Real-time diagnosis of three-phase induction machine using Arduino-Uno card based on park’s circle method

Belaid, Mohamed; Cherroun, Lakhmissi; Benbrika, Maroua

doi:10.29354/diag/90641

Cited by 5 publications

(4 citation statements)

References 2 publications

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“…Currently developed diagnostic systems should be able to detect damages occurring in electrical machines at the initial stage, thanks to which the maintenance will be able to plan necessary service repairs. Low-cost diagnostic systems based on cheap microprocessors, measurement cards, and available programming environments are characterized by a modular structure, which can be expanded with new functionalities in a relatively short time [5][6][7][8][9]. The basic diagnostic signals used in the process of detecting faults in electrical machines include stator current, mechanical vibrations (acceleration), and noise (acoustic wave).…”

Section: Literature Overviewmentioning

confidence: 99%

Application of the STFT for Detection of the Rotor Unbalance of a Servo-Drive System with an Elastic Interconnection

Ewert,

Wicher,

Pajchrowski

2024

Electronics

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The article focuses on the use of short-time Fourier transform (STFT) to detect the unbalance of a drive with a flexible connection between the driving machine and the load. The authors present the unbalance model and justify, through subsequent experiments, why the STFT-based approach is appropriate. The effectiveness of the presented method of analyzing signals from acceleration sensors was confirmed experimentally by designing an artificial neural network for detecting the unbalance.

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Section: Literature Overviewmentioning

confidence: 99%

Application of the STFT for Detection of the Rotor Unbalance of a Servo-Drive System with an Elastic Interconnection

Ewert,

Wicher,

Pajchrowski

2024

Electronics

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“…Boudiaf et al [8] introduced a real-time diagnosis method for three-phase induction machines using an Arduino-Uno card based on Park's circle method. The authors focus on leveraging the capabilities of Arduino-Uno to implement an effective realtime diagnostic system for induction machines, specifically employing Park's circle method for analysis.…”

Section: Induction Motor Iot Monitoring System Non-invasive Technique...mentioning

confidence: 99%

Revolutionizing Motor Health: IoT-Driven Detection of Electrical Abnormalities in Three-Phase A.C. Induction Motors

Alex Kok Bin See,

Jed Jian Yao Ong

2023

Malaysian J. Sci. Adv. Tech.

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This paper presented a comprehensive investigation into the detection of electrical abnormalities in a 3-phase alternating current (AC) induction motor (IM) rated at 1.5 kW under simulated single phasing and overloading test conditions. The findings from data analysis on electrical abnormalities simulated physically on an IM were reported, employing a non-invasive technique. Sensors were deployed to monitor the IM and measurements were taken. The data logging and control were designed using an industrial-grade graphical system design software, LabVIEW, and NI PXIe-1071 embedded controller hardware. A novel combination of in-situ on-line current measurements, infrared temperature detection, and 3-axes micro-electro-mechanical systems (MEMS) accelerometers were utilized for measurements. The paper described an internet of things (IoT) monitoring system capable of alerting the operator of the motor to any electrical abnormalities. Experimental results suggested that an IM subjected to single phasing experienced observable z-plane vibration, with a standard deviation of 0.24 G. For overload tests (i.e., 50 Hz and 30 Hz) on the IM based on the national electrical manufacturers association (NEMA) standard, the percentage of heating ranged between 103.98% and 109.67%, respectively; however, no significant increase in z-plane vibrations was observed.

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“…In the scientific research and exploitation practice of induction motor (IM) drives, it is necessary to find reliable solutions for diagnostic systems, which are expected to meet more and more difficult requirements. At the same time, there is a move away from highly specialized and expensive solutions to low-budget systems based on measuring cards, cheap microprocessors and widely available software environments [1][2][3][4]. In such flexible diagnostic systems, it is possible to extend functionality relatively quickly by adding extra software modules, in which different signal processing methods are used.…”

Section: Introductionmentioning

confidence: 99%

Low-Cost Monitoring and Diagnosis System for Rolling Bearing Faults of the Induction Motor Based on Neural Network Approach

2020

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In this article, a low-cost computer system for the monitoring and diagnosis of the condition of the induction motor (IM) rolling bearings is demonstrated and tested. The system allows the on-line monitoring of the IM bearings and subsequent fault diagnostics based on analysis of the vibration measurement data. The evaluation of the bearing condition is made by a suitably trained neural network (NN), on the basis of the spectral and envelope analysis of the mechanical vibrations. The system was developed in the LabVIEW environment in such a way that it could be run on any PC. The functionality of the application has been tested on a real object. The study was conducted on a low-power IM equipped with a set of specially prepared bearings so as to model the different damages. In the designed computer system, a selected NN for detecting and identifying the defects of individual components of the induction motor’s bearings was implemented. The training data for NNs were obtained from real experiments. The magnitudes of the characteristic harmonics, obtained from the spectral analysis and the envelope analysis, were used for training and testing the developed neural detectors based on Matlab toolbox. The experimental test results of the developed monitoring and diagnosis system are presented in the article. The evaluation of the system’s ability to detect and identify the defects of individual components of bearings, such as the rolling element, and outer race and inner race, was made. It was also shown that the developed NN-based detectors are insensitive to other motor faults, such as short-circuits of the stator winding, broken rotor bars or motor misalignment.

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Real-time diagnosis of three-phase induction machine using Arduino-Uno card based on park’s circle method

Cited by 5 publications

References 2 publications

Application of the STFT for Detection of the Rotor Unbalance of a Servo-Drive System with an Elastic Interconnection

Application of the STFT for Detection of the Rotor Unbalance of a Servo-Drive System with an Elastic Interconnection

Revolutionizing Motor Health: IoT-Driven Detection of Electrical Abnormalities in Three-Phase A.C. Induction Motors

Low-Cost Monitoring and Diagnosis System for Rolling Bearing Faults of the Induction Motor Based on Neural Network Approach

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