Log File Analysis Method for Automation Systems
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Solution Overview
Problem
The overwhelming number of log files generated in automation systems of large technical installations, such as power plants, makes it difficult for operators to analyze and evaluate events effectively due to data complexity and dependencies, hindering timely detection of deviations and potential failures.
Innovation Solution
A method that reduces the number of log files by filtering and grouping relevant logs, using log codes and key parameters to identify and combine sequences, allowing for simplified analysis and pattern recognition, thereby facilitating quick evaluation of events.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If automated control and monitoring systems generate log files for every event in the technical system, then complete event recording is achieved, but the number of log files becomes overwhelming and difficult to analyze
Solution Approach 1:
The patent segments the overwhelming number of log files into meaningful groups based on event types, time periods, and relevance criteria. By dividing the log data into manageable segments with specific characteristics, operators can analyze smaller, organized subsets rather than being overwhelmed by the complete set of logs, thus resolving the contradiction between complete recording and analysis complexity.
Solution Approach 2:
The patent extracts and highlights only the most relevant log files and events from the complete set, separating critical information from routine data. This extraction process identifies and isolates significant events that require operator attention, removing unnecessary noise and reducing the effective number of logs that need detailed analysis while maintaining complete recording for reference.
2Reliability
If multiple measured values from defective sensors and components are recorded, then complete system monitoring is maintained, but the number of additional log files increases and complicates operator understanding
Solution Approach 1:
The patent applies local quality by treating different log files and events with different levels of analysis and presentation based on their relevance and importance. Critical events from defective components receive special handling and prominent display, while routine measurements are grouped or summarized. This differential treatment maintains complete monitoring data while optimizing the presentation for operator understanding based on local event characteristics.
Solution Approach 2:
The patent introduces an intermediary processing layer that automatically analyzes, filters, and pre-processes log files before presenting them to operators. This intermediary system correlates events, identifies patterns, and prepares synthesized presentations of multiple measured values, acting as a mediator between the raw data from defective sensors and the operator's interpretation needs, thus maintaining monitoring completeness while easing operational complexity.
3Loss of information
If all log files are presented to operators for analysis, then complete event information is available, but the time required for analysis and evaluation increases significantly
Solution Approach 1:
The patent performs preliminary actions by automatically pre-processing, filtering, and organizing log files before operator review. Events are pre-categorized, correlated, and ranked by significance, with critical events identified and highlighted in advance. This preliminary preparation reduces the time required for operator analysis while ensuring that complete event information remains available for thorough evaluation when needed.
Data Source
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AI summary
The invention relates to a method for analyzing and/or evaluating at least one event (E) of a technical plant from a plurality of generated log files (P1 to Pn) of an automation system (1) of the technical plant, comprising the following steps: determining a number of relevant log files (PR1 to PRx) based on at least one log parameter, encoding/marking the relevant log files (PR1 to PRx) by means of at least one associated log code (C), specifying at least one key parameter (S) for the analysis and/or evaluation, identifying multiple, in particular duplicate, relevant log files (PR1 to PRx) based on the at least one key parameter (S) from the relevant log files (PR1 to PRx).and grouping the relevant log files (PR1 to PRx) into a number of sequences (SQ) of relevant log files (PR1 to PRx) based on the repetition rate of the key parameter (S) in a time window to be analyzed.