An Efficient Algorithm for Server Thermal Fault Diagnosis Based on Infrared Image

Liu, Huan; Xie, Ting; Ran, Jian; Gao, Shan

doi:10.1088/1742-6596/910/1/012031

Cited by 11 publications

(6 citation statements)

References 11 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…To reduce the computing time, extracting more representative infrared image features from the image and performing feature selection becomes an excellent solution. Liu et al extracted texture features, moment features, and modified entropy features from the manually acquired region of interest (RoI) as data for server fault diagnosis [17]. Thobiani et al decomposed bearing infrared thermography.…”

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

View full text Add to dashboard Cite

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.

show abstract

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

View full text Add to dashboard Cite

show abstract

“…[28], the author presented a new approach for testing the thermal power installation equipment using the infrared thermographic technique. The SVM classifiers were used to detect the status of computer servers using thermal fault diagnosis, achieving 81.5% accuracy [29].…”

Section: Introductionmentioning

confidence: 99%

Predictive Maintenance of Power Substation Equipment by Infrared Thermography Using a Machine-Learning Approach

et al. 2017

View full text Add to dashboard Cite

A variety of reasons, specifically contact issues, irregular loads, cracks in insulation, defective relays, terminal junctions and other similar issues, increase the internal temperature of electrical instruments. This results in unexpected disturbances and potential damage to power equipment. Therefore, the initial prevention measures of thermal anomalies in electrical tools are essential to prevent power-equipment failure. In this article, we address this initial prevention mechanism for power substations using a computer-vision approach by taking advantage of infrared thermal images. The thermal images are taken through infrared cameras without disturbing the working operations of power substations. Thus, this article augments the non-destructive approach to defect analysis in electrical power equipment using computer vision and machine learning. We use a total of 150 thermal pictures of different electrical equipment in 10 different substations in operating conditions, using 300 different hotspots. Our approach uses multi-layered perceptron (MLP) to classify the thermal conditions of components of power substations into "defect" and "non-defect" classes. A total of eleven features, which are first-order and second-order statistical features, are calculated from the thermal sample images. The performance of MLP shows initial accuracy of 79.78%. We further augment the MLP with graph cut to increase accuracy to 84%. We argue that with the successful development and deployment of this new system, the Technology Department of Chongqing can arrange the recommended actions and thus save cost in repair and outages. This can play an important role in the quick and reliable inspection to potentially prevent power substation equipment from failure, which will save the whole system from breakdown. The increased 84% accuracy with the integration of the graph cut shows the efficacy of the proposed defect analysis approach.

show abstract

“…Com uma modelagem adequada e uma base suficientemente grande, muitos trabalhos em termografia conseguiram utilizar redes neurais e classificadores SVM para reconhecimento de padrões e detecção automática de problemas/falhas (ACHARYA et al, 2012;ESTEVE AGELET et al, 2012;GHANTY et al, 2008;LIU et al, 2017;SIM, 2002;SVENSSON, 2014;USAMENTIAGA et al, 2014;WANG;ZHANG;SHEN, 2010;WONG et al, 2009) Por meio do uso de um classificador NN (Nearest Neighbors), método bastante comum de classificação, o trabalho visa analisar imagens térmicas de rolamentos defeituosos, em conjunto com a aplicação de um algoritmo genético (GA). A proposta consiste na seleção de duas das características extraídas das imagens, formando um espaço de características 2D, que é utilizado com o classificador NN para analisar a efetividade dos parâmetros escolhidos.…”

Section: Trabalhos Correlatos Em Outras áReasunclassified

“…Existem outros momentos, como os momentos de Hu, que são invariantes a posição, escala e rotação, bastante úteis em casos nos quais não é possível capturar imagens de uma posição fixa. Outras características comumente utilizadas nesses trabalhos compreendem a homogeneidade, energia e entropia, por exemplo (FANG;WU, 2007;LIU et al, 2017). Quando se há maior conhecimento sobre a aplicação, pode-se extrair características que trazem indícios de alguma propriedade em particular.…”

unclassified

See 1 more Smart Citation

Detecção automática de problemas elétricos por meio de análise computacional de imagens térmicas

Olivatti¹

View full text Add to dashboard Cite

An Efficient Algorithm for Server Thermal Fault Diagnosis Based on Infrared Image

Cited by 11 publications

References 11 publications

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

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

Predictive Maintenance of Power Substation Equipment by Infrared Thermography Using a Machine-Learning Approach

Detecção automática de problemas elétricos por meio de análise computacional de imagens térmicas

Contact Info

Product

Resources

About