevent damage, reduce maintenance costs and increase productivity.

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Section: Machine Learning With Time-seriesmentioning

confidence: 99%

AI-Based Edge Acquisition, Processing and Analytics for Industrial Food Production

Vermesan

Bellmann

Bahr

et al. 2022

Ambient Intelligence and Smart Environments

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Machine hearing for predictive maintenance of BLDC motors

Prieto,

Bravo Montenegro,

Rengifo

2024

JQME

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PurposeThe purpose of this paper is to approach predictive maintenance (PdM) of brushless direct current (BLDC) motors using audio signal processing and extracting statistical and spectral features to train classical machine learning (ML) models.Design/methodology/approachThe proposed methodology relies on classification predictive model that shows the motors prone to failure. To verify this, the model was implemented and tested with audio data. The trained models are then deployed to an Industrial Internet of Things (IIoT) application built using Django.FindingsThe implementation of the methodology allows for achieving performance as high as 92% accuracy, proving that spectral features should be considered when training ML models for PdM.Originality/valueThe proposed model is an effective decision-making tool that provides an ideal solution for preventive maintenance scheduling problems for BLDC motors.

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Machine Learning and Audio Signal Processing for Predictive Maintenance: A review

Prieto,

Bravo

2023

2023 IEEE 6th Colombian Conference on Automatic Control (CCAC)

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A Combination of Fourier Transform and Machine Learning for Fault Detection and Diagnosis of Induction Motors

Cited by 7 publications

References 42 publications

AI-Based Edge Acquisition, Processing and Analytics for Industrial Food Production

AI-Based Edge Acquisition, Processing and Analytics for Industrial Food Production

Machine hearing for predictive maintenance of BLDC motors

Machine Learning and Audio Signal Processing for Predictive Maintenance: A review

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