3D Sensor Content Level Measurement in Bulk Solids
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Solution Overview
Problem
Current methods for measuring the amount of bulk solids in containers, such as silos, lack accuracy and require complex calibration, especially when using non-contact level sensors, which are prone to errors due to the uneven surface of bulk solids, and are difficult for non-skilled personnel to install and calibrate accurately.
Innovation Solution
The use of high-resolution 3D cameras and 3D processing algorithms to acquire depth maps and compute accurate surface models of the content within the container, allowing for precise measurement of content levels and volumes, and automatic adjustment of critical parameters, enabling accurate measurement without the need for skilled installation or calibration.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of manufacture
If non-contact level sensors are used to measure content levels in containers with bulk solids, then installation and calibration become easier and cost decreases, but measurement accuracy deteriorates due to uneven surfaces causing errors up to 15-20%
Solution Approach 1:
The patent transitions from 1D point-level measurements to 3D surface mapping by using a sensor capable of measuring multiple points simultaneously across the content surface. This dimensional expansion allows capturing the uneven surface topology rather than relying on a single point measurement, thereby maintaining accuracy while using non-contact sensing methods.
Solution Approach 2:
The patent divides the content surface into multiple measurement points that are measured independently and then averaged. By segmenting the measurement area into discrete points and combining their readings, the system achieves accurate representation of the overall content level despite local surface variations, resolving the accuracy issue while maintaining ease of installation.
2Measurement precision
If multiple level sensors are installed to improve measurement accuracy by averaging readings from different points, then measurement accuracy improves, but device complexity and installation difficulty increase significantly
Solution Approach 1:
The patent merges multiple measurement functions into a single sensor unit. Instead of installing and coordinating multiple separate level sensors, one non-contact sensor is configured to measure multiple points simultaneously, combining what would have been separate devices into a unified system that achieves the same accuracy with reduced complexity.
Solution Approach 2:
The patent makes a single sensor perform multiple measurement functions by enabling it to capture data from multiple points across the content surface. This multi-functional capability replaces what would traditionally require multiple specialized sensors, simplifying the overall system while maintaining measurement accuracy through multi-point sampling.
3Measurement precision
If multi-point sensors are used to measure content levels at multiple locations, then measurement accuracy improves, but calibration difficulty increases due to the need for precise orientation and sampling representation
Solution Approach 1:
The patent enables the sensor system to perform self-calibration by automatically adjusting its measurement parameters and orientation based on the actual content surface it observes. The system adapts to the specific container and content conditions without requiring manual calibration procedures, eliminating the complex orientation and sampling adjustments that would otherwise be needed.
Solution Approach 2:
The patent allows the measurement system to dynamically adjust its parameters such as measurement points selection, sampling density, and orientation angles based on the actual conditions observed. This parameter adaptability enables accurate measurements without fixed calibration requirements, as the system automatically optimizes its operation for each specific situation.
4Measurement precision
If load cells or strain gauges are used to measure content mass accurately, then measurement accuracy improves, but installation complexity and cost increase due to support structure requirements
Solution Approach 1:
The patent replaces mechanical measurement systems (load cells and strain gauges that require support structures) with non-contact optical or electromagnetic sensing. This substitution eliminates the need for mechanical contact and complex support infrastructure while achieving comparable measurement accuracy through advanced signal processing and multi-point surface mapping.
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 provides higher accuracy comparable to load cells, simplifies the installation and calibration process, and allows for precise measurement of content levels and volumes, even in irregular containers, reducing measurement errors and enabling automation.
Implementation Method 1
emitting one pulse from a light source in a field of illumination toward a surface of said substance in said container. A backscatter signal of said pulse is detected by an optical detector
Implementation Method 2
A depth map of a given resolution is acquired by a 3D sensor
Data Source
AI summary
The method comprising attaching a 3D sensor (20) on a top part of the container (10) in a position (P) and with an orientation (O) such that its field of view (FOV) is oriented towards the content (11) stored in the container (10); acquiring, by the 3D sensor (20), a depth map (DM); and computing, by a computing unit, a 3D surface model by processing said acquired depth map (DM) and using said given position (P), orientation (O) and field of view (FOV), and a 3D level model by removing from the computed 3D surface model the points corresponding to the interior walls of the container (10), using a 3D function that searches the intersection or matching between the 3D surface model and the shape of the container (S), and filling in the missing points corresponding to the content (11) that falls out of the field of view (FOV) of the 3D sensor (20).