scholarly journals ESTIMATION OF THE DEFECT HAZARD CLASS IN BUILDING STRUCTURES: A DECISION SUPPORT SYSTEM

2021 ◽  
Vol 17 (4) ◽  
pp. 106-114
Author(s):  
Vladislav Kats ◽  
Liubov Adamtsevich
Keyword(s):  
Acoustic Emission ◽  
Decision Support ◽  
Decision Support System ◽  
Condition Monitoring ◽  
Support System ◽  
Sufficient Conditions ◽  
Technical Condition ◽  
Building Structures ◽  
Hazard Class ◽  
Diagnostic Features

Technical condition monitoring of building structures located on hazardous facilities is a necessary requirement for their sustainable functioning. In this regard, the problem of development intellectual monitoring systems that allow to detect and classify operating defects by the hazardous level becomes very urgent. The study presents an approach of building decision support system (DSS) for detecting defects in building structures and estimation of their hazard class. Proposed approach is based on multi-criteria assessment of consecutive measurements acquired by acoustic emission method. A distinctive characteristic of the proposed approach is the ability to take into account the evolution of defects by mapping each AE time-series to diagnostic features matrix and analysing these matrices in sliding windows with overlay. Each matrix is validated by two criteria that form the necessary and sufficient conditions of the existence the evolving defects in building structure. They include the criterion for changing the number of clusters and the criterion for changing the acoustic emission activity. Proposed method was verified on the experimental data acquired from the technical condition monitoring of the vertical oil tanks. The results obtained fromthe experiment confirm the proposal that this approach can be utilized for effectively solving the problem of conditionalmonitoring of building structures located on the hazardous facilities allowing to detect and classify defects by theirhazardous level.

Decision Support System for Identifying Technical Condition of Combustion Engine

2016 ◽  
Vol 41 (3) ◽  
pp. 449-460 ◽  
Author(s):  
Adam Deptuła ◽  
Piotr Osiński ◽  
Urszula Radziwanowska
Keyword(s):  
Decision Support ◽  
Decision Support System ◽  
Support System ◽  
Input Data ◽  
Sound Pressure ◽  
Combustion Engine ◽  
Technical Condition ◽  
Decision Method ◽  
Acoustic Diagnostics ◽  
Type Of Failure

AbstractThe paper is a continuation of the publication under the title “Acoustic diagnostics applications in the study of technical condition of combustion engine” and concerns the detailed description of decision support system for identifying technical condition (type of failure) of specified combustion engine. The input data were measured sound pressure levels of specific faults in comparison to the noise generated by undamaged motor. In the article, the whole procedure of decision method based on game graphs is described, as well as the interface of the program for direct usage.

The Design and Implementation of Condition Monitoring and Decision Support System in HVDC Converter Station

2015 ◽  
Vol 734 ◽  
pp. 707-713
Author(s):  
Wei Wei Jin ◽  
Cun Yu ◽  
Xing Chao Yang ◽  
Zhan Xia Geng ◽  
Hao Ran Zhang ◽  
...  
Keyword(s):  
Decision Support ◽  
Decision Support System ◽  
Condition Monitoring ◽  
Support System ◽  
Engineering Application ◽  
Monitoring Method ◽  
Design And Implementation ◽  
On Line ◽  
Grid Construction ◽  
Condition Monitoring System

The shortcomings of current on-line condition monitoring system include: the research was concentrated in AC substation; the function was single and completely independent; the protocols were not compatible and the interface was not unified. To improve these disadvantages and better satisfy the needs of the condition monitoring in converter station, the design and implementation of condition monitoring and decision support system in HVDC converter station was realized. By studying of condition monitoring method, decision support technology and engineering application of HVDC project, the system design principle was discussed, the software structure was shown and the function realization was given in detail. The implementation realized real time monitoring for running equipments, provided reliable gist for maintenance and reduced the costs of resources. The implementation is applied to HVDC project witch has a great significance in improving the operation efficiency and promoting the smart grid construction.

A Method of Training Technical Condition Monitoring Systems in Construction

2021 ◽  
Vol 27 (7) ◽  
pp. 350-358
Author(s):  
V. A. Kats ◽  
◽  
A. A. Volkov ◽  
Keyword(s):  
Acoustic Emission ◽  
Monitoring System ◽  
Condition Monitoring ◽  
Accurate Estimate ◽  
Technical Condition ◽  
Monitoring Systems ◽  
Technical Diagnostics ◽  
Hazard Class ◽  
Condition Monitoring System ◽  
Current Monitoring

Technical condition estimation of the constructions is a relevant problem. In order to acquire comprehensive information of the testing object monitoring should be complex, providing effective and accurate estimate of the hazard class of the defects and forecasting its failure. Most of the current monitoring systems are based on acquiring and handling diagnostic via acoustic emission (AE) method. However, importantly, parameters of the acoustic emission propagated by defects depend on multiple factors such as type of the defect and its origin and the presence of noise on the testing object during data acquisition. In this regard, the problem of training the technical condition monitoring system is particularly important. In current work, we proposed a training method of monitoring systems for technical diagnostics of the constructions based on four subsequent stages: features extraction from AE data on two-time scales, features' dimensionality reduction, outliers detection and anomalies detection. Proposed method provides trained model for the detection of defects evolution in the building constructions. It has been tested on real constructions of the oil reservoir. The verification of the proposed method was provided by estimation of the accuracy metric of the trained model. Based on cross-validation, the mean error was 1.4 %. This confirms that proposed method can be effectively utilized as a part of technical condition monitoring system for more accurate forecasting hazard class of the defects and their evolution inside constructions.

Decision Support System Development Based On Android Using Analytical Hierarchy Process (AHP) Method For Determining The Middle Small Business Development Priority In Sorong City

2019 ◽  
Vol 5 (2) ◽  
pp. 25-39
Author(s):  
Luluk Suryani ◽  
Raditya Faisal Waliulu ◽  
Ery Murniyasih
Keyword(s):  
Decision Support ◽  
Small Business ◽  
Decision Support System ◽  
Support System ◽  
Analytical Hierarchy Process ◽  
System Development ◽  
Business Development ◽  
Ahp Method ◽  
Small Business Development ◽  
Hierarchy Process

Usaha Kecil Menengah (UKM) adalah salah satu penggerak perekonomian suatu daerah, termasuk Kota Sorong. UKM di Kota Sorong belum berkembang secara optimal. Ada beberapa penyebab diantaranya adalah mengenai finansial, lokasi, bahan baku dan lain-lain. Untuk menyelesaikan permasalah tersebut peneliti terdorong untuk melakukan pengembangan Aplikasi yang dapat membantu menentukan prioritas UKM yang sesuai dengan kondisi pelaku usaha. Pada penelitian ini akan digunakan metode Analitycal Hierarchy Process (AHP), untuk pengambilan keputusannya. Metode AHP dipilih karena mampu menyeleksi dan menentukan alternatif terbaik dari sejumlah alternatif yang tersedia. Dalam hal ini alternatif yang dimaksudkan yaitu UKM terbaik yang dapat dipilih oleh pelaku usaha sesuai dengan kriteria yang telah ditentukan. Penelitian dilakukan dengan mencari nilai bobot untuk setiap atribut, kemudian dilakukan proses perankingan yang akan menentukan alternatif yang optimal, yaitu UKM. Aplikasi Sistem Pendukung Keputusan yang dikembangkan berbasis Android, dimana pengguna akan mudah menggunakannya sewaktu-waktu jika terjadi perubahan bobot pada kriteria atau intensitas.  Hasil akhir menunjukkan bahwa metode AHP berhasil diterapkan pada Aplikasi Penentuan Prioritas Pengembangan UKM.

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