Splice Life Prediction via Zero-Crossing Waveform Analysis
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Solution Overview
Problem
Current methods for inspecting the integrity of power transmission network splices are inadequate for predicting life expectancy and preventing catastrophic failures due to environmental conditions or material degradation, leading to increased transmission losses and costly replacements.
Innovation Solution
A method that monitors splice degradation by establishing a baseline signal and analyzing zero-crossing points, angular displacement, voltage, and period changes over time to predict the remaining useful life and time to failure of splices, using probes for voltage and current measurements and an algorithm to generate a decay rate curve.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional mechanical splice methods are used to join power transmission components, then the splice can be installed and connected, but the splice integrity cannot be determined after mechanical joining and no means exist to inspect splice integrity at initial install or predict future breakdowns
Solution Approach 1:
The patent replaces mechanical inspection methods with electrical signal analysis. By injecting test signals through the splice and analyzing the resulting voltage and current waveforms, the system detects splice degradation through electrical characteristics rather than physical examination. This substitution enables non-intrusive, continuous monitoring of splice integrity without requiring mechanical disassembly or physical access to the splice point.
Solution Approach 2:
The patent introduces test signal injection equipment and analysis algorithms as intermediaries between the splice and the monitoring system. These intermediaries convert invisible electrical degradation into measurable waveform characteristics (amplitude, frequency, phase shifts), enabling indirect detection of splice conditions. The intermediary processing system translates raw electrical signals into actionable integrity assessments.
2Reliability
If splice monitoring is implemented to predict life expectancy, then catastrophic failures can be prevented, but the complexity of the monitoring system increases beyond simple zero-crossing methods
Solution Approach 1:
The patent segments the monitoring approach into distinct analytical components: zero-crossing detection, amplitude analysis, frequency spectrum analysis, and phase relationship measurement. Each component processes a specific aspect of the waveform data independently, then the results are integrated to form a comprehensive splice health assessment. This segmentation allows complex analysis to be performed through multiple simple, specialized measurement functions.
Solution Approach 2:
The patent applies multiple analysis methods (amplitude, frequency, phase, zero-crossing) beyond what a single simple method could provide. By implementing excessive measurement capabilities and analyzing multiple waveform characteristics simultaneously, the system achieves superior prediction accuracy. The redundant measurements provide cross-validation and more comprehensive degradation detection than any single method alone.
3Loss of time
If preventive maintenance is deployed with time to failure information, then manpower and resources can be forecasted, but current methods provide only good or bad splice determination without life expectancy prediction
Solution Approach 1:
The patent performs preliminary analysis by establishing baseline waveform characteristics for new, healthy splices and storing these reference values. By comparing ongoing measurements against these pre-established baselines, the system proactively identifies degradation trends before failure occurs. This preliminary baseline creation enables predictive rather than reactive maintenance scheduling.
Solution Approach 2:
The patent implements continuous feedback by repeatedly measuring waveform characteristics, comparing them to baselines and previous measurements, and updating degradation assessments over time. This feedback loop tracks the progression of splice degradation and updates life expectancy predictions dynamically. The system learns from ongoing measurements and adjusts predictions as degradation patterns emerge, enabling increasingly accurate maintenance timing forecasts.
Data Source
AI summary
A monitoring apparatus, and method of use, that is capable of determining the joint characteristics by means of waveforms shifts at zero-crossing angular distortions through predictive failure algorithm specific to circuit under test.