Air-Fuel Ratio Sensor Abnormality Detection Using Mapping Data
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Solution Overview
Problem
Existing air-fuel ratio sensor abnormality detection systems cannot accurately determine sensor responsiveness issues before a predetermined threshold is reached, limiting their ability to utilize sensor information effectively.
Innovation Solution
An abnormality detection device that uses mapping data to calculate an abnormality determination variable based on time series data of excess fuel amounts and air-fuel ratio detection variables, incorporating variables such as temporally-varying and difference variables, to assess sensor behavior and responsiveness.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If the control device merely compares the responsiveness abnormality determination value with the detection value when the integrated value reaches the predetermined value, then the detection method is simple, but the detection timing is delayed and early abnormality information is lost
Solution Approach 1:
The system performs preliminary abnormality assessment by continuously analyzing the relationship between excess fuel amount and air-fuel ratio detection values before the integrated value reaches the predetermined threshold. This early analysis enables detection of responsiveness abnormalities before traditional methods would trigger, losing no early abnormality information while maintaining manageable complexity through pre-computed mapping data
Solution Approach 2:
The system applies partial action by selectively analyzing data points only when specific conditions are met (e.g., when excess fuel amount exceeds a threshold), rather than continuously processing all data. This approach improves detection accuracy for significant abnormalities while avoiding unnecessary computational complexity for normal operating conditions
2Loss of time
If the system continuously analyzes sensor behavior before the integrated value reaches the threshold, then early abnormality detection is enabled, but computational load increases
Solution Approach 1:
Mapping data specifying the relationship between excess fuel amount and air-fuel ratio detection values is pre-computed and stored before runtime. During operation, the system only needs to retrieve and compare current sensor readings against this pre-established mapping, enabling rapid early abnormality detection without the computational burden of real-time complex calculations
Solution Approach 2:
The system uses a simplified copy or representation of the complex relationship between fuel amount and sensor response in the form of lookup tables or pre-computed mapping data. This allows fast comparison and detection operations without performing full computational analysis during runtime, reducing energy consumption while maintaining detection speed
Data Source
AI summary
An abnormality detection device for an air-fuel ratio sensor is provided. An air-fuel ratio sensor is provided in an exhaust passage. A storage device stores mapping data specifying a mapping. The mapping outputs an abnormality determination variable using first time series data and second time series data as an input. The first time series data is time series data of an excess amount variable in a first predetermined period. The excess amount variable is a variable corresponding to an excess amount of fuel actually discharged to the exhaust passage in relation to an amount of fuel reacting without excess or deficiency with oxygen contained in a fluid discharged to the exhaust passage. The second time series data is time series data of an air-fuel ratio detection variable in a second predetermined period.