Proportional Oxygen Probe Diagnosis via High-Pass Filtering
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Solution Overview
Problem
Existing methods for diagnosing proportional upstream oxygen sensors in internal combustion engines are prone to false positives due to non-robustness during transient engine operation phases, particularly because richness regulation corrections are influenced by low-frequency components unrelated to the alternation of rich and lean mixture periods.
Innovation Solution
A diagnostic method that involves regulating the exhaust gas richness with a variable setpoint, filtering measurements using high-pass filtering to isolate frequency components matching the alternation periods, and calculating a diagnostic criterion based on corrected richness measurements to accurately differentiate between functional and faulty sensors.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If the diagnostic criterion is calculated using raw richness measurements during transient engine operation, then the diagnosis can be performed continuously, but the risk of false positive diagnoses increases due to low-frequency components unrelated to sensor response
Solution Approach 1:
A high-pass filter is introduced as an intermediary between the raw richness measurements and the diagnostic criterion calculation. This filter removes low-frequency components that are unrelated to the sensor's response to rich-lean alternations, thereby eliminating the source of false positives while allowing continuous diagnosis to proceed
Solution Approach 2:
The harmful low-frequency components are extracted and removed from the richness measurements through high-pass filtering. This separation allows the diagnostic criterion to be calculated using only the relevant high-frequency components that actually reflect sensor performance, thus improving reliability without sacrificing diagnostic continuity
2Productivity
If the richness setpoint is corrected based on catalyst conditions and downstream probe signals, then the engine operation is optimized, but the diagnostic criterion becomes falsified by low-frequency components
Solution Approach 1:
The high-pass filter acts as an intermediary that separates the optimized richness setpoint (which includes low-frequency corrections for catalyst conditions) from the diagnostic measurement. By filtering out these low-frequency components, the diagnostic criterion reflects only the sensor's response to rich-lean alternations, not the optimization corrections
Solution Approach 2:
The richness signal is segmented into two distinct components: the optimized setpoint (containing low-frequency corrections for catalyst and downstream probe) and the diagnostic signal (containing only high-frequency variations related to sensor response). This segmentation allows both engine optimization and accurate diagnosis to coexist without interference
Data Source
AI summary
A diagnostic method for an oxygen sensor in which the exhaust gas mixture is regulated with a variable setpoint. The method comprises the following steps: the operation of the internal combustion engine is controlled to obtain at least three alternations of rich and lean exhaust gas emission periods, and for each period, the exhaust gas mixture is measured and then corrected mixture measurements are determined; a diagnostic criterion is calculated as the average over the number of alternations of the difference for each period between the maximum measurement of the corrected rich mixture and the minimum measurement of the corrected lean mixture; then the condition of the sensor is determined based on the diagnostic criterion and a stored value.