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Stream/Area:
-
Subject:
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Paper ID:
IJSREM64843
Volume:
10 |
6 |
Jun 2026
Pages:
1-10
Publication Frequency:
Monthly (12 issues per year)
License:International Journal of Scientific Research in Engineering and Management follows the Creative Commons Attribution 4.0 (CC BY 4.0) License, allowing free use and distribution with proper attribution.
Mechanical Engineering
Aditya Engineering College,Beed
Mechanical Engineering
Aditya Engineering College,Beed
In automobile tanks without leak detection, leaks were discovered late, after contamination had spread, requiring difficult and costly cleanups. It is also very dangerous due to ignition of fuel. By contrast, if you have effective leak detection, you can respond quickly to signs of leaks. You can minimize the extent of environmental damage and the threat to human health and safety. Early action on your part also protects you from the high costs that can result from cleaning up extensive leaks and responding to third-party liability claims. There are many methods and types of test equipment for solving these problems. Each test method is suitable only for a specific leak rate or for fixed forms and technologies. In most instances where leak detection is used, explicit leak rate measurement is not required, but the system must be able to recognize if the leak rate is above or below a specified level. This acceptance level is the main parameter to consider when selecting the appropriate method or combination of testing methods. Several other factors must be taken into account. In particular system costs, complexities, environmental impact, reliability, influence of external conditions, operator dependence and user-friendless should also be considered. Leak testing is a nondestructive test method that provides the capability to detect the liquid or gas escaping from a sealed pressure system and to locate the individual leaks for possible repair.
KEYWORDS
The work presented in the report is an attempt in designing a special purpose machine of a given dimension. Extensive literature review was carried out to study the various methods of leak detection techniques. A suitable technique was chosen from the available methods to for testing the oil tank. Pro/ENGINEER 5.0 is used extensively for making parts with different types of operations. Then all the parts are assembled for making a complete special purpose machine in Pro/ENGINEER 5.0 in assembly section. We have done analysis of shaft and top clamp assembly in ANSYS WORKBENCH 2025 R2 which gives us suitable data for manufacturing. Lot of work is yet to be done on the design aspects before the SPM can be read for market consumption. The design procedure has to take into various other parameters to make it suitable for practical applications. Further research into the design and manufacture process would result in production of even better SPM.
1. Leak Detection Methods and Defining the Sizes of Leaks - 4th International Conference of Slovenian Society for Non destructive Testing - Ljubljana, Slovenia. 2. Leak Testing Volume of ASNT Handbook, McMaster, American Society for Non destructive Testing, Columbus, OH, 1980. 3. International Journal of Engineering Research and General Science Volume 2, Issue 3, April-May 2014 ISSN 2091-2730 51. Review Paper on Leak Detection 4. F. Saleem, Z. Ahmad, M. F. Siddique, M. Umar, and J.-M. Kim, “Acoustic Emission-Based Pipeline Leak Detection and Size Identification Using a Customized One-Dimensional DenseNet,” Sensors, vol. 25, no. 4, pp. 1112, Feb. 2025. 5. R. Chu, L. Chik, Y. Song, J. Chan, and X. Li, “Real-time Fuel Leakage Detection via Online Change Point Detection,” International Journal of Data Science and Analytics, vol. 20, pp. 6583–6600, Jul. 2025. 6. W. Hua, Y. Chen, X. Zhao, J. Yang, H. Chen, Z. Wu, and G. Fang, “Research on a Corrosion Detection Method for Oil Tank Bottoms Based on Acoustic Emission Technology,” Sensors, vol. 24, no. 10, pp. 3053, May 2024. 7. M. F. Siddique, Z. Ahmad, N. Ullah, S. Ullah, and J.-M. Kim, “Pipeline Leak Detection: A Comprehensive Deep Learning Model Using CWT Image Analysis and an Optimized DBN-GA-LSSVM Framework,” Sensors, vol. 24, no. 12, pp. 4009, Jun. 2024. 8. D. Hu, J. Wang, H. Zhi, C. Peng, R. He, and L. Wu, “Experimental Test of Pressure Variation and Leakage Location in Petroleum Pipeline Leakage Process,” Journal of Failure Analysis and Prevention, vol. 23, pp. 1621–1632, 2023. 9. M. F. Siddique, Z. Ahmad, and J.-M. Kim, “Pipeline Leak Diagnosis Based on Leak-Augmented Scalograms and Deep Learning,” Engineering Applications of Computational Fluid Mechanics, vol. 17, no. 1, 2023. 10. O. Agbolade, O. Olanrewaju, S. Oyetunji, and J. Babatola, “A LoRaWAN-based IoT System for Leakage Detection in Pipelines,” European Journal of Engineering and Technology Research, vol. 8, no. 5, Sep. 2023.
Publication Date
6/12/2026
Peer Review Type
Double-Blind Peer Review
Review Rounds: 2
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