Application of deep neural network and generative adversarial network to industrial maintenance: A case study of induction motor fault detection

Lee, Yong Oh; Jo, Jun; Hwang, Jongwoon

doi:10.1109/bigdata.2017.8258307

Cited by 116 publications

(60 citation statements)

References 20 publications

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“…In the accumulating step, multiple sets of correlated signals were concatenated as a single RGB image. In [11], signal processing techniques such as the Hilbert-Huang transformation (HHT) and wavelet transform were employed for vibration signal decomposition to detect bearing faults. We also exploited NSP representation of vibration signals for bearing fault detection (denoted as BF-NSP), but using a simpler decomposition method.…”

Section: Image Transformation Of Vibration Signalsmentioning

confidence: 99%

“…Using BF-NSP, solving FDD problems by time-series data analysis becomes an image recognition problem. We employ a CNN classifier because it provides outstanding performance in image recognition and classification [11]. The structure of the proposed CNN classifier, the CNN-based bearing fault detector (CBFD), is shown in Figure 4.…”

Section: Fault Classification Using Cnnmentioning

confidence: 99%

“…There are three solution types for data imbalance: oversampling [11,21], down-sampling [22], and ensemble learning [23]. For extreme cases such as rare occurrence fault condition data, oversampling was the most suitable approach.…”

Section: Generative Model For Oversampling Fault Condition Datamentioning

confidence: 99%

“…Recently, deep neural networks (DNNs) with more powerful fitting abilities have been developed and widely applied to prognostics and health management. In [10][11][12][13][14][15], time-domain and frequency-domain features are extracted in data processing, and then an FDD model is applied for motor status classification. In [16][17][18], vibration image generation with signal analysis was utilized for feature extractions.…”

Section: Introductionmentioning

confidence: 99%

“…Such imbalanced conditions degrade data-driven FDD, especially for convolutional neural network (CNN)-based classifiers. Among oversampling [11,21], down-sampling [22], and ensemble learning [23], all of which have been proposed to solve the data imbalance issue, oversampling is most suitable for industrial FDD because of severe data imbalance ratios. Additionally, oversampling is always the most effective way to deal with class imbalance for the CNN model on image classification [24].…”

Section: Introductionmentioning

confidence: 99%

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Generative Oversampling Method for Imbalanced Data on Bearing Fault Detection and Diagnosis

Suh

Lee

et al. 2019

Applied Sciences

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In this study, we developed a novel data-driven fault detection and diagnosis (FDD) method for bearing faults in induction motors where the fault condition data are imbalanced. First, we propose a bearing fault detector based on convolutional neural networks (CNN), in which the vibration signals from a test bench are used as inputs after an image transformation procedure. Experimental results demonstrate that the proposed classifier for FDD performs well (accuracy of 88% to 99%) even when the volume of normal and fault condition data is imbalanced (imbalance ratio varies from 20:1 to 200:1). Additionally, our generative model reduces the level of data imbalance by oversampling. The results improve the accuracy of FDD (by up to 99%) when a severe imbalance ratio (200:1) is assumed.

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Section: Image Transformation Of Vibration Signalsmentioning

confidence: 99%

Section: Fault Classification Using Cnnmentioning

confidence: 99%

Section: Generative Model For Oversampling Fault Condition Datamentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Generative Oversampling Method for Imbalanced Data on Bearing Fault Detection and Diagnosis

Suh

Lee

et al. 2019

Applied Sciences

Self Cite

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The Future of Manufacturing with AI and Data Analytics

Shah,

Bhanushali

et al. 2024

Artificial Intelligence‐Enabled Digital Twin for Smart Manufacturing

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A review of artificial intelligence applications in manufacturing operations

Plathottam,

Rzonca,

Lakhnori

et al. 2023

J Adv Manuf & Process

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Artificial intelligence (AI) and machine learning (ML) can improve manufacturing efficiency, productivity, and sustainability. However, using AI in manufacturing also presents several challenges, including issues with data acquisition and management, human resources, infrastructure, as well as security risks, trust, and implementation challenges. For example, getting the data needed to train AI models can be difficult for rare events or costly for large datasets that need labeling. AI models can also pose security risks when integrated into industrial control systems. In addition, some industry players may be hesitant to use AI due to a lack of trust or understanding of how it works. Despite these challenges, AI has the potential to be extremely helpful in manufacturing, particularly in applications such as predictive maintenance, quality assurance, and process optimization. It is important to consider the specific needs and capabilities of each manufacturing scenario when deciding whether and how to use AI in manufacturing. This review identifies current developments, challenges, and future directions in AI/ML relevant to manufacturing, with the goal of improving understanding of AI/ML technologies available for solving manufacturing problems, providing decision‐support for prioritizing and selecting appropriate AI/ML technologies, and identifying areas where further research can yield transformational returns for the industry. Early experience suggests that AI/ML can have significant cost and efficiency benefits in manufacturing, especially when combined with the ability to capture enormous amounts of data from manufacturing systems.

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Application of deep neural network and generative adversarial network to industrial maintenance: A case study of induction motor fault detection

Cited by 116 publications

References 20 publications

Generative Oversampling Method for Imbalanced Data on Bearing Fault Detection and Diagnosis

Generative Oversampling Method for Imbalanced Data on Bearing Fault Detection and Diagnosis

The Future of Manufacturing with AI and Data Analytics

A review of artificial intelligence applications in manufacturing operations

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