A Three-Dimensional Complex Measurement Model-Based Avionic Radio-Frequency Power Source Health Assessment Method. (19th October 2020)

• Record Type: Journal Article
• Authors: Huo, Lin; Li, Shiqi; Fei, Simiao; Lyu, Chuan
• Other Names: Lv Zhihan Academic Editor.
• Is Part Of: Complexity. Volume 2020(2020)
• Publication Date: 2020-10-19
• Subjects: Chaotic behavior in systems -- Periodicals; Complexity (Philosophy) -- Periodicals; 003
• DOI: 10.1155/2020/6369729
• Languages: English
• ISSNs: 1076-2787

Abstract

Abstract : Health assessment is an important part of PHM technology, which is crucial to product state monitoring and management. With the complexity of product structure and diversification of functions, the product health presents uncertainty under the complex influence of multiple factors. Nowadays, the general health assessment method is relatively simple and only considers the product function completeness, and the assessment effect accuracy still needs to be strengthened in the actual working environment. Most assessment parameters and assessment methods are selected ignoring the impact of environmental changes and operating time performance. Therefore, a new health measurement method for the avionic radio-frequency power source based on a three-dimensional complex measurement model is proposed. Firstly, the product health definition is proposed, and the health connotation is analyzed from three-dimensional aspects containing functional integrity, environmental adaptability, and temporal sustainability. According to the function and structure characteristics of radio-frequency power source, the health characterization parameters are then obtained and the health assessment parameter system is established. Finally, the three-dimensional complex measurement model is given, and the RF power source health assessment is carried out comprehensively from integrity, stability, and reliability aspects. The three-dimensional health assessment method provides a new way to solve the uncertainty of product health state under multiple factor effects, which is conducive to the targeted optimization management strategy.