Infrastructure Data Trend Analysis for Early Component Failure Prediction
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Solution Overview
Problem
Display-apparatus-manufacturing apparatuses face increased manufacturing time and cost due to unpredictable component failures, necessitating a system to accurately predict component failures for timely replacement.
Innovation Solution
An automatic analysis system for infrastructure operation data that includes a scheduler, data extractor, trend-coefficient calculator, and determiner to predict component failures by calculating relative standard deviations, trend coefficients, and determining abnormal conditions through linear regression analysis, with an alarm for generating warning signals.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If component failure prediction is not implemented, then the manufacturing apparatus operates without additional monitoring systems, but component failures cause increased manufacturing time and cost
Solution Approach 1:
The system uses the infrastructure's own operational data (vibration, temperature, current) to predict its own failures, eliminating the need for external monitoring equipment. The existing sensors and controllers serve dual purposes: normal operation control and failure prediction data collection.
Solution Approach 2:
The patent replaces complex mechanical monitoring systems with data-driven predictive analytics. Instead of using additional mechanical sensors and manual inspection procedures, the system uses linear regression analysis on electrical and operational parameters to predict failures.
2Measurement precision
If traditional monitoring methods are used, then the system structure remains simple, but failure prediction accuracy is insufficient for timely component replacement
Solution Approach 1:
The system performs preliminary failure prediction by continuously analyzing operational data trends before actual failures occur. The linear regression model calculates predicted values and compares them with actual values to detect anomalies early, enabling proactive component replacement.
Solution Approach 2:
The system implements feedback by comparing predicted operational parameters with actual measured values. When the absolute difference exceeds a threshold, the system generates warnings, creating a closed-loop monitoring system that continuously refines its predictions based on actual performance.
3Reliability
If comprehensive data collection is performed, then prediction accuracy improves, but data processing complexity and computational load increase
Solution Approach 1:
The system extracts only the most critical operational parameters (vibration, temperature, current) needed for failure prediction, rather than collecting and processing all possible data from the infrastructure. This selective extraction reduces computational complexity while maintaining prediction accuracy.
Solution Approach 2:
The patent transforms raw operational data into meaningful predictive indicators by calculating rates of change and comparing them against threshold values. This parameter transformation simplifies the data processing requirement while enhancing the ability to detect emerging failures.
Data Source
AI summary
Provided are an automatic analysis system and automatic analysis method for infrastructure operation data. The automatic analysis system includes a scheduler configured to determine a first designated period, a second designated period, and a third designated period, a data extractor configured to calculate relative standard deviations based on data of one or more components according to an operation of an infrastructure during the first designated period, and configured to select a representative value from among the relative standard deviations calculated during the second designated period including the first designated period, a trend-coefficient calculator configured to calculate a trend coefficient during the third designated period through linear regression analysis based on representative values selected by the data extractor during the third designated period including the second designated period, and a determiner configured to determine whether the infrastructure is predicted to be abnormal based on the trend coefficient.