Analytic Passive Intermodulation Behavior on the Coaxial Connector using Monte Carlo Approximation
Abstract
This paper presents a novel passive intermodulation (PIM) prediction method considering random contact behavior using a Monte Carlo method for a coaxial connector. A smart contact model for a contact unit at a microcosmic level is proposed. Using Monte Carlo approximation and micromeasurements, different random distributed contact samples for different contact components inside the coaxial connector are reconstructed. In the experiment, PIM on inner and outer conductor was tested and compared with predication. A good agreement proves the proposed PIM prediction method is efficient. Rather than generating a single PIM prediction value, this method will give a PIM confidence interval for all the potential PIM values considering the contact force statistical behavior. The work will help analyze fluctuated PIM on coaxial connectors and inspire a new method to predict PIM risk.
Recommended Citation
X. Chen et al., "Analytic Passive Intermodulation Behavior on the Coaxial Connector using Monte Carlo Approximation," IEEE Transactions on Electromagnetic Compatibility, vol. 60, no. 5, pp. 1207 - 1214, Institute of Electrical and Electronics Engineers (IEEE), Oct 2018.
The definitive version is available at https://doi.org/10.1109/TEMC.2018.2809449
Department(s)
Electrical and Computer Engineering
Research Center/Lab(s)
Electromagnetic Compatibility (EMC) Laboratory
Keywords and Phrases
Connectors (structural); Contact resistance; Electric conductors; Forecasting; Intermodulation distortion; Intermodulation measurement; Metals; Coaxial connectors; Confidence interval; Force; Micromeasurements; Monte-carlo approximations; Passive intermodulation; Prediction methods; Statistical behavior; Monte Carlo methods
International Standard Serial Number (ISSN)
0018-9375; 1558-187X
Document Type
Article - Journal
Document Version
Citation
File Type
text
Language(s)
English
Rights
© 2018 Institute of Electrical and Electronics Engineers (IEEE), All rights reserved.
Publication Date
01 Oct 2018