Intelligent Detection Methods of Electrical Connection Faults in RF Circuits

Abstract

Printed circuit boards (PCBs) have a large number of electrical connection nodes. Exposure to harsh environments may lead to connection faults in these nodes. In the present work, intelligent detection methods for electrical connection faults were studied. Specifically, the fault characteristics of connectors, bonding wires and solder balls in the frequency domain were analyzed. The reflection and transmission parameters of an example filter circuit with electrical connection faults were calculated using the Simulation Program with Integrated Circuit Emphasis (SPICE). With these obtained electrical parameters, three machine learning algorithms were used to detect example electrical connection faults for the example circuit. Based upon the performance evaluations of the three algorithms, one can conclude that machine-learning-based intelligent fault detection is a promising technique in diagnosing circuit faults due to electrical connection issues with high accuracy and lower time cost as compared to current manual processes.

A Boundary Scan Test Vectors Optimization Method Based on Improved GA-AO* Approach Considering Fault Probability Model

“…The boundary scan test is an advanced technique for digital circuit fault detection, which is widely used in large-scale digital circuit tests due to its fast and efficient testing advantages [1][2][3][4][5]. However, with the increase in IC density and complexity, the scale of boundary scan test vectors also increases rapidly, which leads to a decrease in test efficiency and fault detection precision [6,7].…”

Special Issue on Intelligent Diagnostic and Prognostic Methods for Electronic Systems and Mechanical Systems

“…This Special Issue contains 10 papers covering various areas of fault diagnoses and prognostics, including the generation of excitation signals for fault detection circuits, estimations of weak fault signature signals, fault diagnosis methods, and fault prognostic methods. Wang, Z. et al [1] investigated an intelligent detection method for electrical connection faults in RF circuits, using three machine learning methods-support vector machine (SVM), logistic regression (LR), and gradient boosting decision tree (GBDT)-for a filter circuit. Chen, J. et al [2] added uncertainties to the health assessment model of the flight control system and established a health assessment model of the flight control system under uncertainty conditions.…”