Alarm Threshold Monitoring for Reliable Technical Status Detection
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Solution Overview
Problem
Current alarm systems in technical equipment generate excessive and unreliable alarms, known as alarm floods, due to their reliance on statistical analysis that does not account for process knowledge, leading to difficulties for operators in determining the overall technical status and identifying root causes.
Innovation Solution
A computer-implemented method that calculates univariate distances of signals from technical systems relative to alarm thresholds, aggregating these distances into an abnormality indicator to provide operators with a simplified and reliable assessment of the technical status, reducing false positives and enabling timely corrective actions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If statistical data-driven methods are used for process monitoring, then alarm notifications are generated, but the number of false positives increases and reliability decreases
Solution Approach 1:
The patent introduces an intermediary assessment module that acts as a mediator between the statistical monitoring system and the alarm notification system. This module evaluates multiple signals and their interrelationships before triggering an alarm, filtering out false positives while maintaining rapid detection capability. The intermediary layer processes the statistical outputs and applies additional validation logic before generating final alarm notifications.
2Measurement precision
If multiple alarm activations are generated for a single root cause, then comprehensive monitoring is achieved, but alarm floods occur and operator ability to deal with alarms decreases
Solution Approach 1:
The patent merges multiple individual alarm activations into a single consolidated alarm notification when they share a common root cause. The system analyzes the relationships between different signals and their deviations, identifying when multiple alarms are correlated and caused by the same underlying issue. This consolidation reduces alarm floods while maintaining comprehensive monitoring coverage.
Solution Approach 2:
The patent implements a universal alarm assessment mechanism that handles multiple types of signals and abnormality patterns through a single integrated evaluation framework. This multi-functional approach allows the system to process diverse signal types (temperature, pressure, flow, etc.) and apply unified assessment criteria, reducing the need for separate alarm pathways and thereby reducing overall alarm complexity for operators.
3Device complexity
If statistical analysis without process knowledge is used, then analysis simplicity is maintained, but false positives increase and abnormality identification becomes difficult
Solution Approach 1:
The patent applies local quality by integrating process-specific knowledge into specific evaluation modules rather than requiring complete process knowledge throughout the entire system. Each signal evaluation can use targeted process knowledge relevant to that particular parameter, maintaining overall system simplicity while improving detection reliability where it matters most. This allows selective incorporation of domain expertise without overwhelming system complexity.
Data Source
AI summary
A computer-implemented method for determining an abnormal technical status of a technical system includes: receiving, from the technical system, a plurality of signals, each signal being sampled over time and reflecting the technical status of at least one system component; computing, for each signal with associated high and low alarm thresholds obtained from an alarm management system, at every sampling time point, a univariate distance to its associated alarm thresholds as a maximum of the distances between a value of the respective signal and its associated alarm thresholds to quantify a degree of abnormality for the respective at least one system component; computing, at every sampling time point, based on the univariate distances at the respective sampling time points, an aggregate abnormality indicator reflecting the technical status of the technical system; and providing, to an operator, a comparison of the aggregate abnormality indicator with a predetermined abnormality threshold.