AI Sensor Assembly with Diverse Inputs for Error-Safe Detection
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing sensor arrangements in industrial applications, particularly in security technology, face challenges in ensuring high reliability and error safety due to complex calculation methods required for generating safety functions from sensor measurements.
Innovation Solution
A sensor arrangement that utilizes at least one sensor to record objects in a surveillance area, with sensor signals being evaluated using AI processing algorithms. This setup generates diverse, independent input data for multiple AI processing units, which produce overall results through logical linking and comparison, thereby enhancing reliability and error safety.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If complex calculation methods are used to generate safety functions from sensor measurements, then reliability and fault tolerance are improved, but device complexity and computational requirements worsen
Solution Approach 1:
The patent divides the sensor array into multiple independent sensor units, each capable of generating measurement values. This segmentation allows the system to process data from multiple independent sources, improving reliability through diversity while keeping individual sensor complexity manageable. The segmentation principle is applied by having multiple sensors rather than one complex sensor, and multiple AI algorithms rather than one complex calculation method.
Solution Approach 2:
The patent introduces AI processing algorithms as intermediaries between the raw sensor measurements and the safety function generation. These AI algorithms act as mediators that can handle complex pattern recognition and decision-making, reducing the complexity burden on the overall system architecture while maintaining high reliability through intelligent processing.
2Reliability
If multiple diverse input data items are processed through AI algorithms, then error safety and reliability are improved, but processing time and computational resources worsen
Solution Approach 1:
The patent performs preliminary processing of sensor data by generating multiple diverse input data items from raw measurements before the main safety function evaluation. This preliminary action includes creating different representations or features from the same sensor data, which are then processed by AI algorithms. This approach prepares the data in advance, allowing faster and more reliable decision-making when safety evaluation is needed.
3Adaptability or versatility
If AI processing algorithms are used to evaluate sensor signals, then adaptability to changing conditions is improved, but calculation complexity and verification requirements worsen
Solution Approach 1:
The patent employs AI processing algorithms that can dynamically adapt to changing conditions in the monitored environment. These algorithms can learn from new data and adjust their behavior accordingly, providing high adaptability to varying operational conditions, lighting changes, and different object types. The dynamic nature of AI algorithms allows the system to maintain performance across diverse scenarios without requiring manual reconfiguration.
Data Source
Figure 1~2
Figure 3
Figure 4
AI summary
The invention relates to a sensor arrangement (1) with at least one sensor (2) for detecting objects in a monitoring area. Depending on sensor signals from the at least one sensor (2), at least two diverse, independent input data sets are received or generated and each is fed to a processing unit with an AI (Artificial Intelligence) processing algorithm (212, 222). Processing results generated in the processing units are combined to form an overall result that constitutes an output signal of the sensor arrangement (1). The invention further relates to a corresponding method.