A Data-Driven Condition Monitoring of Product Quality Analysis System Based on RS and AHP

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.

Construction of an Integrated Framework for Complex Product Design Manufacturing and Service Based on Reliability Data

“…Pang JH et al [33] developed an intelligent product quality analysis and management system based on rough set (RS) and analytic hierarchy process (AHP), and the results showed that a data-driven condition monitoring and quality analysis system is an important tool for preventing disasters in complex electromechanical products. Wang YR et al [34] proposed an integrated solution for complex product manufacturing operation and maintenance based on DT for the lack of deep integration between manufacturing operation and maintenance and information island in manufacturing and operation and maintenance links, and they demonstrated the feasibility and effectiveness of the integrated approach by combining the bogie failure prediction of a model of rolling stock.…”