Spectral Features-Based Damage Diagnosis of Structural Steel Plate
PPranesh Krishnan1, Sazali Yaacob2, Paulraj M P3, Mohd Shukry Abdul Majid4
1Pranesh Krishnan, Post Doctoral Researcher, Intelligent Automotive Systems Research Cluster, Electrical Electronic and Automation Section, Universiti Kuala Lumpur Malaysian Spanish Institute, Kulim Hi-Tech Park, Kulim, Kedah, Malaysia.
2Sazali Yaacob, Professor, Electrical Electronic and Automation Section, Universiti Kuala Lumpur Malaysian Spanish Institute, Kulim, Kedah, Malaysia.
3Paulraj M P, Professor, and Head of Institution, Ramakrishna Institute of Technology, Coimbatore, Tamil Nadu, India.
4Mohd Shukry Abdul Majid, Associate Professor, School of Mechatronic Engineering, Universiti Malaysia Perlis, Arau, Perlis, Malaysia.
Manuscript received on September 18, 2019. | Revised Manuscript received on 22 September, 2019. | Manuscript published on October 10, 2019. | PP: 630-633 | Volume-8 Issue-12, October 2019. | Retrieval Number: K14800981119/2019©BEIESP | DOI: 10.35940/ijitee.K1480.1081219
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: Cracks and physical damages are a threat to the strength of the structures. Non-destructive test (NDT) measures are used to detect the damages at the earlier phase to avoid any major damages to the structures. Vibration signal processing is one of the NDT methods to determine the damages based on the experimental modal analysis. In this study, an experimental setup is devised to freely suspend a steel plate of size 30 cm by 60 cm. Based on the experimental modal analysis, the steel structure is struck using an impact hammer and the dispersed mechanical energy is bagged as vibration response using an accelerometer. The damages of size 512 µm to 1852 µm were manually simulated at arbitrary locations on the surface of the steel structure. The data acquisition procedure is repeated before and after the simulation of damage. The vibration signals are then processed, and the spectral features are extracted. The feature set is normalized between 0 and 1 are then mapped towards the condition of the plate to formulate the final dataset. Using a k-fold cross validation technique, the dataset is trained and tested using Least square support vector machine (LS-SVM) and k-nearest neighbor (KNN) classifiers. The results are compared and discussed.
Keywords: Damage Detection, Experimental Modal Analysis, Nondestructive Testing, Spectral Features
Scope of the Article: Design and Diagnosis