Distributed Data Engines for Real-Time Fault Detection
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current process control systems face limitations in real-time performance monitoring and analytics, as they rely on offline data analysis, are influenced by limited controller memory and bandwidth, and struggle with large data sets and streaming data, leading to inaccurate and delayed prescriptive actions.
Innovation Solution
The implementation of distributed data engines within process control systems for real-time data monitoring and analytics, enabling local data processing and streaming of analytics data across a dedicated network, which allows for timely and optimized decision-making.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If distributed data engines are implemented for real-time data monitoring and analytics, then the timeliness and accuracy of fault detection is improved, but the device complexity increases
Solution Approach 1:
The system divides the data processing functionality into multiple distributed data engines deployed across different devices in the process control system. Each data engine independently processes data from specific data sources, performing local analytics and fault detection. This segmentation allows real-time processing without requiring a single complex centralized system, thereby improving detection accuracy while managing system complexity through modular distribution.
2Loss of information
If all data from multiple data sources is streamed across the network for centralized analysis, then the analytics comprehensiveness is improved, but the bandwidth usage increases
Solution Approach 1:
The system extracts and processes only the essential and relevant data elements at each distributed data engine location. Each data engine performs local filtering and preprocessing, extracting key information from raw data streams before transmission. This extraction approach ensures that comprehensive analytics are maintained by preserving critical data while significantly reducing the volume of data transmitted across the network, thereby lowering bandwidth consumption.
3Loss of time
If real-time data processing is performed across all data sources, then the responsiveness of prescriptive actions is improved, but the processing cycles increase
Solution Approach 1:
Each distributed data engine performs preliminary data processing, filtering, and preliminary analytics locally before data is aggregated or further processed. This preliminary action at the source reduces the computational burden on centralized systems and enables faster initial detection and response. Critical anomalies are identified and flagged locally in real-time, allowing immediate prescriptive actions without waiting for complete centralized processing, thus improving responsiveness while maintaining processing efficiency.
Data Source
AI summary
A technique is provided for providing early fault detection using process control data generated by control devices in a process plant. The technique determines a leading indicator of a condition within the process plant, such as a fault, abnormality, or decrease in performance. The leading indicator may be determined using principal component analysis. A process signal indicating a process variable corresponding to the leading indicator is then obtained and analyzed. A rolling fast Fourier transform (FFT) may be performed on the process signal to generate time-series data with which to monitor the process plant. When the presence of the leading indicator is detected in the time-series data, an alert or other prediction of the condition may be generated. Thus, process faults may be identified using fluctuations and abnormalities as leading predictors.