Alarm Optimization System Using Historical Data Analysis
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Solution Overview
Problem
Alarm systems in monitoring and control systems often generate excessive and late notifications, overwhelming operators and leading to decreased attention and potential overlooking of critical messages, due to their inability to accurately distinguish between primary and secondary alarms and lack of effective data utilization.
Innovation Solution
The method optimizes alarm systems by using historical process measurement values to map analog data to binary events, identifying causal relationships, and calculating alarm limits based on past activations, allowing for more precise suppression of redundant alarms and earlier detection of impending issues.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If alarm systems report all generated alarms to ensure complete information delivery, then information completeness is improved, but operator overload and attention decrease
Solution Approach 1:
The alarm system segments alarms into different categories (primary alarms, secondary alarms, causal alarms) and applies different handling rules to each segment. Primary alarms that require operator action are reported, while secondary alarms that are consequences of primary alarms are suppressed, thus maintaining information completeness for critical issues while reducing overall alarm volume to prevent operator overload.
Solution Approach 2:
An intermediary alarm management system is introduced between the process control system and operators. This intermediary analyzes alarm relationships, identifies causal connections, and selectively filters alarms before presenting them to operators, thereby maintaining essential information while reducing the burden on operators.
2Ease of operation
If alarm suppression mechanisms are implemented to reduce alarm volume, then operator overload is reduced, but information completeness may be compromised
Solution Approach 1:
The alarm suppression mechanism incorporates feedback loops that continuously monitor alarm patterns and relationships. The system learns from historical data about which alarms are causally related and adjusts suppression rules accordingly, ensuring that critical information is never suppressed while systematically reducing redundant alarms that contribute to operator overload.
Solution Approach 2:
The system performs preliminary analysis of alarm relationships before alarms reach operators. By pre-identifying causal connections and classifying alarms as primary or secondary, the system prepares suppression decisions in advance, ensuring that only truly critical alarms are reported while maintaining information completeness for actionable issues.
3Device complexity
If binary data is used for alarm analysis to simplify processing, then processing complexity is reduced, but analysis accuracy decreases
Solution Approach 1:
The system changes the parameter representation from simple binary alarm states to multi-dimensional parameters including temporal patterns, alarm relationships, and process context. This allows for more accurate analysis of alarm causality and relationships while maintaining manageable processing complexity through structured parameter organization and efficient algorithms.
Data Source
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AI summary
The invention relates to a method and a device suitable for the execution thereof, wherein historical process measurement values (12, 22, 32) are evaluated and are used for the generation of alarm suppression rules (11), and - in connection with predetermined requirements (13, 23, 33) - also for the testing, evaluation and optimization of alarm configuration parameters (31). The values are further used to identify predictive alarms. The results obtained from the methods or from the device are suitable for the configuration of optimized alarm configuration parameters (35) and alarm suppression rules (11) in control systems.