PCV Diagnostics Using Physics-Based Pressure Models
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Solution Overview
Problem
Existing PCV diagnostic methods, such as those described by Jentz et al., are less adaptable to hardware changes and have lower diagnostic confidence due to polynomial equation modeling, leading to potential misdiagnosis and reduced system performance.
Innovation Solution
A method involving the generation of estimated crankcase pressures using a nominal and faulted model of the PCV system, comparing these models with actual sensor readings, and triggering indicators for sensor degradation when differences exceed thresholds, utilizing physics-based pressure balanced equations to enhance diagnostic confidence.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If polynomial equation modeling is used for PCV system diagnostics, then the diagnostic logic can be implemented, but the diagnostic confidence decreases and adaptability to hardware changes is reduced
Solution Approach 1:
The patent changes the mathematical modeling approach from polynomial equations to physics-based pressure balanced equations. This parameter change in the modeling methodology simultaneously improves adaptability to hardware changes and increases diagnostic confidence, as physics-based equations naturally adapt to different hardware configurations while providing more reliable diagnostic results.
Solution Approach 2:
The patent substitutes the mathematical modeling system from polynomial equations to physics-based equations. This replacement of the modeling mechanism enables better adaptability to hardware variations while maintaining higher diagnostic confidence, as physics-based equations reflect actual physical behavior of the PCV system.
2Measurement precision
If continuous monitoring of crankcase pressure sensor is performed, then diagnostic accuracy is maintained, but processing resources are consumed
Solution Approach 1:
The patent implements periodic monitoring of the crankcase pressure sensor based on triggered conditions rather than continuous monitoring. The system periodically evaluates whether the difference between faulted and nominal models exceeds a threshold, and only then initiates monitoring. This periodic action maintains diagnostic accuracy while significantly reducing processing resource consumption.
Solution Approach 2:
The patent performs preliminary comparison between faulted and nominal models before initiating actual sensor monitoring. This preliminary action determines whether monitoring is necessary based on model differences, preventing unnecessary continuous monitoring and conserving processing resources while maintaining diagnostic accuracy when needed.
3Reliability
If faulted model and nominal model comparison is performed continuously, then PCV system component diagnosis is improved, but computational load increases
Solution Approach 1:
The patent implements periodic comparison between faulted and nominal models based on trigger conditions rather than continuous comparison. The system periodically checks whether model differences exceed a threshold and only initiates detailed diagnosis when necessary. This periodic approach maintains reliable PCV system component diagnosis while reducing computational load.
Solution Approach 2:
The patent performs partial model comparison by first evaluating whether the difference between faulted and nominal models surpasses a threshold before conducting full diagnostic analysis. This partial action approach maintains diagnostic reliability for actual faults while avoiding excessive computational load for normal operating conditions.
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
This approach increases diagnostic confidence and reduces misdiagnosis, improving PCV system performance by selectively sampling pressure sensors and conserving processing resources.
Implementation Method 1
the PCV system is modeled based on one or more pressure balanced equations which may be physics based
Data Source
AI summary
Methods and systems are provided for implementing a diagnostic technique for a positive crankcase ventilation (PCV) assembly. In one example, the method includes generating an estimated crankcase pressure at the location of a crankcase pressure sensor based on a nominal model and a faulted model of a PCV system that is generated by a controller. The method further includes diagnosing a PCV system component based on a difference between the estimated crankcase pressure and an input from the crankcase pressure sensor; and responsive to the crankcase pressure sensor having a faulted diagnosis, triggering a sensor degradation indicator.