Background Data Artefact Detection in Laser Diffraction Analysis
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Solution Overview
Problem
Existing laser diffraction-based particle size analysis systems face challenges in obtaining accurate and reliable data due to poor background measurements, which can lead to inaccurate particle size measurements and inefficient use of resources, requiring time-consuming manual data quality checking.
Innovation Solution
A method for automatically identifying and classifying data quality issues in background data using a processor, employing dynamic and static algorithms, including peak detection, adaptive diffraction, standard deviation, and machine learning techniques to detect artefacts such as contamination, misalignment, and thermal instability, and providing corrective actions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual data quality checking is performed, then data accuracy can be assessed, but time consumption and operational complexity increase
Solution Approach 1:
The system performs self-diagnosis by automatically analyzing its own background data for artefacts and quality issues. The processor autonomously identifies problems such as contamination, misalignment, and thermal instability without requiring external manual intervention, thereby maintaining measurement precision while eliminating time-consuming manual checking.
Solution Approach 2:
The patent replaces the mechanical/manual process of data quality assessment with an automated computational system. The processor uses algorithms to substitute human operators in detecting and evaluating background data quality, transforming a manual analytical task into an automated digital process that reduces time consumption while maintaining or improving detection accuracy.
2Reliability
If multiple measurements are taken to ensure data quality, then measurement reliability improves, but sample wastage and resource consumption increase
Solution Approach 1:
The system performs preliminary assessment of background data quality before actual sample measurements are conducted. By detecting artefacts and potential issues in advance, the system prevents unnecessary sample measurements, thereby ensuring measurement reliability while minimizing sample wastage and resource consumption.
Solution Approach 2:
The patent converts potentially harmful background artefacts (contamination, misalignment, thermal instability) into beneficial diagnostic information. By detecting these artefacts automatically, the system transforms what would be sources of measurement error into early warning signals that prevent wasted measurements, thus improving reliability while reducing sample consumption.
3Productivity
If automated artefact detection is implemented, then productivity increases, but device complexity increases
Solution Approach 1:
The processor is designed to perform multiple functions: it not only detects various types of artefacts (contamination, misalignment, thermal instability) but also classifies them and provides diagnostic information. This multi-functionality consolidates what would otherwise require separate systems into a single integrated component, increasing productivity while managing device complexity through functional integration.
4Speed
If background data is not properly checked, then measurement speed increases, but measurement precision deteriorates
Solution Approach 1:
The system autonomously validates its own background data by detecting artefacts such as contamination, misalignment, and thermal instability. This self-service quality check ensures measurement precision is maintained without requiring external intervention or slowing down the measurement process, as the validation occurs automatically in the background.
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
Enhances data accuracy and efficiency by automatically detecting and correcting background data artefacts, improving signal-to-noise ratio and reducing sample wastage, while saving time and resources.
Implementation Method 1
generating scattered light from the interaction of the light beam with particles within the sample cell
Implementation Method 2
detecting the scattered light with the plurality of detectors; detecting the scattered light intensity over a range of different scattering angles
Data Source
AI summary
A method of automatically identifying data quality issues in background data for laser diffraction-particle characterisation is provided. The method comprises receiving background data corresponding to light intensity measured by each of a plurality of detectors in a laser-diffraction-based particle size analysis system. A processor is used to automatically determine if the background data contains at least one artefact that is indicative of a source of error in the particle size analysis system. If the background data is found to contain at least one artefact an indication is provided that the background data contains at least one artefact. Furthermore, the at least one artefact is classified, and the classification is reported.