A Combination of Support Vector Machine and Heuristics in On-line Non-Destructive Inspection System

In order to meet the development of shock absorber on-line detection, a new method of indicator diagrams recognition for shock absorber based on support vector machine (SVM) is proposed. Different fault patterns of shock absorber indicator diagram are discussed, including their main causes. The recognition model is constructed each with Linear, Polynomial and Radial Basis Function (RBF) kernel function. The experimental results show that the best average recognition rate is 96.4%. This method is effective in indicator diagram fault recognition of shock absorber.

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Detection of sand deposition in pipeline using percussion, voice recognition, and support vector machine

Structural Health Monitoring ◽

10.1177/1475921720918890 ◽

2020 ◽

Vol 19 (6) ◽

pp. 2075-2090 ◽

Cited By ~ 2

Author(s):

Hao Cheng ◽

Furui Wang ◽

Linsheng Huo ◽

Gangbing Song

Keyword(s):

Support Vector Machine ◽

Large Scale ◽

Rapid Development ◽

Voice Recognition ◽

Support Vector ◽

Machine Model ◽

Frequency Scale ◽

Removal Time ◽

The Impact ◽

Non Destructive

Deposits prevention and removal in pipeline has great importance to ensure pipeline operation. Selecting a suitable removal time based on the composition and mass of the deposits not only reduces cost but also improves efficiency. In this article, we develop a new non-destructive approach using the percussion method and voice recognition with support vector machine to detect the sandy deposits in the steel pipeline. Particularly, as the mass of sandy deposits in the pipeline changes, the impact-induced sound signals will be different. A commonly used voice recognition feature, Mel-Frequency Cepstrum Coefficients, which represent the result of a cosine transform of the real logarithm of the short-term energy spectrum on a Mel-frequency scale, is adopted in this research and Mel-Frequency Cepstrum Coefficients are extracted from the obtained sound signals. A support vector machine model was employed to identify the sandy deposits with different mass values by classifying energy summation and Mel-Frequency Cepstrum Coefficients. In addition, the classification accuracies of energy summation and Mel-Frequency Cepstrum Coefficients are compared. The experimental results demonstrated that Mel-Frequency Cepstrum Coefficients perform better in pipeline deposits detection and have great potential in acoustic recognition for structural health monitoring. In addition, the proposed Mel-Frequency Cepstrum Coefficients–based pipeline deposits monitoring model can estimate the deposits in the pipeline with high accuracy. Moreover, compared with current non-destructive deposits detection approaches, the percussion method is easy to implement. With the rapid development of artificial intelligence and acoustic recognition, the proposed method can realize higher accuracy and higher speed in the detection of pipeline deposits, and has great application potential in the future. In addition, the proposed percussion method can enable robotic-based inspection for large-scale implementation.

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Support vector machine for image based automated inspection system

Proceedings of the 20th IEEE Instrumentation Technology Conference (Cat. No.03CH37412) ◽

10.1109/imtc.2003.1208218 ◽

2004 ◽

Author(s):

Chien-Ting Kuo ◽

Chih-Hsien Kung ◽

Chih-Ming Kung ◽

J.H. Jeng

Keyword(s):

Support Vector Machine ◽

Support Vector ◽

Inspection System ◽

Automated Inspection

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Keyboard Characters Recognition with Support Vector Machine

Applied Mechanics and Materials ◽

10.4028/www.scientific.net/amm.336-338.2283 ◽

2013 ◽

Vol 336-338 ◽

pp. 2283-2287

Author(s):

Xin Wen Gao ◽

Xing Jian Guan ◽

Ben Bo Guan

Keyword(s):

Support Vector Machine ◽

Feature Extraction ◽

Dimensional Space ◽

Training Data ◽

Support Vector ◽

Inspection System ◽

Higher Dimensional ◽

Key Technologies ◽

Recognition Function ◽

The Difference

This paper proposed a method to detect the defects of keyboard characters. The work, which is a part of the keyboard inspection system, integrates two key technologies to realize the recognition function. First, Feature extraction is applied to select the best properties of the keyboard characters to distinguish the difference and six features are chosen. Second, we integrate support vector machine (SVM) into the classification method and the radial basis kernel function is used to map the training data into higher dimensional space to facilitate the classification. We get a satisfied result in the classification finally which demonstrate the proposed approach is effective.

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