A Rolling Bearing Fault Diagnosis Based on Conditional Depth Convolution Countermeasure Generation Networks under Small Samples

Cheng, Peiyao; Zhang, Shuting; Li, Changyun

doi:10.3390/s22155658

Cited by 14 publications

(4 citation statements)

References 29 publications

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“…Wentao Mao et al used a GAN to improve the generalization ability of the fault diagnosis model of a rolling bearing, and Diwang Ruan et al [23] investigate the bearing fault diagnosis by optimizing a CNN and a GAN collaboratively. Additionally, Cheng Peng et al [24] substantiate the enhancement of the rolling bearing fault diagnosis method as a result of the GAN utilization.…”

Section: Introductionmentioning

confidence: 99%

Combining machine learning methods and data augmentation for misaligned journal bearings design

Arvanitis,

Nikolakopoulos

2023

IOP Conf. Ser.: Mater. Sci. Eng.

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Shaft misalignment is one of the most common defects observed in rotating systems and has a substantial effect on dynamic behaviour, stability, and lifetime. Aim of this study is the binary identification of misalignment using five Machine Learning techniques: Logistic Regression, K-Nearest Neighbours, Support Vector Machines, Decision Tree and Random Forest. Nevertheless, the limited quantity of provided data points coupled with the substantial imbalance between the aligned and misaligned cases necessitated the implementation of oversampling and data augmentation methods. The utilization of SMOTE-LOF for oversampling the minority class, alongside the adoption of a Conditional Tabular GAN for the generation of synthetic data points yielded substantial outcomes. The application of SMOTE with the Local Outlier Factor on the original dataset achieves the oversampling of the minority class by using additional synthetic data, while the LOF factor overcome the noise problem. After the SMOTE-LOF implementation, new synthetic samples of the minority class are added to the dataset, eliminating the imbalance, however the ‘cleaned’ dataset’s small number of samples could lead to poor performance of the Machine Learning techniques. Hence, using a Generative Adversarial Network to expand the dataset is requisite, and since the experimental data is tabular, utilizing a Conditional Tabular Generative Adversarial Network is ideal for this case of study. The results of the current study elucidate the dataset that, among the augmented datasets, exhibits the best quality score when contrasted with the original dataset. Furthermore, the dataset that performs the best on the Machine Learning algorithms is determined.

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Section: Introductionmentioning

confidence: 99%

Combining machine learning methods and data augmentation for misaligned journal bearings design

Arvanitis,

Nikolakopoulos

2023

IOP Conf. Ser.: Mater. Sci. Eng.

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“…To address these concerns, Xu et al [5] have proposed a ViT (Vision Transformer) model that leverages multi-information fusion, enabling bearing fault diagnosis with limited data samples. Additionally, Chen et al [6] have introduced a conditional depth convolution countermeasure generation networks (C-DCGAN) model capable of enhancing small-sample, multi-category data. The vibration signals emanating from bearings in mechanical equipment exhibit characteristics of both mechanical big data and low data density.…”

Section: Introductionmentioning

confidence: 99%

Research on Rolling Bearing Fault Diagnosis Based on Digital Twin Data and Improved ConvNext

Zhang,

Qin,

Zhao

et al. 2023

Sensors

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This article introduces a novel framework for diagnosing faults in rolling bearings. The framework combines digital twin data, transfer learning theory, and an enhanced ConvNext deep learning network model. Its purpose is to address the challenges posed by the limited actual fault data density and inadequate result accuracy in existing research on the detection of rolling bearing faults in rotating mechanical equipment. To begin with, the operational rolling bearing is represented in the digital realm through the utilization of a digital twin model. The simulation data produced by this twin model replace traditional experimental data, effectively creating a substantial volume of well-balanced simulated datasets. Next, improvements are made to the ConvNext network by incorporating an unparameterized attention module called the Similarity Attention Module (SimAM) and an efficient channel attention feature referred to as the Efficient Channel Attention Network (ECA). These enhancements serve to augment the network’s capability for extracting features. Subsequently, the enhanced network model is trained using the source domain dataset. Simultaneously, the trained model is transferred to the target domain bearing using transfer learning techniques. This transfer learning process enables the accurate fault diagnosis of the main bearing to be achieved. Finally, the proposed method’s feasibility is validated, and a comparative analysis is conducted in comparison with similar approaches. The comparative study demonstrates that the proposed method effectively addresses the issue of low mechanical equipment fault data density, leading to improved accuracy in fault detection and classification, along with a certain level of robustness.

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“…Collecting data and labeling them correctly is time-consuming and labor-intensive, and for some real-world situations, one has to deal with small data. In recent years, many researchers have attempted to apply machine learning to small data [ 18 , 19 , 20 , 21 ]. Despite the fact that there is much literature related to unmanned retail and smart vending machines, there is a complete lack of literature on dealing with the problem of products getting stuck to the best of our knowledge.…”

Section: Introductionmentioning

confidence: 99%

A Real-Time Application for the Analysis of Multi-Purpose Vending Machines with Machine Learning

Cao

Ikenoya

Kawaguchi

et al. 2023

Sensors

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With the development of mobile payment, the Internet of Things (IoT) and artificial intelligence (AI), smart vending machines, as a kind of unmanned retail, are moving towards a new future. However, the scarcity of data in vending machine scenarios is not conducive to the development of its unmanned services. This paper focuses on using machine learning on small data to detect the placement of the spiral rack indicated by the end of the spiral rack, which is the most crucial factor in causing a product potentially to get stuck in vending machines during the dispensation. To this end, we propose a k-means clustering-based method for splitting small data that is unevenly distributed both in number and in features due to real-world constraints and design a remarkably lightweight convolutional neural network (CNN) as a classifier model for the benefit of real-time application. Our proposal of data splitting along with the CNN is visually interpreted to be effective in that the trained model is robust enough to be unaffected by changes in products and reaches an accuracy of 100%. We also design a single-board computer-based handheld device and implement the trained model to demonstrate the feasibility of a real-time application.

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A Rolling Bearing Fault Diagnosis Based on Conditional Depth Convolution Countermeasure Generation Networks under Small Samples

Cited by 14 publications

References 29 publications

Combining machine learning methods and data augmentation for misaligned journal bearings design

Combining machine learning methods and data augmentation for misaligned journal bearings design

Research on Rolling Bearing Fault Diagnosis Based on Digital Twin Data and Improved ConvNext

A Real-Time Application for the Analysis of Multi-Purpose Vending Machines with Machine Learning

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