Industrial Alarm Classification Using Real-Time ML Streaming
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Solution Overview
Problem
Current systems fail to provide real-time alerting and insights for industrial asset events due to multiple hardware and software partitions, preventing timely observations and decision-making.
Innovation Solution
The use of machine learning approaches to predict and classify industrial alarms by processing real-time and historical data streams, enabling continuous streaming of alarm detection results for visualization and timely decision-making.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If multiple systems and partitions (hardware and software) are used for industrial alarm management, then system functionality and coverage are improved, but real-time alerting capability deteriorates
Solution Approach 1:
The patent combines multiple alarm management systems and partitions into a unified alarm management platform that processes data from multiple sources simultaneously. This integration enables real-time alerting by eliminating the delays associated with multiple separate systems while maintaining comprehensive functionality across all industrial assets.
2Device complexity
If traditional alarm systems are used without machine learning, then system simplicity is maintained, but prediction accuracy and classification capability deteriorate
Solution Approach 1:
The patent introduces machine learning models as intermediary components between raw alarm data and final alarm classifications. These models act as intelligent mediators that automatically learn patterns from historical data and provide accurate predictions without requiring complex manual configuration, thus maintaining relative system simplicity while dramatically improving prediction accuracy.
Solution Approach 2:
The machine learning models continuously self-train and self-improve by processing incoming alarm data and historical records. This self-service capability allows the system to automatically enhance its prediction accuracy over time without requiring manual intervention or complex reconfiguration, balancing simplicity with high precision.
3Speed
If real-time data streaming is implemented for all industrial assets, then observation timeliness is improved, but data processing load and system resource requirements deteriorate
Solution Approach 1:
The patent extracts and processes only the most critical alarm-related features and events from the continuous data stream, rather than analyzing every data point in real-time. This selective extraction approach maintains timely observation of critical events while significantly reducing the overall data processing load and resource requirements.
Solution Approach 2:
The system applies machine learning models selectively to high-priority alarm conditions and critical assets, rather than uniformly processing all data at maximum intensity. This partial action approach ensures real-time response for critical events while conserving computational resources for less urgent matters.
Data Source
AI summary
An occurrence of a predetermined event is detected within a stream of data and a classification for the event is determined. A message is sent to an application when the predetermined event is detected. The message is received at the application and the message is visualized to a user of the application. The stream of data from the industrial machine to the transceiver circuit, to the control circuit, and to the application occurs in real-time without substantial interruption allowing the user of the application to make decisions and determine insights concerning the industrial machine in real-time.