Big Data Dielectric Breakdown Failure Analysis
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Solution Overview
Problem
Conventional methods for analyzing the cause of dielectric breakdown in vehicles are inefficient, requiring multiple disassembly and repair processes, leading to increased time and costs, and often result in incorrect or unnecessary repairs due to inaccurate failure identification.
Innovation Solution
An apparatus and method utilizing big data analysis that monitors dielectric resistance values and travel information from sensors, calculates influence indexes for failure cause factors, and generates analysis results to accurately identify the cause of dielectric breakdown, reducing the need for extensive disassembly and minimizing incorrect repairs.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional failure analysis methods are used requiring device disassembly and reassembly, then failure cause can be identified through physical inspection, but time and costs for analysis and repair are increased
Solution Approach 1:
The system performs preliminary monitoring of dielectric resistance values and collection of operational data before failure occurs. By continuously tracking resistance changes and storing operational context (temperature, humidity, load conditions), the system prepares failure analysis data in advance, eliminating the need for time-consuming disassembly and physical inspection after failure occurs.
Solution Approach 2:
The patent replaces mechanical disassembly and physical inspection methods with electrical measurement-based diagnosis. By monitoring dielectric resistance values and analyzing operational data patterns, the system identifies failure causes without requiring physical access to internal components, thus eliminating time loss from disassembly and reassembly.
2Reliability
If multiple devices are demounted and disassembled to analyze failure causes, then comprehensive failure analysis can be performed, but time and costs for analysis and repairing are increased
Solution Approach 1:
The system substitutes mechanical disassembly with electrical measurement and data analysis. By monitoring dielectric resistance values and correlating them with operational data, the system achieves comprehensive failure analysis while maintaining high repair efficiency, eliminating the need to demount multiple devices.
Solution Approach 2:
The patent introduces dielectric resistance measurement as an intermediary parameter that provides indirect but comprehensive information about device health. This intermediary measurement allows analysis of failure causes without direct physical access to internal components, maintaining analysis completeness while improving productivity.
3Measurement precision
If dielectric resistance value monitoring and big data analysis are implemented, then failure cause identification accuracy is improved, but system complexity is increased
Solution Approach 1:
The system uses a multi-functional approach where the same sensor network and data processing infrastructure serves both operational monitoring and failure diagnosis purposes. The dielectric resistance measurement system simultaneously tracks device health status and provides diagnostic data, eliminating the need for separate specialized equipment and reducing overall system complexity despite improved accuracy.
4Measurement precision
If comprehensive data collection and big data processing are performed, then failure analysis accuracy is improved, but data processing time and computational resources are increased
Solution Approach 1:
The system performs preliminary organization and storage of operational data and dielectric resistance measurements as they are collected during normal operation. By pre-structuring the data with proper timestamps, operational conditions, and resistance value correlations, the system enables rapid failure cause identification without requiring extensive data processing time when failure occurs.
Data Source
AI summary
An apparatus and a method for analyzing a cause of a failure due to a dielectric breakdown based on big data are provided. A failure cause factor data set, a normal state data set, or a state recovery data set is generated and transmitted to a big data server when a dielectric resistance value is measured to be a minimum normal value or less. A cause of a failure is analyzed by receiving data corresponding to the data sets from the big data server, calculating influence indexes for failure cause factors, and selecting a failure cause factor based on the basis of the influence indexes.