Analog Sensor Flank Analysis for Unstable Error Detection
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing methods for diagnosing analog sensors are inadequate for detecting unstable errors, such as intermittent short circuits or open contacts, and cannot be universally applied to various types of sensors, particularly affecting the reliability and accuracy of systems like engine control in automobiles.
Innovation Solution
An improved error analysis method for analog sensors involves analyzing the sensor signal's flanks using a gradient signal to categorize them into specified categories, determining error types based on the sequence and characteristics of these flanks, without requiring additional analysis signals, and can be adapted for different sensor types.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If existing diagnostic methods are used for analog sensors, then stable errors can be detected, but unstable errors such as intermittent short circuits or open contacts cannot be reliably detected
Solution Approach 1:
The sensor signal is segmented into individual flanks (rising and falling edges), and each flank is independently analyzed for categorization. This segmentation allows the detection system to examine specific signal transitions separately, enabling reliable identification of unstable errors by analyzing the sequence and characteristics of individual flanks rather than treating the signal as a continuous waveform.
Solution Approach 2:
The diagnostic method dynamically adapts to different sensor types and error conditions by adjusting the analysis parameters for flank detection. The system dynamically identifies and evaluates signal flanks based on their characteristics, allowing it to reliably detect various unstable error types including intermittent short circuits, open contacts, and ground shorts across different sensor configurations.
2Reliability
If additional analysis signals are introduced to improve error detection, then detection capability increases, but device complexity increases
Solution Approach 1:
The sensor signal itself is used as the analysis source, eliminating the need for separate diagnostic test signals or additional excitation sources. The method extracts diagnostic information directly from the normal operational sensor signal by analyzing its flanks, thereby achieving unstable error detection without introducing additional complexity through extra hardware components or separate diagnostic signal paths.
Solution Approach 2:
The flank-based diagnostic method is designed to be universally applicable to various types of analog sensors including pressure sensors, temperature sensors, and lambda sensors. The same basic approach of detecting and categorizing signal flanks can be used across different sensor types, reducing the need for type-specific diagnostic circuits and simplifying the overall system architecture while maintaining broad error detection capability.
Data Source
AI summary
A method for analyzing a sensor with respect to unstable errors, wherein a gradient signal is generated based on a sensor signal. Based on the gradient signal, an evaluation unit is used to assign a slope category to a first slope of the sensor signal and a second slope of the sensor signal, the second slope following the first slope. The evaluation unit ascertains a class of error based on the slope categories assigned to the first slope and the second slope.