Abnormal Situation Prevention Configuration System
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current diagnostic tools in process plants are primarily reactive, detecting problems after they occur, leading to suboptimal performance and potential significant costs or damage, as they are not designed to prevent abnormal situations proactively.
Innovation Solution
A system that automatically configures and monitors signal processing data collection blocks in process plants, analyzing data to predict and prevent abnormal situations by enabling and disabling data collection blocks as needed, and using a rules engine to analyze data for predictive maintenance.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If diagnostic tools are used to detect problems after they occur, then problem detection capability is improved, but system downtime and costs increase due to reactive rather than preventive operation
Solution Approach 1:
The system performs preliminary actions by continuously collecting and analyzing operational data from multiple sources (vibration sensors, temperature sensors, process control systems) to detect early signs of equipment degradation before failures occur. The diagnostic engine analyzes trends and patterns in the collected data to predict potential failures, enabling maintenance to be scheduled during planned downtime rather than experiencing unplanned outages.
2Measurement precision
If comprehensive data collection blocks are enabled to monitor all parameters, then diagnostic accuracy is improved, but system complexity and computational load increase
Solution Approach 1:
The system segments data collection by enabling specific data collection blocks based on equipment type, location, and risk priority. Rather than uniformly monitoring all parameters across all equipment, the configuration system divides the plant into zones and equipment categories, assigning appropriate monitoring parameters to each segment. This reduces overall system complexity while maintaining diagnostic accuracy for critical assets.
Solution Approach 2:
The system applies partial monitoring action by selectively enabling data collection blocks only for parameters and equipment that require monitoring based on risk assessment and operational criticality. Not all equipment or parameters are monitored at the same level, allowing the system to achieve sufficient diagnostic accuracy for critical functions while reducing complexity for less critical areas.
3Adaptability or versatility
If manual configuration of data collection blocks is performed, then system adaptability is improved, but operation time and labor costs increase
Solution Approach 1:
The configuration system performs self-service by automatically generating data collection block configurations based on equipment metadata, risk assessments, and operational priorities. The system autonomously determines which parameters to monitor, where to place sensors, and how to aggregate data without requiring manual engineering for each equipment item. This maintains adaptability to different equipment types while dramatically reducing configuration time and labor requirements.
Data Source
AI summary
A system for gathering data associated with a process plant, in which parameters are generated by a plurality of signal processing data collection blocks, automatically determines parameters to be monitored. The signal processing data collection blocks may generate data such as statistical data, frequency analysis data, auto regression data, wavelets data, etc. Then, the system monitors the determined parameters.