AI Alarm Rationalization for Industrial Process Control
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Solution Overview
Problem
Conventional alarm rationalization processes in industrial plants are manual, time-consuming, and require significant effort from senior process engineers, taking weeks or months to configure alarms, which is inefficient and labor-intensive.
Innovation Solution
An AI-based alarm management system that automates the alarm rationalization process by using an AI alarm engine to evaluate alarms, generate optimal settings, and populate a Master Alarm Database (MADB) based on predefined alarm philosophy, reducing configuration time from 15-20 minutes per alarm to near-zero.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If manual alarm rationalization is performed by senior process engineers, then alarm configuration accuracy and reliability are improved, but time consumption and labor intensity increase significantly
Solution Approach 1:
An AI-based alarm management system acts as an intermediary between process control data and alarm configuration. The system automatically analyzes process data, identifies alarm conditions, and generates alarm configurations based on predefined philosophies, eliminating the need for manual engineer intervention while maintaining configuration quality
Solution Approach 2:
The manual mechanical process of engineer review and configuration is replaced with an automated computational system. The AI engine processes process control data, applies alarm philosophies, and generates configurations automatically, substituting human cognitive work with machine-based analysis and decision-making
2Reliability
If comprehensive alarm rationalization is performed for all alarms in a plant, then alarm system performance is improved, but the complexity and duration of the rationalization process increase
Solution Approach 1:
The comprehensive alarm rationalization process is segmented into automated modules: data collection from process control systems, AI-based analysis of alarm conditions, application of alarm philosophies, and generation of configurations. Each segment handles specific tasks automatically, reducing overall process complexity while maintaining comprehensiveness
Solution Approach 2:
The AI-based alarm management system provides universal functionality that handles multiple alarm rationalization tasks simultaneously - analyzing process data, identifying alarm conditions, applying various alarm philosophies, and generating configurations across the entire plant, replacing multiple specialized manual processes with a single multi-functional system
3Manufacturing precision
If detailed alarm rationalization documentation is created for each alarm, then alarm management quality is improved, but the labor effort and time required increase
Solution Approach 1:
The system performs self-service by automatically generating complete alarm documentation including alarm conditions, thresholds, descriptions, and configurations based on process control data and predefined philosophies. The AI engine independently completes the entire documentation process without requiring manual engineer input, achieving both high quality and high speed
Solution Approach 2:
The system performs preliminary analysis of process control data to pre-identify alarm conditions and parameters before formal configuration is needed. By preparing alarm candidates and parameters in advance based on historical data and process understanding, the system enables rapid final configuration while maintaining comprehensive documentation quality
Data Source
AI summary
An alarm rationalization system receiving and responsive to industrial process information collected from a process control system for identifying one or more alarms and executing an artificial intelligence (AI) alarm engine. The AI alarm engine builds a process/domain model based on the received industrial process information and historized alarm information to evaluate the alarms in accordance with a predefined alarm philosophy. The AI alarm engine then generates a plurality of alarm definitions based on the model to optimize the alarms. The AI alarm engine automatically populates a Master Alarm Database (MADB) with the alarm definitions. The alarms are then rationalized based on the alarm definitions stored in the MADB.