Sample Introduction Failure Detection Using ICP Signal Classification
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Solution Overview
Problem
Existing sample introduction systems for inductively coupled plasma analytical instruments face challenges in reliably detecting issues in real-time, leading to lost acquisition time and potential damage to components.
Innovation Solution
A system that uses instrument data, including signal and sensor data, to apply a trained classifier to detect whether the sample introduction system is operating in a normal or failure state, allowing for immediate activation of error procedures.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If conventional detection methods (visual evaluation or interlocks) are used, then the system can detect some malfunctions, but the detection is unreliable and occurs too late to prevent damage or lost acquisition time
Solution Approach 1:
The system performs preliminary detection by continuously monitoring instrument data and applying trained classifiers to identify failure states before they cause damage or significant time loss. The classifier detects patterns indicating impending failures, allowing preventive action to be taken before actual malfunction occurs.
Solution Approach 2:
The system implements continuous feedback by monitoring instrument data in real-time, comparing it against trained classifier models, and providing immediate detection of failure states. This closed-loop feedback mechanism enables reliable detection and rapid response to malfunctions.
2Reliability
If conventional interlocks are used, then some safety functions are provided, but user error or variation in measurement conditions leads to misidentification of malfunctions
Solution Approach 1:
The system performs self-diagnosis by automatically analyzing instrument data through trained classifiers to identify failure states. The system serves itself by detecting its own malfunctions without requiring user interpretation or manual evaluation, eliminating user error while maintaining simple operation.
Solution Approach 2:
The system replaces manual visual evaluation and simple interlock mechanisms with an automated classification system that uses machine learning algorithms to analyze instrument data. This substitution of mechanical/s manual detection with intelligent automated detection improves accuracy while maintaining ease of use.
3Productivity
If no detection system is used, then the system operates continuously without interruptions, but component damage may occur and acquisition time is lost
Solution Approach 1:
The system takes preliminary protective action by detecting failure states before they progress to component damage. The trained classifier identifies early signs of malfunction in instrument data, allowing the system to shut down or alert operators before actual damage occurs, thus protecting components while minimizing interruptions to productive operation.
Data Source
AI summary
A method of operating a sample introduction system of an inductively coupled plasma analytical instrument, the method comprising applying a trained classifier to instrument data, obtained from the analytical instrument, during operation of the analytical instrument, to detect whether the sample introduction system is operating in a normal state or in a failure state. The method further comprises activating an error procedure in the event that the sample introduction system is operating in a failure state. The instrument data comprises signal data obtained from an analytical measurement made by the analytical instrument. The trained classifier is trained using a training data set comprising instrument data corresponding to the normal state of the sample introduction system.