Analytical Instrument Diagnostics Using Machine Learning Models
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Solution Overview
Problem
Analytical instruments like mass spectrometry (MS) and liquid chromatography-mass spectrometry (LC-MS) systems face operational instability due to hardware and software issues, making it difficult to detect and diagnose component failures and human errors, relying on time-consuming trial-and-error methods that lack adequate knowledge of system components.
Innovation Solution
Implementing a diagnostic services process using machine learning and neural networks to analyze data channels, generate computational models for standard and non-standard operating conditions, and predict system failures, thereby improving diagnostic accuracy and efficiency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional trial-and-error methods are used for diagnosis, then operators can identify issues through experience, but the process is extensive and time-consuming
Solution Approach 1:
The system performs preliminary actions by continuously collecting and storing operational data from multiple sources (mass spectrometer, chromatography system, system controller) during normal operation. This pre-collected data is then used for rapid diagnostic analysis when issues arise, eliminating the need for time-consuming trial-and-error procedures while maintaining high diagnostic accuracy through comprehensive baseline information.
Solution Approach 2:
An intermediary diagnostic system is introduced between the operational instruments and the operators. This intermediary automatically analyzes operational data, compares it against baseline patterns, and generates diagnostic recommendations, thereby reducing both the time required for diagnosis and the reliance on operator experience while improving diagnostic consistency.
2Ease of operation
If operators rely on experience to diagnose issues, then they can handle common problems, but they lack adequate knowledge of particular system components for efficient diagnosis
Solution Approach 1:
The diagnostic system provides self-service by automatically analyzing operational data and generating component-level diagnostic information without requiring operators to have specialized knowledge. The system serves itself by collecting, processing, and interpreting data from multiple instrument components, then presenting actionable diagnostic results that compensate for the operators' limited component knowledge while improving diagnostic efficiency.
Solution Approach 2:
The system replaces the mechanical reliance on operator knowledge and experience with an automated electronic diagnostic system. Instead of operators needing to understand complex system components, the electronic system automatically processes operational data, identifies anomalies, and provides diagnostic guidance, thereby improving ease of operation without losing critical system component information.
3Measurement precision
If analytical instruments operate continuously for detailed characterization, then precise analytical data is obtained, but operational instability increases due to hardware and software issues
Solution Approach 1:
The system implements continuous feedback by monitoring operational data from multiple sources during continuous operation. The diagnostic system constantly compares current operational parameters against baseline patterns and stored historical data, providing real-time feedback on system health. This enables early detection of hardware and software issues while maintaining continuous operation for precise analytical data collection, thereby improving operational stability without sacrificing measurement precision.
Data Source
AI summary
Techniques and apparatus for diagnostic processes for analytical instruments are described. In one embodiment, for example, an apparatus may include at least one memory, and logic coupled to the at least one memory. The logic may be configured to receive diagnostic information associated with at least one analytical instrument, and process the diagnostic information using a computational model to generate at least one diagnostic model for at least one diagnostic. Other embodiments are described.