Motor Fault Diagnosis Using CNN Based Deep Learning Algorithm Considering Motor Rotating Speed

Han, Ji‐Hoon; Choi, Dong-Jin; Hong, Sun-Ki; Kim, Hyeong-Seob

doi:10.1109/iea.2019.8714900

Cited by 35 publications

(13 citation statements)

References 5 publications

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“…The achieved classification accuracy was 98%, 98%, and 100% for the IMs under normal, rotor fault, and bearing fault conditions. A CNN based method to diagnose faults in IMs taking into account the motor speed (Han, Choi, Hong & Kim, 2019). The vibration signal was used as input to a CNN.…”

Section: Auto-encoders (Aes)mentioning

confidence: 99%

Machine learning and deep learning based methods toward industry 4.0 predictive maintenance in induction motors: State of the art survey

Drakaki

Karnavas

Tziafettas

et al. 2022

JIEM

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Purpose: Developments in Industry 4.0 technologies and Artificial Intelligence (AI) have enabled data-driven manufacturing. Predictive maintenance (PdM) has therefore become the prominent approach for fault detection and diagnosis (FD/D) of induction motors (IMs). The maintenance and early FD/D of IMs are critical processes, considering that they constitute the main power source in the industrial production environment. Machine learning (ML) methods have enhanced the performance and reliability of PdM. Various deep learning (DL) based FD/D methods have emerged in recent years, providing automatic feature engineering and learning and thereby alleviating drawbacks of traditional ML based methods. This paper presents a comprehensive survey of ML and DL based FD/D methods of IMs that have emerged since 2015. An overview of the main DL architectures used for this purpose is also presented. A discussion of the recent trends is given as well as future directions for research.Design/methodology/approach: A comprehensive survey has been carried out through all available publication databases using related keywords. Classification of the reviewed works has been done according to the main ML and DL techniques and algorithmsFindings: DL based PdM methods have been mainly introduced and implemented for IM fault diagnosis in recent years. Novel DL FD/D methods are based on single DL techniques as well as hybrid techniques. DL methods have also been used for signal preprocessing and moreover, have been combined with traditional ML algorithms to enhance the FD/D performance in feature engineering. Publicly available datasets have been mostly used to test the performance of the developed methods, however industrial datasets should become available as well. Multi-agent system (MAS) based PdM employing ML classifiers has been explored. Several methods have investigated multiple IM faults, however, the presence of multiple faults occurring simultaneously has rarely been investigated.Originality/value: The paper presents a comprehensive review of the recent advances in PdM of IMs based on ML and DL methods that have emerged since 2015.

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Section: Auto-encoders (Aes)mentioning

confidence: 99%

Machine learning and deep learning based methods toward industry 4.0 predictive maintenance in induction motors: State of the art survey

Drakaki

Karnavas

Tziafettas

et al. 2022

JIEM

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“…Wavelet packet decomposition was applied in combination with SVM to distinguish different types of bearing faults in [40,41]. In [42], A deep learning algorithm was developed for motor fault diagnosis that also keeps in considering motor speed parameters.…”

Section: Literature Reviewmentioning

confidence: 99%

An Expert System for Rotating Machine Fault Detection Using Vibration Signal Analysis

Kafeel¹,

Aziz

Awais

et al. 2021

Sensors

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Accurate and early detection of machine faults is an important step in the preventive maintenance of industrial enterprises. It is essential to avoid unexpected downtime as well as to ensure the reliability of equipment and safety of humans. In the case of rotating machines, significant information about machine’s health and condition is present in the spectrum of its vibration signal. This work proposes a fault detection system of rotating machines using vibration signal analysis. First, a dataset of 3-dimensional vibration signals is acquired from large induction motors representing healthy and faulty states. The signal conditioning is performed using empirical mode decomposition technique. Next, multi-domain feature extraction is done to obtain various combinations of most discriminant temporal and spectral features from the denoised signals. Finally, the classification step is performed with various kernel settings of multiple classifiers including support vector machines, K-nearest neighbors, decision tree and linear discriminant analysis. The classification results demonstrate that a hybrid combination of time and spectral features, classified using support vector machines with Gaussian kernel achieves the best performance with 98.2% accuracy, 96.6% sensitivity, 100% specificity and 1.8% error rate.

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“…Han et al. 82 identified a method where the target vibration signals in the training network are comparatively independent. Therefore it does not require any guidance while gathering another dataset from another working condition, which is the major benefit when compared with usual transfer learning-based flaws.…”

Section: Decision Tree Random Forest Ensemble Modelmentioning

confidence: 99%

Fault diagnosis of various rotating equipment using machine learning approaches – A review

Manikandan

Duraivelu

2020

Proceedings of the Institution of Mechanical Engineers, Part E:

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Fault diagnosis of various rotating equipment plays a significant role in industries as it guarantees safety, reliability and prevents breakdown and loss of any source of energy. Early identification is a fundamental aspect for diagnosing the faults which saves both time and costs and in fact it avoids perilous conditions. Investigations are being carried out for intelligent fault diagnosis using machine learning approaches. This article analyses various machine learning approaches used for fault diagnosis of rotating equipment. In addition to this, a detailed study of different machine learning strategies which are incorporated on various rotating equipment in the context of fault diagnosis is also carried out. Mainly, the benefits and advance patterns of deep neural network which are applied to multiple components for fault diagnosis are inspected in this study. Finally, different algorithms are proposed to propagate the quality of fault diagnosis and the conceivable research ideas of applying machine learning approaches on various rotating equipment are condensed in this article.

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Motor Fault Diagnosis Using CNN Based Deep Learning Algorithm Considering Motor Rotating Speed

Cited by 35 publications

References 5 publications

Machine learning and deep learning based methods toward industry 4.0 predictive maintenance in induction motors: State of the art survey

Machine learning and deep learning based methods toward industry 4.0 predictive maintenance in induction motors: State of the art survey

An Expert System for Rotating Machine Fault Detection Using Vibration Signal Analysis

Fault diagnosis of various rotating equipment using machine learning approaches – A review

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