Technical gazette, Vol. 27 No. 2, 2020.
Original scientific paper
https://doi.org/10.17559/TV-20190828170533
A Data-Driven Condition Monitoring of Product Quality Analysis System Based on RS and AHP
Jihong Pang
; College of Mechanical and Electrical Engineering, Wenzhou University, Wenzhou 325035, P. R. China
Ruiting Wang
; College of Mechanical and Electrical Engineering, Wenzhou University, Wenzhou 325035, P. R. China
Quan Xiao
; School of Information Management, Jiangxi University of Finance and Economics, Nanchang 330032, P.R. China
Feifei Qin
; School of Information Science and Engineering, Wenzhou Business College, Wenzhou 325035, P. R. China
Abstract
Mechanical and electrical products have complex structure and intelligent control system, their reliability plays an important role in the normal operation of security facilities. However, most manufacturers usually pay more attention to the product designing and manufacturing quality, with little interest in the intelligent fault diagnosis. The objective of this study is to develop the products quality intelligent analysis and management system based on Rough Set (RS) and Analytic Hierarchy Process (AHP). Firstly, this paper reviews the principle of hardware, software design, monitoring platform and quality analysis system to reduce the number of information transfer with computer technology. Secondly, the fault types and feature extractions of different faults of elevators are presented and simplified by using RS theory. Then, the objective weight of level index model can be obtained by AHP method, and the comprehensive analysis weight of each index is obtained by using the value of subjective and objective weight coefficients with the golden ratio. Finally, a comprehensive decision weight of the major index for quality control analysis system of many vertical elevators is presented. The results show that the data-driven condition monitoring and quality analysis system is a kind of important means to prevent a disaster of complex mechanical and electrical products.
Keywords
analytic hierarchy process; data-driven condition monitoring; fault information; mechanical and electrical products; quality analysis system; rough set
Hrčak ID:
236786
URI
Publication date:
15.4.2020.
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