Contextual Analytics Mapping for Root-Cause Alarm Remediation
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Solution Overview
Problem
The complexity and volume of operational data from industrial plants make it difficult to identify and remediate interrelated warnings or alarms, often leading to sub-optimal plant performance, where individual remediation of alarms may worsen other issues.
Innovation Solution
A method and system utilizing an analysis engine to collect, analyze, and correlate operational data, determining correlations and tasks to improve plant operations, including automatic adjustments to equipment parameters, and providing contextual analytics mapping to prioritize and visualize remediation actions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If operational data from industrial plants is collected and monitored, then plant performance and safety can be improved, but the complexity and volume of data make it difficult to identify and remediate interrelated warnings or alarms
Solution Approach 1:
The patent combines multiple alarms and warnings into alarm sequences that represent interrelated problems. By merging individual alarm signals into structured sequences with defined start and end conditions, the system reduces data complexity while maintaining reliability, enabling operators to identify root causes more efficiently.
Solution Approach 2:
The patent introduces alarm sequences as an intermediary layer between raw operational data and operator decision-making. These sequences act as mediators that process, organize, and contextualize multiple alarms, transforming complex data volumes into manageable information structures that improve plant performance monitoring.
2Ease of repair
If individual alarms are remediated separately, then specific warnings can be addressed, but interrelated problems may worsen and plant performance may deteriorate
Solution Approach 1:
The patent defines alarm sequences with predetermined start and end conditions that identify complete problem cycles before remediation begins. By establishing these boundaries in advance, the system enables operators to understand the full scope of interrelated problems before taking action, preventing premature or isolated remediation that could worsen plant performance.
Solution Approach 2:
The patent uses alarm sequences to provide feedback about the state and resolution of interrelated alarms. By tracking when sequences start and end, the system gives operators feedback about the effectiveness of remediation actions and the interconnections between alarms, enabling more reliable plant performance management.
3Loss of information
If the volume of operational data increases, then more comprehensive monitoring is achieved, but the speed and effectiveness of identifying root causes decreases
Solution Approach 1:
The patent segments the continuous stream of operational data into discrete alarm sequences with defined boundaries. This segmentation organizes comprehensive monitoring data into manageable units that can be quickly analyzed, reducing the time to identify root causes while maintaining information completeness about plant conditions.
Solution Approach 2:
The patent adds a temporal dimension to alarm data by defining sequences with start and end conditions. This dimensional transformation organizes data not just by type but by temporal progression and interrelation, enabling faster root cause identification while preserving comprehensive information about plant operations over time.
Data Source
AI summary
A computing system for receiving operational data including process parameters generated by sensors in a plant. An analysis engine uses the operational data to automatically provide a first listing of worst performing process parameters, that when a selected poor performing process parameter is chosen generates a ranked filtered view of equipment parameters for associated processing equipment that may be affected by the selected poor performing parameter and a filtered view of recommendations for recognizing action(s) to fix the associated processing equipment and/or the selected poor performing process parameter, and/or a second listing of worst performing processing equipment that when a selected poor performing processing equipment is chosen generates a ranked filtered view of suspected process parameters that may be affected by the selected poor performing processing equipment along with a filtered view of recommendations for recognizing action(s) to fix the selected poor performing processing equipment and the suspected process parameters.