Novel MOA Fault Detection Technology Based on Small Sample Infrared Image

Wei, Baoquan; Zuo, Yong; Liu, Yande; Luo, Wei; Wen, Kaiyun; Deng, Fang−Ming

doi:10.3390/electronics10151748

Cited by 10 publications

(7 citation statements)

References 16 publications

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“…It is well known that a metal oxide arrester (MOA) is an important piece of protection equipment for ensuring the safe operation of a power transmission system. To monitor the health condition of the MOA, a novel MOA fault detection technology based on a small sample infrared image was proposed in Reference [5]. In the study, a lightweight MOA identification and location algorithm was developed, which can not only reduce the amount of data uploaded but also reduce the search space of the cloud algorithm.…”

Section: The Present Issuementioning

confidence: 99%

“…Once it is difficult for the historical data to meet the requirements of machine learning algorithms in terms of quantity and quality, they may lead to condition monitoring and fault diagnosis errors. Although some efforts (e.g., [5,11]) have been made in the development of machine learning algorithms based on small sample data, the machine learning using small samples is still an unsolved problem that requires further efforts to overcome in the future.…”

Section: Futurementioning

confidence: 99%

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Advances in Machine Condition Monitoring and Fault Diagnosis

2022

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In the past few decades, with the great progress made in the field of computer technology, non-destructive testing, signal and image processing, and artificial intelligence, machine condition monitoring and fault diagnosis technology have also achieved great technological progress and played an active and important role in various industries to ensure the efficient and reliable operation of machines, lower the operation and maintenance costs, and improve the reliability and availability of large critical equipment [...]

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Section: The Present Issuementioning

confidence: 99%

Section: Futurementioning

confidence: 99%

Advances in Machine Condition Monitoring and Fault Diagnosis

2022

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“…Figure 5 shows the vertical projection results for the RoI. The image was first scanned from left to right, and thereafter the pixel value of each column was accumulated [38]. By counting the number of pixels in each column, it can be observed that the cumulative value of pixels undergoes a sudden change at the junction of two characters, where the pixel cumulative value was the minimum value.…”

Section: Roi Extraction Based On Contour Informationmentioning

confidence: 99%

Thermal Defect Detection for Substation Equipment Based on Infrared Image Using Convolutional Neural Network

Wang

Zhang

et al. 2021

Electronics

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Thermal defects of substation equipment have a great impact on the stability of power systems. Temperature is crucial for thermal defect detection in infrared images. The traditional detection methods, which have low efficiency and poor accuracy, record the temperature of infrared images manually. In this study, a thermal defect detection method based on infrared images using a convolutional neural network (CNN) is proposed. Firstly, the improved pre-processing method is applied to reduce background information, and the region of interest is located according to the contour and position information, hence improving the quality of images. Then, the temperature values are segmented to establish the dataset (T-IR11), which contains 11 labels. Finally, the CNN model is constructed to extract features, and the support vector machine is trained for classification. To verify the effectiveness of the proposed method, precision, recall, and F1 score are adopted and 10-fold cross-validation is employed on the T-IR11 dataset. The results demonstrate that the accuracy of the proposed method is 99.50%, and the performance is superior to that of previous methods in terms of infrared images. The proposed method can realize automatic temperature recognition and equipment with thermal defects can be recorded systematically, which has significant practical value for defect detection in substation equipment.

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“…As a result, deeper features could be extracted [12]. Wei et al used migration learning and a deep convolutional generative adversarial network (DCGAN) to construct an extended model of faulty samples to solve the fault diagnosis of a small sample of infrared images [13]. Mian et al performed the fault diagnosis of multiple bearing faults by fusing time-frequency images of vibration signals generated by continuous wavelet transform (CWT) and captured infrared images using CNN.…”

Section: Introductionmentioning

confidence: 99%

SSDStacked-BLS with Extended Depth and Width: Infrared Fault Diagnosis of Rolling Bearings under Dual Feature Selection

Zhou,

Liu,

Shen

2023

Mathematics

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In fault diagnosis, broad learning systems (BLS) have been applied in recent years. However, the best fault diagnosis cannot be guaranteed by width node extension alone, so a stacked broad learning system (stacked BLS) was proposed. Most of the methods for choosing the number of depth layers used optimization algorithms that tend to increase computation time. In addition, the data under single feature selection are not sufficiently representative, and effective features are easily lost. To solve these problems, this article proposes an infrared fault diagnosis model for rolling bearings based on integration of principal component analysis and singular value decomposition (IPS) and the stacked BLS with self-selected depth model (SSDStacked-BLS). First, 72 second-order statistical features are extracted from the pre-processed infrared images of rolling bearings. Next, feature selection is performed using IPS. he IPS feature selection module consists of principal component analysis (PCA) and singular value decomposition (SVD). The feature selection is performed by PCA and SVD separately, which are then stitched together to form a new feature. This ensures a comprehensive coverage of infrared image features. Finally, the acquired features are input into SSDStacked-BLS. This model establishes a data storage group for the residual training characteristics of stacked BLS, adding one block at a time. The accuracy rate of each newly added block is output and saved to the data storage group. If the diagnostic rate fails to increase three consecutive times, the block stacking is stopped and the results are output. IPS-SSDStacked-BLS achieved an accuracy of 0.9667 in 0.1775 s. This is almost five times faster than stacked BLS optimized using the grid search method. Compared with the original BLS, its accuracy was 0.0445 higher and the time was approximated. Compared with IPS-SVM, IPS-RF, IPS-1DCNN and 2DCNN, IPS-SSDStacked-BLS was more advantageous in terms of accuracy and time consumption.

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Novel MOA Fault Detection Technology Based on Small Sample Infrared Image

Cited by 10 publications

References 16 publications

Advances in Machine Condition Monitoring and Fault Diagnosis

Advances in Machine Condition Monitoring and Fault Diagnosis

Thermal Defect Detection for Substation Equipment Based on Infrared Image Using Convolutional Neural Network

SSDStacked-BLS with Extended Depth and Width: Infrared Fault Diagnosis of Rolling Bearings under Dual Feature Selection

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