NEURAL NETWORK PROCESSING OF ELECTROMAGNETIC ACOUSTIC SIGNALS TO IDENTIFY THE STRESS-STRAIN STATE AND DAMAGE OF POWER EQUIPMENT

Author:

Bashirov Mussa G.1ORCID,Akchurin Damir Sh.1ORCID,Kuvaytsev Kirill N.1,Maksimochkin Dmitry E.1

Affiliation:

1. Ufa State Petroleum Technical University

Abstract

The purpose of the study is to develop and train an artificial neural network to identify the stress-strain state and damage to the metal of power equipment based on the values of the parameters of the harmonic components of the electromagnetic-acoustic transducer signal. Materials and methods. Experimental study of the relationship between the parameters of the harmonic components of the signal of an electromagnetic-acoustic transducer with the stress-strain state and damage to the structure of standard metal samples, development of an artificial neural network and methods for its training to identify the stress-strain state and damage to the structure of the metal according to the loading diagram. Results. Analysis of changes in the microstructure and frequency models of standard steel samples used in power engineering confirmed the possibility of identifying the stress-strain state and damage to the structure of metals based on the values of the parameters of the harmonic components of the electromagnetic-acoustic transducer signal. To solve this problem, an artificial neural network has been developed and trained. After training, the effectiveness of the network in identifying the stress-strain state and damage to the structure of metals reached 92.16%, which is acceptable for the tasks of recognizing the technical condition of metal structural elements of electrical installation equipment. Conclusions. The use of an artificial neural network to identify the stress-strain state and damage to metal structures based on the harmonic parameters of the electromagnetic-acoustic transducer signal enables to identify areas of concentration of mechanical stress and damage to the metal structure at the early stage of development, thereby increasing reliability and safety operation of electrical equipment.

Funder

Russian Science Foundation

Publisher

I.N. Ulianov Chuvash State University

Subject

General Medicine,General Chemistry

Reference20 articles.

1. Akt tekhnicheskogo rassledovaniya prichin avarii na Sayano-Shushenskoi GES 17 avgusta 2009 goda [Report of technical investigation into the causes of the accident at the Sayano-Shushenskaya HPP on August 17, 2009]. Available at: http://www.gosnadzor.ru/news/aktSSG_bak.doc (Accessed Data: 2023, Aug. 7).

2. Aleshin N.P. Issledovanie vyyavlyaemosti poverkhnostnykh ob”emnykh defektov pri ul’trazvukovom kontrole s primeneniem voln Releya, generiruemykh elektromagnitno-akusticheskim preobrazovatelem [Study of the detectability of surface volumetric defects during ultrasonic testing using Rayleigh waves generated by an electromagnetic-acoustic transducer]. Defektoskopiya, 2021, no. 5, pp.22–30.

3. Analiz prichin avarii na energoustanovkakh, podkontrol’nykh organam Rostekhnadzora za 2021 god [Analysis of the causes of accidents at power plants controlled by Rostechnadzor for 2021]. Available at: http://szap.gosnadzor.ru/activity/energonadzor/nesc_sluch/Analiz%20prichin%20avarii%20za%202021.pdf (Accessed Data: 2023, Aug. 7).

4. Ayazyan G.K., Khorobrov V.R., Galiev R.M. Metod identifikatsii dinamicheskikh kharakte-ristik ob”ektov s zapazdyvaniem [Method for identifying the dynamic characteristics of objects with delay]. Avtomatizatsiya i metrologicheskoe obespechenie v neftyanoi promyshlennosti: mezhvuz. nauchy sbornik [Automation and metrological support in the oil industry: Scientific Collection]. Ufa, Publ. UNI, 1980, pp. 29–33.

5. Bashirov M.G., Bashirova E.M., Yusupova I.G., Akchurin D.Sh. Issledovanie sposobov povysheniya effektivnosti elektromagnitno-akusticheskogo preobrazovaniya sredstv diagnostiki energeticheskogo oborudovaniya [Research on ways to increase the efficiency of electromagnetic-acoustic conversion of diagnostic tools for power equipment]. Promyshlennaya energetika, 2022, no. 10, pp. 2–9.

同舟云学术

1.学者识别学者识别

2.学术分析学术分析

3.人才评估人才评估

"同舟云学术"是以全球学者为主线,采集、加工和组织学术论文而形成的新型学术文献查询和分析系统,可以对全球学者进行文献检索和人才价值评估。用户可以通过关注某些学科领域的顶尖人物而持续追踪该领域的学科进展和研究前沿。经过近期的数据扩容,当前同舟云学术共收录了国内外主流学术期刊6万余种,收集的期刊论文及会议论文总量共计约1.5亿篇,并以每天添加12000余篇中外论文的速度递增。我们也可以为用户提供个性化、定制化的学者数据。欢迎来电咨询!咨询电话:010-8811{复制后删除}0370

www.globalauthorid.com

TOP

Copyright © 2019-2024 北京同舟云网络信息技术有限公司
京公网安备11010802033243号  京ICP备18003416号-3