AI Anomaly Detection Across Production Process Indicators
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Solution Overview
Problem
Complex industrial plants require advanced anomaly detection systems to identify deviations in process variables, as existing solutions often necessitate highly qualified personnel and struggle with increasing data volumes, leading to potential losses and inefficiencies.
Innovation Solution
An apparatus and method utilizing trained artificial intelligence to detect and predict anomalies across multiple performance indicators, integrating machine learning algorithms and temporal data analysis to support operational and business objectives, with the ability to prioritize and automate countermeasures based on relevance.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional anomaly detection methods are used, then highly qualified personnel are required for evaluation, but this increases operational complexity and costs
Solution Approach 1:
The system enables self-service anomaly detection through automated AI analysis. The anomaly detector with trained artificial intelligence automatically evaluates sensor data and identifies anomalies without requiring highly qualified personnel, making the system self-sufficient in terms of anomaly detection while maintaining high reliability
Solution Approach 2:
The patent replaces the mechanical system of human expert evaluation with an artificial intelligence-based anomaly detector. The AI model processes sensor data and detects anomalies automatically, substituting the need for highly qualified personnel with an automated computational system that maintains or improves detection accuracy
2Measurement precision
If more sensors are deployed to monitor increasing data volumes, then measurement coverage is improved, but data processing complexity increases
Solution Approach 1:
The patent merges multiple sensor data streams into a unified anomaly detection framework. The system integrates data from various sensors and process variables, combining them into a cohesive analysis that detects anomalies across the entire production process rather than analyzing each sensor separately, thereby reducing overall processing complexity
Solution Approach 2:
The anomaly detector is designed as a universal system that handles multiple types of sensor data and process variables through a single AI model. The trained artificial intelligence can process diverse data types uniformly, making the system multi-functional and reducing the need for separate processing pipelines for different data sources
3Reliability
If manual evaluation of anomalies is performed, then detection accuracy can be maintained, but response time and productivity decrease
Solution Approach 1:
The system implements continuous automated anomaly detection through the trained AI model that continuously processes sensor data in real-time. This continuous automated action maintains high detection accuracy while significantly improving response speed compared to intermittent manual evaluation, as the system operates without interruption or human intervention delays
Solution Approach 2:
The patent replaces manual anomaly evaluation with automated AI-based detection. The artificial intelligence system processes and evaluates anomalies automatically, substituting human manual review with a computational system that operates faster and maintains consistent accuracy, thereby improving both productivity and response time
Data Source
AI summary
A device for identifying anomalies in an industrial system for implementing a production process for a product in which the industrial system includes a plurality of sensors for measuring process variables of the production process includes an anomaly detector having at least one trained artificial intelligence, wherein the artificial intelligence is configured and trained to detect and/or predict anomalies in the production process based on a plurality of measured data from the sensors, where the anomaly detector outputs anomaly information upon detecting and/or predicting an anomaly, where in this case anomalies may be detected and predicted at the same time in multiple different performance indicators of the production process, and where the performance indicators each relate to the entire production process for the production of the product.