Engine Airflow Management Prognosis via Residual Error Trends
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Solution Overview
Problem
Current airflow management systems for combustion engines are reactive and unable to predict sensor fault severity or remaining useful life, limiting their ability to forecast future health and performance degradation.
Innovation Solution
An airflow management system that includes sensors for mass airflow, throttle position, and manifold air pressure, with a controller programmed to generate residual error values and execute response actions based on trends, using mathematical models to estimate performance and detect faults, thereby predicting sensor degradation and executing proactive maintenance actions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If reactive fault monitoring is used to detect present sensor conditions, then current fault detection is achieved, but future state prediction and remaining useful life estimation are lost
Solution Approach 1:
The system performs preliminary actions by continuously monitoring sensor signals and comparing them against mathematical models to generate residual error values before actual faults occur. This proactive approach enables prediction of future sensor degradation and remaining useful life estimation, transforming reactive fault detection into predictive maintenance capability
Solution Approach 2:
The system implements feedback mechanisms by continuously comparing actual sensor readings with model-based expected values, generating residual error signals that feed back into the prognosis algorithm. This feedback loop enables real-time assessment of sensor health trends and prediction of future failure states
2Measurement precision
If multiple sensors are monitored with mathematical models to generate residual error values, then prediction accuracy is improved, but system complexity increases
Solution Approach 1:
The controller serves multiple functions by integrating sensor monitoring, mathematical model execution, residual error calculation, and prognosis generation within a single device. This multi-functionality approach improves measurement precision through comprehensive sensor analysis while managing system complexity by consolidating operations in the existing controller rather than adding separate dedicated systems
Data Source
AI summary
An engine airflow management system includes an inlet portion to receive ambient air and a mass airflow (MAF) sensor to sense mass flow rate of air passed through the inlet portion. The airflow management system includes a throttle body to selectively restrict airflow and a throttle position sensor (TPS) to sense an opening value of the throttle body. The airflow management system includes an intake manifold in fluid connection with the throttle body configured to direct airflow to a number of combustion cylinders. A manifold air pressure (MAP) sensor detects air pressure at the intake manifold. A controller is programmed to monitor signals from each of the MAF sensor, TPS, and the MAP sensor and generate a residual error value based on a difference between a model-based value and a corresponding monitored signal. A response action is based on a trend of at least two residual error values.