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Fault detection and diagnosis of the interconnects are crucial forprognostics and health management (PHM) of electronics. Traditional methods,which rely on electronic signals as prognostic factors, often struggle toaccurately identify the root causes of defects without resorting to destructivetesting. Furthermore, these methods are vulnerable to noise interference, whichcan result in false alarms. To address these limitations, in this paper, wepropose a novel, non-destructive approach for early fault detection andaccurate diagnosis of interconnect defects, with improved noise resilience. Ourapproach uniquely utilizes the signal patterns of the reflection coefficientacross a range of frequencies, enabling both root cause identification andseverity assessment. This approach departs from conventional time-seriesanalysis and effectively transforms the signal data into a format suitable foradvanced learning algorithms. Additionally, we introduce a novel severityrating ensemble learning (SREL) approach, which enhances diagnostic accuracyand robustness in noisy environments. Experimental results demonstrate that theproposed method is effective for fault detection and diagnosis and has thepotential to extend to real-world industrial applications.

Non-destructive Fault Diagnosis of Electronic Interconnects by Learning Signal Patterns of Reflection Coefficient in the Frequency Domain