Processing Plant Fault Analysis Using Linked Process Values
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Solution Overview
Problem
Existing methods for fault analysis and deviation prediction in process engineering plants, such as paint shops, are inefficient and unreliable, lacking in automated detection and cause identification of fault situations.
Innovation Solution
A method for fault analysis that automatically detects fault situations, determines their causes, and predicts process deviations by linking process values based on criteria like plant position, historical data, and user selection, using machine learning and expert rules to create predictive models.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If automated fault detection and cause identification systems are implemented, then fault analysis reliability is improved, but device complexity increases
Solution Approach 1:
The system performs automated self-diagnosis by automatically detecting fault situations, identifying causes, and linking relevant process values without requiring manual intervention. The fault analysis system autonomously queries historical data, applies evaluation criteria, and generates diagnostic results, enabling the system to serve itself in fault detection and analysis.
Solution Approach 2:
The system utilizes feedback mechanisms by continuously monitoring process data, comparing current states with historical fault patterns, and automatically adjusting diagnostic conclusions based on evaluated criteria. The feedback loop enables the system to learn from historical fault situations and improve diagnostic accuracy over time through iterative analysis.
2Measurement precision
If comprehensive process data is collected and analyzed for fault detection, then measurement precision is improved, but loss of time increases
Solution Approach 1:
The system performs preliminary actions by pre-collecting and organizing process data during normal operation, maintaining ready-to-use historical fault databases, and pre-establishing evaluation criteria. When a fault occurs, the system can immediately query pre-prepared data and apply predefined analysis methods, significantly reducing the time required for fault diagnosis while maintaining high measurement precision.
3Manufacturing precision
If historical fault data is extensively queried and compared, then manufacturing precision is improved, but loss of time increases
Solution Approach 1:
The system applies partial action by selectively querying only the most relevant historical fault data based on current fault symptoms and pre-established evaluation criteria. Instead of exhaustively analyzing all historical data, the system focuses on pertinent cases that match current conditions, achieving high diagnostic accuracy while minimizing data processing time through targeted queries.
Data Source
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AI summary
In order to provide a method for fault analysis in a processing plant, for example a painting plant, by means of which fault situations can be easily and reliably analysed, according to the invention said method comprises the following: identifying, in particular automatically, a fault situation in the processing plant (101); storing, in a fault database (136), a fault situation data record for each fault situation that has been identified; automatically determining a fault cause for the fault situation, and/or automatically determining process values relevant to the fault situation, on the basis of the fault data record for the respective fault situation that has been identified.