Inductive Proximity Sensor Signal Correction for Installation Independence
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Solution Overview
Problem
Inductive proximity sensors face challenges in achieving high switching distances, F1 behavior, and low installation dependency, with existing solutions either being costly or requiring extensive individual training for each sensor, especially when dealing with varying installation situations and materials.
Innovation Solution
The proposed solution involves an inductive proximity sensor that uses a combination of classic signal processing and machine learning methods to evaluate sensor signals, allowing for the determination of correction values for environmental interference, thereby achieving high switching distances and F1 behavior with reduced installation dependency, without the need for individual sensor training.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If hardware measures such as foils or additional compensation coils are used to minimize installation jump, then installation dependency is reduced, but manufacturing costs increase and switching distance is reduced
Solution Approach 1:
The patent replaces hardware-based compensation mechanisms (foils, additional coils) with a software-based machine learning evaluation method. The sensor system uses a trained evaluation algorithm that automatically compensates for installation effects by analyzing sensor signals and comparing them against training data that includes various installation scenarios, thereby achieving installation independence without adding physical components
Solution Approach 2:
The patent changes the evaluation parameters from simple amplitude detection to comprehensive signal analysis including phase, frequency, and temporal characteristics. By using machine learning models trained on diverse installation parameters, the system adapts its evaluation criteria to compensate for installation variations, effectively changing how the sensor interprets its signals rather than changing its physical structure
2Measurement precision
If machine learning methods are used for sensor signal evaluation, then F1 behavior and switching distance are improved, but training effort and complexity increase
Solution Approach 1:
The patent performs preliminary training actions during the manufacturing process where sensors are trained offline using controlled training setups with known reference objects. This preliminary training establishes the machine learning model before the sensor is deployed, so that during actual operation, the sensor can immediately utilize the pre-trained evaluation algorithm without requiring real-time training, thus reducing operational training time to nearly zero
Solution Approach 2:
The patent implements self-service mechanisms where the sensor system automatically performs evaluation and correction using its own sensor signals and the pre-trained machine learning model. The system serves itself by automatically compensating for installation effects and material variations without requiring external calibration or manual intervention during operation, thereby eliminating ongoing training time requirements
3Measurement precision
If additional signals from vertical or coaxial coils are used to determine correction values, then installation jump compensation is improved, but device complexity increases
Solution Approach 1:
The patent makes the existing sensor coil multi-functional by using it for both primary detection and installation effect detection. The same coil that detects target objects also captures signals influenced by installation conditions, and the machine learning evaluation method analyzes these signals to extract both target information and installation characteristics, eliminating the need for separate vertical or coaxial coils while maintaining correction accuracy
Solution Approach 2:
The patent extracts installation-related information from the existing sensor signals without requiring additional hardware. The machine learning evaluation method separates and extracts the installation effect components from the composite sensor signal, isolating the installation jump characteristics for compensation while using the remaining signal for target detection, thereby achieving precise correction without adding physical components
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 sensors to maintain high switching distances and F1 behavior across different installation conditions, recognizing target materials effectively, while reducing the need for extensive training and minimizing installation-dependent variations, thus optimizing sensor performance and cost-effectiveness.
Implementation Method 1
inductive proximity sensor for detecting an object
Data Source
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AI summary
A sensor (10) for detecting an object is specified, which has a detection unit (12, 22) for detecting a sensor signal and a control and evaluation unit (24) which is designed to determine an object property by evaluating the sensor signal, to determine a correction value for disturbances of the sensor environment from the sensor signal using a machine learning method and to take the correction value into account when determining the object property.