Fault classification in three-phase motors based on vibration signal analysis and artificial neural networks

Ribeiro, Ronny Francis; Almeida, Fabrício Alves de; Gomes, Guilherme Ferreira

doi:10.1007/s00521-020-04868-w

Cited by 38 publications

(6 citation statements)

References 11 publications

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“…On this basis, it was possible to detect cracked rotor cage bars, eccentricity, and damaged bearings of the induction machine. Another example of using a mechanical vibration signal to identify many different damages is presented in [12]. The authors discussed the possibility of using the mean, maximum, and RMS values and additionally cross factors, kurtosis, and peak values for rotor unbalance detection.…”

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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“…Equipment Fault Parameters Method [40] Motor Bearing Current signal CNN [41] CNC machine Condition Vibrations ANN [42] Motor Operations Current and voltage signal ANN/MLP [43] Pump Condition Multi variables AE [44] CNC machine Mechanical Vibrations signal SAE [45] Motor Operations Stator currents ANN [46] Motor Bearing Vibrations signal LSTM [47] Rotating machinery Bearing Vibrations signal AE+ MLP [48] Rotating machinery Bearing Vibrations signal LSTM [49] Cooling radiator Condition Thermal image CNN [50] Rotating machinery Degradation image Infrared image streams (CNN+LSTM) (LSTM+AE) [51] Compressor Condition Multi variables RNN-LSTM [52] Elevator system Movement Acceleration data AE [53] Motor Condition Current signal EWT-CNN [54] Autoclave sterilizer Pump NTC thermistors LSTM [55] Worm gearboxes Operations Multi variables CNN [56] Rotating machinery Rotor, bearing Vibration signals CNN [57] Railcar factories Wheel bearing Temperature variation ANN [58] Rotating machinery Bearing Accelerometers CNN [59] Motor Bearing Current signal ANN [60] Conveyors system Motor Multi variables CNN [61] Motor Bearing Accelerometer LSTM+RNN [62] Motor Rotor bar Torque control ANN [63] Motor Stator winding stator currents ANN [64] Motor Condition Vibrations signal ANN [65] Rotating machinery Bearing Rotation speed, load levels CNN [66] Motor Stator winding Multi variables MLP+LSTM+CNN [67] Motor Operations Current signal ANN [68] Motor+rotating equipment Bearing Vibrations signal CNN+DNN [69] Motor Bearing Microphone, accelerometer DCNN+CNN-LSTM+LSTM…”

Section: Workmentioning

confidence: 99%

Predictive Maintenance of Electromechanical Systems Using Deep Learning Algorithms: Review

Abood¹,

Nasser²,

Al-Khazraji³

2022

ISI

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Predictive Maintenance (PM) is a major part of smart manufacturing in the fourth industrial revolution. The classical fault diagnosis approach for complex systems such as an electromechanical system is not effective. Taking the advantage of the successful implementations of PM together with Deep Learning (DL) methods replaces the conventional diagnosis methods with modern diagnosis methods. This study intends to aid experts, engineers, and technicians in different electromechanical systems in comprehending how the PM used DL methods to find the multi-fault diagnosis. In this direction, this paper presents a comprehensive review of recent works of DL techniques that are applied to PM for electromechanical systems by classifying the research according to equipment, fault, parameters, and method. To perform the review, 30 papers that are published in proceedings and journals are reviewed within a time window between the years 2016 to 2022. In the context of the electromechanical system, it is observed that motors are the most equipment selected for PM. Moreover, stator winding faults are found to be less selected than bearing for diagnosis of the unhealthy status of the motor. In terms of DL methods, the study reveals that AE, LSTM, and CNN are mostly used. In addition, poorly mixed models of DL methods are also noticed. Finally, finding the optimal design variable of the DL architecture was not widely explored.

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“…In case of failure, it can affect production through unwanted downtime, or humans' safety [2]. The industrial maintenance acts as a strategic function for the enterprises, it is not only serves to repair the work tool but rather to anticipate and avoid the failures [3]. It constitutes an essential point of reliability and dependability of electrical systems.…”

Section: Introductionmentioning

confidence: 99%

Enhanced motor fault detection system based on a dual-signature image classification method using CNN

2023

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This paper proposes a new Motor Image Classification (MIC) approach based on a multi-signal conversion technique using Convolutional Neural Network (CNN). In this regard, two one-dimensional (1D) signals are combined and converted into a (2D) color image with motor information pixels. Initially, the vibration signal is converted into the frequency domain. Each point of this signal is firstly assigned a color according to its amplitude and then placed successively on a specific column to obtain a pixilated image. An outline is added to the image representing the internal motor temperature. Therefore, the vibratory and thermal situation of the engine is clearly represented in a Dual-Signature Image (DSI). Our system proves the efficiency of the color compared to grayscale images. It ensures fast and effective prevention, which results in a long service lifetime and maximum motor availability. The diagnostic success rate of our system is 99.93%.

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Fault classification in three-phase motors based on vibration signal analysis and artificial neural networks

Cited by 38 publications

References 11 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

Predictive Maintenance of Electromechanical Systems Using Deep Learning Algorithms: Review

Enhanced motor fault detection system based on a dual-signature image classification method using CNN

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