Parallel Intake MAF Sensor Failure Detection
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Solution Overview
Problem
Conventional diagnostic systems fail to accurately determine which mass air flow (MAF) sensor has failed in a parallel intake engine, leading to increased costs and decreased performance due to incorrect failure detection.
Innovation Solution
A system and method that estimate the total MAF into the engine based on throttle cross-sectional area and pressure ratio, split the estimated total MAF into separate components using a predetermined factor, calculate MAF residuals by comparing estimated and measured values, and detect sensor failures based on thresholds, allowing for accurate failure detection and discarding faulty sensor measurements.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional diagnostic systems are used to detect MAF sensor failures, then the system structure remains simple, but the measurement precision and reliability of failure detection deteriorate
Solution Approach 1:
The patent segments the total MAF measurement into separate MAF measurements for each induction path by using multiple MAF sensors (first MAF sensor for first induction path, second MAF sensor for second induction path). This segmentation allows the system to identify which specific sensor has failed by comparing individual path measurements against expected values, thereby improving failure detection accuracy without requiring a completely complex diagnostic system.
Solution Approach 2:
The patent introduces an intermediary calculation approach where the control module calculates expected MAF values based on throttle position and engine operating conditions, then compares these expected values against actual sensor readings. This intermediary comparison mechanism enables precise failure detection by identifying discrepancies between expected and actual measurements, improving detection accuracy while maintaining reasonable system complexity.
2Reliability
If multiple MAF sensors are installed in parallel intake paths, then the reliability of failure detection improves, but the device complexity increases
Solution Approach 1:
The patent divides the intake system into separate induction paths with dedicated MAF sensors for each path. This segmentation allows the system to independently monitor each path's air flow, making it possible to identify which specific sensor has failed. The control module processes measurements from multiple sensors and compares them against expected values to determine failure, thereby improving reliability while managing complexity through structured sensor placement.
Solution Approach 2:
The patent makes the control module multi-functional by having it perform both normal engine control functions and diagnostic functions for MAF sensor failure detection. The control module calculates expected MAF values based on throttle position and engine conditions, compares these against sensor readings, and identifies failures. This multi-functionality improves reliability without proportionally increasing device complexity, as the existing control module handles additional diagnostic tasks.
3Loss of information
If incorrect failure detection occurs, then the diagnostic system remains simple, but the loss of information and time increases
Solution Approach 1:
The patent implements a feedback mechanism where the control module continuously compares actual MAF sensor readings against expected values calculated from throttle position and engine operating conditions. When a discrepancy exceeds a threshold, the system identifies a sensor failure and generates an appropriate diagnostic code. This feedback loop provides accurate failure identification information while maintaining reasonable calculation complexity through efficient comparison logic.
Solution Approach 2:
The patent replaces complex mechanical diagnostic systems with electronic calculation and comparison methods. Instead of using additional mechanical sensors or complex diagnostic hardware, the system uses the control module's computational capabilities to calculate expected MAF values, compare them against actual readings, and identify failures. This substitution reduces the need for complex mechanical diagnostic systems while providing accurate failure information.
Data Source
AI summary
A system for a parallel intake engine includes first, second, third, and fourth modules. The first module estimates a total mass air flow (MAF) into the engine based on a cross-sectional area of a throttle and a pressure ratio across the throttle. The second module estimates first and second MAFs through first and second induction paths, respectively, based on the estimated total MAF and a factor. The third module calculates first and second differences between the estimated first and second MAFs and first and second MAFs measured by first and second MAF sensors, respectively. The fourth module detects failures of the first and second MAF sensors based on the first and second differences and first and second thresholds, respectively.