Pipette Pressure Monitoring via Statistical Profile
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Solution Overview
Problem
Existing methods for monitoring fluid transfer processes in pipette systems rely on high-resolution threshold values and tolerance intervals, leading to increased storage and processing complexity, reduced precision, and a higher likelihood of false error detections, as they fail to accurately reflect the system's behavior and are influenced by random fluctuations.
Innovation Solution
The method employs interpolation points derived from a probability function, specifically a Gaussian distribution, to define an allowable pressure profile that adapts to the properties of the fluid transfer process, reducing storage and processing complexity while maintaining precision by mapping stochastic data onto a parameterized equation, allowing for efficient error detection and identification of errors specific to process phases.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If high-resolution threshold values and tolerance intervals are used to monitor fluid transfer processes, then measurement precision is improved, but device complexity and storage requirements increase
Solution Approach 1:
The patent transforms the monitoring approach from using fixed high-resolution threshold values to using adaptive tolerance intervals defined by statistical parameters (mean and standard deviation) calculated from historical process data. This allows the system to maintain high measurement precision while reducing processing complexity by working with summarized statistical parameters rather than extensive threshold tables
Solution Approach 2:
The patent creates a virtual model of normal process behavior through probability distribution functions based on historical data. Instead of storing and processing actual historical pressure curves, the system uses parametric models (mean, standard deviation) that replicate the essential characteristics of normal operation, enabling efficient real-time monitoring without high computational requirements
2Measurement precision
If high-resolution threshold values and tolerance intervals are used to monitor fluid transfer processes, then measurement precision is improved, but storage requirements increase
Solution Approach 1:
The patent reduces data storage requirements by transforming detailed pressure measurements into summarized statistical parameters. Instead of storing extensive threshold value tables or complete historical pressure profiles, the system only needs to store mean and standard deviation values for each process phase, dramatically reducing storage volume while maintaining the ability to detect errors with high precision
Solution Approach 2:
The patent extracts only the essential characteristics of normal process behavior (central tendency and variability) from historical data, discarding redundant detailed information. By storing only the mean and standard deviation parameters that define the tolerance intervals, the system achieves high measurement precision with minimal storage requirements
3Measurement precision
If equidistant sampling pattern with high sampling frequency is used to define tolerance interval, then measurement precision is improved, but loss of information increases due to not distinguishing stable runs from runs with natural fluctuations
Solution Approach 1:
The patent implements dynamic tolerance intervals that adapt to the actual process behavior through statistical analysis. Instead of using fixed equidistant sampling patterns, the system calculates mean and standard deviation from historical data to create variable tolerance boundaries that naturally accommodate stable runs and distinguish them from abnormal fluctuations, preserving essential system behavior information
4Measurement precision
If complex mathematical algorithms are used to analyze measured pressure profile, then measurement precision is improved, but device complexity increases
Solution Approach 1:
The patent extracts only the essential features needed for error detection by comparing actual pressure values against statistically derived tolerance intervals based on mean and standard deviation. This avoids complex mathematical algorithms while maintaining measurement precision by focusing on the most critical aspects of pressure profile analysis
Solution Approach 2:
The patent transforms complex pressure curve analysis into simple parameter comparison. Instead of applying complex algorithms to analyze the entire pressure profile, the system converts the problem into comparing whether pressure values fall within tolerance ranges defined by mean ± n×standard deviation, dramatically reducing computational complexity while preserving detection precision
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
This approach enables precise error detection with reduced storage and processing complexity, accurately reflecting system behavior without losing significant characteristics, allowing for efficient monitoring and correction of errors in fluid transfer processes.
Implementation Method 1
monitoring the pressure of a gas volume in the pipette filled with liquid
Data Source
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AI summary
The present invention relates to a method for monitoring a fluid transfer process, including the steps: providing an allowable pressure profile; detecting a pressure occurring in the course of the fluid transfer process; comparing detected pressure with the allowable pressure profile and signaling an error, if the detected pressure is not within the allowable pressure profile. The allowable pressure profile is defined by interpolation points, the interpolation points being based on a probability function representing a family of pressure courses of a plurality of fluid transfer processes. The allowable pressure profile can be divided into at least two distinct process sections, each section corresponding to a distinct process phase of the fluid transfer process. The probability function is calculated from a family of test pressure curves and reflects the statistical behavior of the pipette system. The present invention further relates to a data carrier for storing interpolation point information as well as to a kit-of-parts comprising a device implementing the inventive method, together with a data carrier for storing interpolation point information.