Chromatography System Automated Fault Detection
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Chromatography systems, such as HPLC systems, often experience unplanned failures due to complex arrangements of movable components and require offline diagnostics, which disrupt operations and incur unnecessary costs and downtime, as conventional diagnostic tests rely on expert users and interrupt system use.
Innovation Solution
The implementation of automated systems and methods that use sensor data and computational models, including machine-learning models, to identify error conditions, classify operational states, and trigger self-recovery actions without user intervention, allowing for continuous operation and reducing maintenance intervals.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If automated diagnostic systems with sensor data and machine-learning models are implemented, then early error detection and continuous operation are enabled, but device complexity increases
Solution Approach 1:
The system performs self-diagnosis by automatically monitoring its own operational parameters through sensors and evaluating them using machine-learning models. The chromatography system detects errors, classifies operational states, and triggers self-recovery actions without external intervention, enabling the system to serve its own diagnostic needs and maintain continuous operation.
Solution Approach 2:
Conventional mechanical diagnostic tests performed by expert users are replaced with an automated electronic system comprising sensors, computational models, and machine-learning algorithms. This substitution transforms manual, interruptive diagnostics into continuous, automated monitoring that does not require system shutdown or expert intervention.
2Reliability
If conventional offline diagnostic tests are performed, then error conditions are identified, but system downtime and operational disruption occur
Solution Approach 1:
The diagnostic system operates continuously during normal system function without requiring shutdown or interruption. Sensors continuously monitor operational parameters, and the machine-learning model continuously evaluates system state, ensuring that diagnostic activities proceed simultaneously with productive operations rather than interrupting them.
Solution Approach 2:
The system detects and addresses error conditions before they lead to system failure or require manual intervention. By continuously monitoring parameters and identifying deviations from normal operation early, the system can trigger self-recovery actions proactively, preventing downtime rather than responding to it after occurrence.
3Productivity
If automated self-recovery actions are triggered, then maintenance intervals are extended and costs are reduced, but automation extent increases
Solution Approach 1:
The system automatically performs recovery actions such as flushing, purging, or parameter adjustments when errors are detected, without requiring manual maintenance intervention. This self-service capability handles routine corrective actions autonomously, reserving human expertise for more complex issues and thereby extending maintenance intervals.
Solution Approach 2:
The system continuously monitors operational parameters and uses machine-learning models to evaluate system state, creating a closed-loop feedback system. When deviations are detected, the system automatically adjusts parameters or triggers recovery actions, then continues monitoring to verify correction, forming an automated feedback-driven maintenance cycle that reduces manual intervention.
Data Source
AI summary
Disclosed herein are chromatography support systems, as well as related methods, computing devices, and computer-readable media. For example, in some embodiments, the systems and methods disclosed herein may enable the automatic identification of error or fault conditions in a chromatography system.