Throttle Body Coking Prognosis via Residual Error Analysis
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Solution Overview
Problem
Current airflow management systems for combustion engines are reactive and limited to diagnosing faults in sensors without estimating fault severity or predicting degradation, making it difficult to forecast the future state of health or remaining useful life of sensors.
Innovation Solution
An airflow management system that includes sensors to monitor mass airflow, throttle position, and manifold air pressure, with a controller programmed to generate residual error values and throttle body coking metrics, allowing for proactive response actions based on detected variations and contamination, using mathematical models to estimate contamination and predict future sensor performance.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional reactive fault diagnosis is used, then current sensor faults can be detected, but future sensor degradation and remaining useful life cannot be predicted
Solution Approach 1:
The system performs preliminary actions by continuously monitoring sensor signals and generating residual error values before actual failures occur. The controller analyzes degradation trends in real-time and predicts future sensor performance, enabling proactive maintenance scheduling and preventing unexpected failures.
Solution Approach 2:
The system implements feedback mechanisms by continuously comparing actual sensor readings with expected values, generating residual error signals that feed back into the prediction algorithms. This closed-loop approach allows the system to adapt to changing sensor characteristics and improve prediction accuracy over time.
2Measurement precision
If continuous monitoring of multiple sensors is implemented, then accurate prediction of sensor degradation can be achieved, but system complexity increases
Solution Approach 1:
The controller is designed with multi-functionality, serving both as the engine management unit and the sensor prognosis system. By integrating multiple functions into a single controller, the system achieves accurate multi-sensor monitoring without proportionally increasing overall system complexity.
Solution Approach 2:
The system uses residual error values as intermediary parameters that simplify the complex task of sensor degradation prediction. Instead of directly analyzing multiple sensor signals simultaneously, the controller first generates residual errors that encapsulate degradation information, making the prediction process more manageable.
3Reliability
If proactive maintenance based on prediction is implemented, then potential engine issues can be prevented, but additional response actions and interventions are required
Solution Approach 1:
The system takes preliminary action by predicting sensor degradation and throttle body coking before they cause significant engine performance issues. This allows maintenance to be scheduled proactively, preventing rough idling, hesitation, and poor fuel economy that would otherwise require more extensive corrective interventions.
Solution Approach 2:
The prognosis system operates autonomously, with the controller automatically monitoring sensors, generating predictions, and triggering maintenance alerts without requiring constant human intervention. The system serves itself by managing its own health monitoring and maintenance scheduling.
Data Source
AI summary
A method of conducting prognosis for an airflow management system for a combustion engine includes adjusting a throttle body valve position control signal in response to a detected airflow variation and monitoring an airflow variation compensation (AVC) value corresponding to a degree of adjustment of the control signal. The method also includes generating a throttle body coking severity metric based on at least a plurality of residual error values and the AVC value, and executing at least one response action based on the throttle body coking severity metric exceeding a predetermined threshold.