Volume 21, Issue 1 (6-2024)
JSDP 2024, 21(1): 27-38 |
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Parvinnia E, Safari M, khayami S. Exploring on rotating machines abnormal state with data mining in protective parameters. JSDP 2024; 21 (1) : 3
URL: http://jsdp.rcisp.ac.ir/article-1-1351-en.html
URL: http://jsdp.rcisp.ac.ir/article-1-1351-en.html
Computer engineering department, Shiraz branch. Islamic Azad university
Abstract: (785 Views)
In order to protect rotating machines and prevent their operation in unusual situations, protective control systems and process data are traditionally used. In this article, a method has been proposed to detect the indirect effects of abnormal operating modes using data mining methods. One of the dangerous conditions of abnormal operation in compressors, as one of the important rotating machines in industries, is the surge condition. In this article, the real data stored during three years of a three-stage refrigerant compressor in a gas refinery are used. the relationship between the surge state of the compressor and the amount of vibration in its different parts has been investigated. It has been proven with data mining methods that there is a direct relationship between the state of surge and the amount of vibration. Also, more sensitive points to vibration during the surges have been identified and it has been proven that by measuring these points, surges can be detected. Therefore, in addition to the existing and previous traditional methods that use process data, it is possible to use the amount of vibration of the points as an extension protection system for surge detection. in this way, more protection of the compressor against the state of surge can be achieved. In this study, various data mining methods have been evaluated, and the results of the nearest neighbor method with the number of neighbors of two have the best performance, and the effects of the number of records in the data set on the quality and accuracy of the results have been investigated.
Article number: 3
Type of Study: Research |
Subject:
Paper
Received: 2022/11/13 | Accepted: 2024/02/25 | Published: 2024/08/3 | ePublished: 2024/08/3
Received: 2022/11/13 | Accepted: 2024/02/25 | Published: 2024/08/3 | ePublished: 2024/08/3
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