Artificial Neural Network-based fault diagnostics of an electric motor using vibration monitoring

Rad, M. Khatami; Torabizadeh, Mohammadehsan; Noshadi, Amin

doi:10.1109/tmee.2011.6199495

Cited by 13 publications

(7 citation statements)

References 4 publications

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“…Classification: normal and abnormal data in training using interpretable models: linear regression [116], logistic regression [39,116], decision tree (DT) [39,71]. ML classification techniques as SVM [39,71,163] and feedforward NN [140]. Generative methods: GAN [88], VAE [187].…”

Section: Combination Of Modelsmentioning

confidence: 99%

“…-Classification: diagnose the data to a known failure type or similar working data and then prognosticate a degradation according to the historical data of this class. Despite any classifier can be used for this purpose, the following ones are widely used in literature: feed-forward NN [140], SVM [140], BN [9,85,86], HMM [201], fuzzy logic based [211] and RF [16,62].…”

Section: Prognosismentioning

confidence: 99%

“…Those algorithms are summarised, compared and their main applications are referenced in the following tables. On one hand, the anomaly detection algorithms based on binary and multi-class classification approaches [5,140]…”

Section: Anomaly Detectionmentioning

confidence: 99%

See 2 more Smart Citations

Deep learning models for predictive maintenance: a survey, comparison, challenges and prospect

Serradilla,

Zugasti,

Zurutuza

2020

Preprint

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Given the growing amount of industrial data spaces worldwide, deep learning solutions have become popular for predictive maintenance, which monitor assets to optimise maintenance tasks. Choosing the most suitable architecture for each use-case is complex given the number of examples found in literature. This work aims at facilitating this task by reviewing state-of-the-art deep learning architectures, and how they integrate with predictive maintenance stages to meet industrial companies' requirements (i.e. anomaly detection, root cause analysis, remaining useful life estimation). They are categorised and compared in industrial applications, explaining how to fill their gaps. Finally, open challenges and future research paths are presented.

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Section: Combination Of Modelsmentioning

confidence: 99%

Section: Prognosismentioning

confidence: 99%

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Deep learning models for predictive maintenance: a survey, comparison, challenges and prospect

Serradilla,

Zugasti,

Zurutuza

2020

Preprint

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“…One of the ways to detect short-circuit failures between stator turns is through current signature analysis, which characterizes the induction motor through current analysis and its frequency spectrum [63,44,18]. Another method is the vibration analysis [47,24,25], which is the focus of this work.…”

Section: Faults On Wind Turbinesmentioning

confidence: 99%

Intelligent Incipient Fault Detection in Wind Turbines based on Industrial IoT Environment

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px²,

normal³

et al. 2019

AIS

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How to cite this paper: Sousa, P. H. F.; Nascimento, N. M. M.; Almeida, J. S.; Rebouças Filho, P. P. and Albuquerque, V. AbstractThe eagerness and necessity to develop so-called smart applications has taken the Internet of Things (IoT) to a whole new level. Industry has been implementing services that use IoT to increase productivity as well as management systems over the past couple of years. Such services are now encroaching on wind energy, which nowadays is the most acceptable source among renewable energies for electricity generation. This work proposes an intelligent system to identify incipient faults in the electric generators of wind turbines to improve maintenance routines. Four feature extraction methods were applied to vibration signals, and different classifiers were used to predict the running status of the wind turbine. We correctly identified 94.44% of normal conditions, reducing the false positive and negative rates to 0.4% and 1.84%, respectively; a better result than other approaches already reported in the literature. Kiviluoma [29] analyzed the behavior of the power variation in different regions and demonstrated that rapid changes in local wind conditions cause power fluctuations in the IoT system and operational costs of a wind farm

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“…In [4], a case study is presented for vibration analysis of electrical motors, performed under different speed conditions by means of measurements of an accelerometer. In [5], a system of neural networks was implemented for predictive maintenance in electric motors to detect the type of failure based on the analysis of vibrations. In [6], monitoring of the induction motor condition is presented through vibration analysis techniques.…”

Section: Introductionmentioning

confidence: 99%

Magnetoresistive Sensors and Piezoresistive Accelerometers for Vibration Measurements: A Comparative Study

Dionísio

Torres

Ramalho

et al. 2021

JSAN

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This experimental study focuses on the comparison between two different sensors for vibration signals: a magnetoresistive sensor and an accelerometer as a calibrated reference. The vibrations are collected from a variable speed inductor motor setup, coupled to a ball bearing load with adjustable misalignments. To evaluate the performance of the magnetoresistive sensor against the accelerometer, several vibration measurements are performed in three different axes: axial, horizontal and vertical. Vibration velocity measurements from both sensors were collected and analyzed based on spectral decomposition of the signals. The high cross-correlation coefficient between spectrum vibration signatures in all experimental measurements shows good agreement between the proposed magnetoresistive sensor and the reference accelerometer performances. The results demonstrate the potential of this type of innovative and non-contact approach to vibration data collection and a prospective use of magnetoresistive sensors for predictive maintenance models for inductive motors in Industry 4.0 applications.

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Artificial Neural Network-based fault diagnostics of an electric motor using vibration monitoring

Cited by 13 publications

References 4 publications

Deep learning models for predictive maintenance: a survey, comparison, challenges and prospect

Deep learning models for predictive maintenance: a survey, comparison, challenges and prospect

Intelligent Incipient Fault Detection in Wind Turbines based on Industrial IoT Environment

Magnetoresistive Sensors and Piezoresistive Accelerometers for Vibration Measurements: A Comparative Study

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