Sensor Assembly Verification Using Back-Calculated Safety Signals
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Solution Overview
Problem
Existing sensor arrangements in industrial applications, particularly safety sensors, face challenges in achieving high reliability and fail-safety due to complex calculation methods and stringent verification requirements for generating monitoring and safety functions.
Innovation Solution
A sensor arrangement with a computing module that uses an algorithm to derive quantities from measured values, a system modeling module to back-calculate values, and a verification unit to compare these back-calculated values with measured values, allowing for flexible and fast evaluation of sensor data, even using virtual data from an external source like a digital twin, ensuring reliability and fail-safety.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If complex calculation methods are used to ensure high reliability and fail-safety, then the reliability of safety functions improves, but the device complexity and computational burden increase
Solution Approach 1:
The verification process is segmented into two independent parts: (1) forward calculation from measured values to derived quantities using the algorithm, and (2) backward calculation from derived quantities to back-calculated measured values using system modeling. Each segment can be independently verified, simplifying the overall verification complexity while maintaining high reliability.
Solution Approach 2:
The system implements feedback verification by comparing back-calculated measured values with original measured values. This closed-loop feedback mechanism ensures reliability by continuously validating that the algorithm and system model produce consistent results, without requiring complex proof of the algorithm's correctness.
2Reliability
If rigorous verification of algorithms is performed to ensure fail-safety, then the safety function reliability improves, but the time and resources required for verification increase
Solution Approach 1:
System modeling is performed in advance to create a pre-established model that can rapidly back-calculate measured values from derived quantities. This preliminary preparation eliminates the need for time-consuming real-time verification of complex calculations, as the model structure is already validated before operation.
Solution Approach 2:
Instead of directly verifying the complex algorithm's correctness, the system creates a copy of the measurement process through system modeling - back-calculating measured values from derived quantities and comparing them with original measurements. This indirect verification approach is faster and less resource-intensive than direct algorithm verification.
3Productivity
If simple estimating algorithms are used for fast evaluation, then the processing speed improves, but the reliability and accuracy of derived quantities may deteriorate
Solution Approach 1:
Even simple estimating algorithms benefit from the feedback verification mechanism. The back-calculated measured values are compared with original measurements, providing automatic validation that ensures reliability without requiring complex algorithms. This feedback loop compensates for the simplicity of the estimating algorithm.
Solution Approach 2:
The system allows flexible adjustment of algorithm parameters and complexity based on application requirements. Simple estimating algorithms can be used when speed is critical, while more complex algorithms can be employed when higher accuracy is needed, with verification ensuring reliability in both cases.
4Adaptability or versatility
If external data sources are used to generate derived quantities, then the versatility and flexibility improve, but the reliability may deteriorate due to potential data insecurity
Solution Approach 1:
External data sources are integrated into the feedback verification loop. Derived quantities from external sources are back-calculated to produce back-calculated measured values, which are then compared with actual sensor measurements. This verification ensures that even insecure external data sources produce reliable results.
Solution Approach 2:
System modeling acts as an intermediary between external data sources and the safety function generation. The model transforms external derived quantities into back-calculated measured values that can be verified against sensor data, mediating the trust relationship between external sources and safety-critical outputs.
Data Source
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AI summary
The invention relates to a sensor arrangement (10) comprising a sensor (1) generating measured values (101), a computing module in which quantities (103) derived from the measured values (101) are generated by means of an algorithm (102), a safety function (201) with which a safety result is generated depending on the derived quantities (103) generated in the computing module or an external data source (301), a system modeling module in which values (105) calculated from the derived quantities (103) are generated in a sensor space of the sensor (1), and a verification unit in which the algorithm (102) or data from the external data source (301) is verified by comparing the calculated values (105) with the measured values (101). The invention further relates to a corresponding method.