Role of image feature enhancement in intelligent fault diagnosis for mechanical equipment: A review

Sun, Yongjian; Wang, Wei

doi:10.1016/j.engfailanal.2023.107815

Cited by 8 publications

(6 citation statements)

References 161 publications

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“…wileyonlinelibrary.com/jsfa can reveal repetitive patterns in the time series and the structure of the phase space. 30 The combination of different image representation methods can enhance the feature extraction and classification performance of time series data. 30…”

Section: Image Conversion Of the Et Signalsmentioning

confidence: 99%

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Electronic eye and electronic tongue data fusion combined with a GETNet model for the traceability and detection of Astragalus

Jin,

Wang,

et al. 2024

J Sci Food Agric

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BackgroundAstragalus is a widely used traditional Chinese medicine material that is easily confused due to its quality, price and other factors derived from different origins. This article describes a novel method for the rapid tracing and detection of Astragalus via the joint application of an electronic tongue (ET) and an electronic eye (EE) combined with a lightweight CNN‐transformer model. First, ET and EE systems are employed to measure the taste fingerprints and appearance images of different Astragalus samples, respectively. Three spectral transform methods, the Markov transition field (MTF), short‐time Fourier transform (STFT) and recurrence plot (RP), are utilized to convert the ET signals into 2D spectrograms. Then, the obtained ET spectrograms are fused with the EE image to obtain multimodal information. A lightweight hybrid model, termed GETNet, is designed to achieve pattern recognition for the Astragalus fusion information. The proposed model employs an improved transformer module and an improved Ghost bottleneck as its backbone network, complementarily utilizing the benefits of CNN and transformer architectures for local and global feature representation. Furthermore, the Ghost bottleneck is further optimized using a channel attention technique, which boosts the model's feature extraction effectiveness.ResultsThe experiments indicate that the proposed data fusion strategy based on ET and EE devices has better recognition accuracy than that attained with independent sensing devices.ConclusionThe proposed method achieves a high precision (99.1%) and recall (99.1%) values, providing a novel approach for rapidly identifying the origin of Astragalus, and it holds great promise for applications involving other types of Chinese herbal medicines.This article is protected by copyright. All rights reserved.

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Section: Image Conversion Of the Et Signalsmentioning

confidence: 99%

“…30 The combination of different image representation methods can enhance the feature extraction and classification performance of time series data. 30…”

Section: Image Conversion Of the Et Signalsmentioning

confidence: 99%

Electronic eye and electronic tongue data fusion combined with a GETNet model for the traceability and detection of Astragalus

Jin,

Wang,

et al. 2024

J Sci Food Agric

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“…It can effectively capture the similarity and correlation between time series by computing the inner product between vectors, revealing their dynamic relationships. However, GAF is sensitive to noise during the calculation process, which may introduce errors or affect the accuracy of the results [23]. Using only one method to represent the original vibration signal may lose some signal features and may affect the model's resistance to noise, reducing the robustness of the fault diagnosis method.…”

Section: Introductionmentioning

confidence: 99%

Bearing Fault Diagnosis Based on Image Information Fusion and Vision Transformer Transfer Learning Model

Zhang,

Li,

Cai

et al. 2024

Applied Sciences

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In order to improve the accuracy of bearing fault diagnosis under a small sample, variable load, and noise conditions, a new fault diagnosis method based on an image information fusion and Vision Transformer (ViT) transfer learning model is proposed in this paper. Firstly, the method applies continuous wavelet transform (CWT), Gramian angular summation field (GASF), and Gramian angular difference field (GADF) to the time series data, and generates three grayscale images. Then, the generated three grayscale images are merged into an information fusion image (IFI) using image processing techniques. Finally, the obtained IFIs are fed into the advanced ViT model and trained based on transfer learning. In order to verify the effectiveness and superiority of the proposed method, the rolling bearing dataset from Case Western Reserve University (CWRU) is used to carry out experimental studies under different working conditions. Experimental results show that the method proposed in this paper is superior to other traditional methods in terms of accuracy, and the effect of ViT model based on transfer learning (TLViT) training is better than that of the Resnet50 model based on transfer learning training (TLResnet50) under variable loads and small sample conditions. In addition, the experimental results also prove that the IFI with multiple image information has better anti-noise ability than the single information image. Therefore, the method proposed in this paper can improve the accuracy of bearing fault diagnosis under small sample, variable load and noise conditions, and provide a new method for bearing fault diagnosis.

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“…The advantages of fault diagnosis using 2D Images are as follows: Fault diagnosis based on 2D images does not require the need for complex feature extraction processes, predefined parameters, and expert knowledge required in traditional 1D data-based diagnostic methods, thereby shortening experimental procedures and time [ 12 , 13 ]. Two-dimensional images inherently contain more information than 1D data and possess visual characteristics, allowing for more diverse feature extraction and effective fault diagnosis when applied to CNN models [ 14 , 15 ]. Two-dimensional image-based fault detection enables quick and easy identification of various data characteristics without direct feature extraction, offering low time complexity and being well-suited for real-time, high-precision fault diagnosis [ 12 , 16 ].…”

Section: Introductionmentioning

confidence: 99%

“…Two-dimensional images inherently contain more information than 1D data and possess visual characteristics, allowing for more diverse feature extraction and effective fault diagnosis when applied to CNN models [ 14 , 15 ].…”

Section: Introductionmentioning

confidence: 99%

Fault Diagnosis Method for Human Coexistence Robots Based on Convolutional Neural Networks Using Time-Series Data Generation and Image Encoding

Choi,

Park,

et al. 2023

Sensors

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This paper proposes fault diagnosis methods aimed at proactively preventing potential safety issues in robot systems, particularly human coexistence robots (HCRs) used in industrial environments. The data were collected from durability tests of the driving module for HCRs, gathering time-series vibration data until the module failed. In this study, to apply classification methods in the absence of post-failure data, the initial 50% of the collected data were designated as the normal section, and the data from the 10 h immediately preceding the failure were selected as the fault section. To generate additional data for the limited fault dataset, the Wasserstein generative adversarial networks with gradient penalty (WGAN-GP) model was utilized and residual connections were added to the generator to maintain the basic structure while preventing the loss of key features of the data. Considering that the performance of image encoding techniques varies depending on the dataset type, this study applied and compared five image encoding methods and four CNN models to facilitate the selection of the most suitable algorithm. The time-series data were converted into image data using image encoding techniques including recurrence plot, Gramian angular field, Markov transition field, spectrogram, and scalogram. These images were then applied to CNN models, including VGGNet, GoogleNet, ResNet, and DenseNet, to calculate the accuracy of fault diagnosis and compare the performance of each model. The experimental results demonstrated significant improvements in diagnostic accuracy when employing the WGAN-GP model to generate fault data, and among the image encoding techniques and convolutional neural network models, spectrogram and DenseNet exhibited superior performance, respectively.

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Role of image feature enhancement in intelligent fault diagnosis for mechanical equipment: A review

Cited by 8 publications

References 161 publications

Electronic eye and electronic tongue data fusion combined with a GETNet model for the traceability and detection of Astragalus

Electronic eye and electronic tongue data fusion combined with a GETNet model for the traceability and detection of Astragalus

Bearing Fault Diagnosis Based on Image Information Fusion and Vision Transformer Transfer Learning Model

Fault Diagnosis Method for Human Coexistence Robots Based on Convolutional Neural Networks Using Time-Series Data Generation and Image Encoding

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