Multivariate Process Monitoring Interface for Remote Fault Detection
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Solution Overview
Problem
Automated process control systems in process plants struggle to respond appropriately to complex interactions and unexpected fault conditions, requiring human supervision and physical presence for effective management of thousands of data sources.
Innovation Solution
A system that collects process monitoring data to construct a multivariate model, providing a user interface for remote monitoring and advisory control, using graphical interfaces to display performance, variable contributions, and deviations from expected trajectories, allowing for real-time updates and remote operation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If automated process control systems are used to manage groups of sensors, then monitoring efficiency is improved, but the ability to respond to complex interactions and unexpected fault conditions deteriorates
Solution Approach 1:
The patent introduces a supervisory system that acts as an intermediary between automated control systems and human operators. This supervisory system aggregates data from multiple sensors, performs advanced analysis using multivariate statistical models, and presents synthesized information to operators, enabling them to effectively manage complex interactions without being overwhelmed by raw data volume.
Solution Approach 2:
The patent replaces manual monitoring of thousands of sensor data sources with automated multivariate statistical analysis and machine learning models. These computational systems process and interpret complex sensor interactions, substituting human cognitive processing with algorithmic analysis while maintaining the need for human supervisory oversight.
2Reliability
If human operators manage and respond to data from thousands of sources, then fault detection capability is improved, but operational feasibility deteriorates
Solution Approach 1:
The patent extracts and isolates critical fault detection functions from the overall monitoring system. By using multivariate statistical models to identify patterns and anomalies across thousands of data sources, the system separates complex analytical tasks from human operators, presenting only the most significant findings for human decision-making.
Solution Approach 2:
The patent transforms the monitoring approach by adding a computational analysis dimension between raw sensor data and human operators. Multivariate statistical models project high-dimensional sensor data into lower-dimensional spaces that highlight critical fault patterns, making the information manageable for human operators while preserving comprehensive fault detection capability.
3Loss of time
If human operators maintain physical presence at the process plant, then response time to fault conditions is improved, but operational flexibility and safety deteriorate
Solution Approach 1:
The patent creates virtual copies of the physical process plant through digital representations of sensor data and multivariate statistical models. Operators can monitor and analyze plant conditions remotely through these digital twins, maintaining rapid response capabilities while eliminating the need for continuous physical presence, thereby improving operator safety and flexibility.
Data Source
AI summary
Systems and methods are provided that allow a user to monitor, diagnose, and configure a process. A user interface may be presented that displays data received from process monitors, sensors, and multivariate models. The user interface may be interactive, allowing a user to select which composite and multivariate models are displayed. The user interface and multivariate model may be constructed and updated in real time. The user interface may present various data and interfaces, such as a representation of a composite variable in a multivariate model, a representation of the contribution of process variables to the composite variable, and a representation of a subset of the process variables. The user may select a point of the composite variable for analysis, and the interface may indicate the contribution and values of process variables at the selected point. The interface may be transmitted to a remote user.