The Complex Network Model for Industrial Data Based on Spearman Correlation Coefficient

Meng, Chao; Jiang, Xuesong; Wang, Jian; Wei, Xiu Mei

doi:10.1109/ithings/greencom/cpscom/smartdata.2019.00028

Cited by 6 publications

(1 citation statement)

References 23 publications

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“…r s equal to 0.91, which is larger than 0.8. That means there is a significant correlation between CAP values and actual weight loss values [16]. As is known, the actual weight loss value of metals is a quantitative parameter for corrosion assessment of metals [17].…”

Section: Correlation Analysismentioning

confidence: 94%

Corrosion assessment of iron-based metals by the rust recognition method

Shen¹,

Wu²,

Qiu³

2023

International Conference on Image, Signal Processing, and Pattern Recognition (ISPP 2023)

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This paper describes a machine visual recognition method for the surface rust recognition of metals. The corrosion images of iron-based metals (HT200, Q345B, 45 steel and 304) under the specific conditions can be accurately recognized by the proposed method. Based on the rust recognition method, the corrosion area percentage (CAP) of each metal block is calculated. The CAP results indicate that HT200, Q345B and 45 steel are rapidly corroded (CAP over 86 % after 192 hours), but 304 is slightly corroded (CAP: 1.7 % after 192 hours) under the periodic immersion condition. The correlation analysis between CAP values and actual weigh loss values is carried out to evaluate the proposed method. The results show that there is a high correlation between CAP values and actual weight loss values of each metal block. Therefore, CAP values calculated from the rust recognition method can quantitatively identify the corrosion degree of the metal blocks. No matter what type of the iron-based materials is, the corrosion degree of metals can be assessed by the rust recognition method. Even the serious corroded metal blocks, the corrosion degree can be determined through the surface rust recognition.

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Section: Correlation Analysismentioning

confidence: 94%