Smart Asset Controller for Predictive Shutdown Avoidance
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Threshold-based safety systems in condition monitoring for industrial assets often lead to sudden shutdowns, causing undesirable process disruptions and production losses due to their binary nature, which is not adaptive to the deteriorating conditions of assets over time.
Innovation Solution
A smart controller with a simulation engine and asset condition monitor that continuously analyzes data to predict potential shutdowns and adjusts setpoints in real-time, providing early warnings and ensuring safe operating conditions, thereby minimizing the likelihood of asset shutdowns.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If threshold-based safety systems are used for condition monitoring, then asset safety is improved, but process continuity deteriorates due to sudden shutdowns
Solution Approach 1:
The system transitions from static threshold-based monitoring to dynamic predictive monitoring. The simulation engine continuously models asset behavior under varying operating conditions, adjusting predictions in real-time based on current asset state and historical data, thereby avoiding rigid shutdown thresholds while maintaining safety
Solution Approach 2:
The system performs preliminary risk assessment by simulating future asset conditions before actual failures occur. By predicting potential failures and recommending preventive maintenance actions in advance, the system prevents catastrophic failures without causing unnecessary shutdowns, thus maintaining both safety and productivity
2Strength
If threshold-based shutdown systems are implemented, then asset damage is prevented, but production loss increases due to lengthy restart delays
Solution Approach 1:
The system performs preliminary risk assessment and predicts asset failures before they occur. By identifying degradation trends and simulating future conditions, the system enables scheduled maintenance during convenient periods rather than forced shutdowns, thus preventing asset damage while minimizing production disruption and restart delays
Solution Approach 2:
The system continuously monitors asset condition and provides feedback through predictive analytics. By comparing actual asset performance against simulated models, the system generates early warnings and maintenance recommendations that allow operators to plan maintenance activities optimally, avoiding unexpected shutdowns and reducing production loss
3Adaptability or versatility
If continuous monitoring and simulation are implemented, then asset management quality is improved, but system complexity increases
Solution Approach 1:
The simulation engine serves multiple functions: it predicts asset failures, optimizes maintenance schedules, evaluates repair strategies, and provides early warnings. This multi-functionality consolidates what would otherwise require multiple separate systems into a single platform, improving asset management quality while limiting the increase in overall system complexity
Solution Approach 2:
The system automatically collects sensor data, updates simulation models, generates predictions, and provides maintenance recommendations without requiring constant manual intervention. This self-service capability reduces the operational complexity of managing the monitoring system while maintaining high adaptability to different asset conditions
Data Source
AI summary
A smart controller continuously monitors data for determining in real time if there is a likelihood that an asset will be shut down and, if so, recommends a new setpoint at which the asset will continue to operate but will not lead to tripping of the system. The smart controller executes a simulation engine to test the new setpoint before implementation to optimize performance while avoiding a shutdown. In this manner, the smart controller provides early warning of possible shutdowns and ensures that key assets are less likely to be shut down.