Anomaly Detection Module for APC Sensor Data Validation
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Advanced process controllers (APCs) in industrial process systems are vulnerable to computing incorrect set-points due to low-quality sensor data, leading to potential system failures, especially in harsh environments or when sensors are faulty or clogged.
Innovation Solution
A computer system that monitors the technical status of industrial process systems using an interface module for receiving sensor data, an anomaly detection module applying Machine Learning Models to identify abnormal conditions, and a post-processor to determine root causes, allowing for the deactivation of APCs to prevent incorrect set-point calculations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If APCs use sensor data for computing set-points, then control functionality is provided, but system reliability deteriorates when sensor data quality is low
Solution Approach 1:
The system performs preliminary anomaly detection on sensor data before the APC uses it for control calculations. The anomaly detection module analyzes sensor data in advance to identify potential issues, and only data passing this preliminary check is forwarded to the APC, preventing unreliable data from compromising control functionality.
Solution Approach 2:
The patent introduces an intermediary anomaly detection module between the sensor data source and the APC. This intermediary layer filters and validates sensor data, acting as a mediator that protects the APC from processing low-quality data while maintaining the control functionality of the system.
2Reliability
If the system monitors and detects anomalies in sensor data, then reliability is improved, but device complexity increases
Solution Approach 1:
The anomaly detection module operates autonomously, automatically analyzing sensor data and identifying anomalies without requiring external intervention. The system self-monitors and self-validates the quality of its input data, improving reliability through self-service mechanisms rather than adding complex external monitoring systems.
Solution Approach 2:
The system segments the control architecture into distinct functional modules: sensor data acquisition, anomaly detection, and APC control. This segmentation allows the anomaly detection function to be added as a separate, manageable component rather than integrating complexity into the existing APC, making the overall system more maintainable and understandable.
3Object-affected harmful factors
If the system deactivates APCs upon anomaly detection, then harmful factors are prevented, but productivity decreases
Solution Approach 1:
The system uses inexpensive, easily replaceable sensors that can be quickly swapped if anomalies are detected. Rather than deactivating the entire APC system, the solution allows for rapid replacement of faulty sensor components, preventing harmful effects while minimizing impact on overall system productivity through quick recovery.
Solution Approach 2:
The system dynamically adjusts its response to anomalies based on their severity and type. Rather than always deactivating the APC, the system can apply different strategies including partial deactivation, continued operation with monitoring, or rapid sensor replacement, optimizing the balance between preventing harmful effects and maintaining productivity.
Data Source
Figure 1
Figure 2
Figure 3A~3C
AI summary
Computer system (100), computer-implemented method and computer program product are provided for monitoring the technical status of an industrial process system (300) being under control of an advanced process controller (APC). The industrial process system (300) has an operation (330) for processing flow materials. The advanced process controller (APC) is responsive to one or more sensor signals (320). The computer system (100) includes an interface module (110) configured to receive technical status data (321) describing the current technical state of the industrial process system (300) with regards to a respective processing component or the processed material wherein the technical status data (321) corresponds to or is derived from the one or more sensor signals (320). Further, the interface outputs an anomaly alert (AA) in case of an anomaly detection for the industrial process system to enable deactivating of the advanced process controller (APC). The computer system further includes an anomaly detection module (120) to apply one or more Machine Learning Models (MLMn) to the received technical status data (321) to analyze the technical status data for detecting one or more indicators of an abnormal technical status prevailing in the industrial process system. The one or more Machine Learning Models (MLMn) are trained on historic raw or pre-processed sensor data. The anomaly detection module generates the anomaly alert (AA) based on the one or more indicators.