Power Converter Event Monitoring Using Log-Based Fault Attribution
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Solution Overview
Problem
Existing converter monitoring systems fail to accurately and efficiently identify the root cause of faults, leading to prolonged downtime and increased complexity in diagnosing issues, as they require expert analysis of log files and do not account for user-defined messages or interdependent warning and fault messages.
Innovation Solution
A method utilizing user-defined messages and machine learning to analyze log data, identifying probable technical root causes of faults by categorizing messages and allocating them to causal sources, allowing for automated root cause analysis and improved event monitoring.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If expert analysis of log files is used to diagnose faults, then diagnostic accuracy is improved, but diagnostic time and system complexity increase
Solution Approach 1:
The monitoring system performs self-diagnosis by automatically analyzing log data and identifying fault patterns without requiring expert intervention. The system serves itself by implementing automated root cause analysis capabilities that previously required human experts, thereby reducing diagnostic time while maintaining accuracy
Solution Approach 2:
The patent replaces manual expert analysis (mechanical human process) with automated computational analysis using processors and algorithms. The mechanical process of expert log file examination is substituted with electronic data processing that automatically identifies fault patterns and root causes
2Measurement precision
If comprehensive log data analysis is performed to identify root causes, then fault detection accuracy is improved, but system complexity increases
Solution Approach 1:
The patent segments the complex log data analysis task into distinct components: individual log entry parsing, pattern recognition modules, root cause identification algorithms, and hierarchical fault categorization. This segmentation allows the system to manage complexity by breaking down the analysis process into manageable, modular steps that can be processed systematically
Solution Approach 2:
The system introduces intermediary processing layers between raw log data and final fault diagnosis. These intermediaries include standardized log formats, intermediate data structures, and processing algorithms that simplify the transformation of complex raw data into actionable diagnostic information, thereby managing system complexity
3Measurement precision
If user-defined messages are incorporated into monitoring, then monitoring accuracy is improved, but system configuration complexity increases
Solution Approach 1:
The system implements a universal message handling framework that can process both standardized system-generated messages and user-defined custom messages through the same analysis pipeline. This multi-functionality allows the system to accommodate diverse message types without requiring separate processing paths, thereby improving monitoring accuracy while managing configuration complexity through a unified approach
Data Source
AI summary
A method for monitoring an event in a power converter includes using record data after starting the power converter, wherein the record data do not indicate an error for a predetermined period of time after the power converter is started. Messages are assigned to error sources, and a degree of probability is calculated for an assigning process.