Fuel Supply Abnormality Diagnosis Using Minimum Pressure Data
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Solution Overview
Problem
Existing fuel supply systems face challenges in accurately diagnosing fuel pump deterioration and other abnormalities, as they often misinterpret decreases in fuel pressure under specific conditions as pump deterioration, leading to incorrect diagnoses.
Innovation Solution
An abnormality diagnosis system that records minimum fuel pressure data and associated conditions, including elapsed time and fuel temperature, to differentiate between impeller deterioration, check valve operation failures, and other causes of pressure drops, using machine learning to enhance diagnostic accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If feedback correction value integration is used to diagnose fuel pump deterioration, then fuel pump deterioration can be detected, but false diagnoses occur due to pressure decreases under specific conditions
Solution Approach 1:
The system records the state data (elapsed time, fuel temperature) at the moment minimum fuel pressure occurs before any diagnosis is made. This preliminary recording of conditions allows subsequent analysis to distinguish between actual pump deterioration and pressure decreases caused by specific operating conditions, thereby preventing false diagnoses while maintaining accurate detection of real deterioration.
2Measurement precision
If diagnosis is performed under specific operating conditions, then fuel pump deterioration can be identified, but other causes of pressure decrease cannot be detected
Solution Approach 1:
The system monitors and records multiple parameters including elapsed time since fuel pump start and fuel temperature, in addition to fuel pressure. By analyzing the combination of these parameters when minimum pressure occurs, the system can identify both fuel pump deterioration and other abnormalities such as check valve failures, thereby expanding detection scope while maintaining precise deterioration identification.
3Adaptability or versatility
If minimum fuel pressure and associated state data are recorded, then various abnormalities can be detected, but data processing complexity increases
Solution Approach 1:
The control device automatically records the state data (elapsed time, fuel temperature) at the moment minimum fuel pressure occurs during each trip. This preliminary automatic recording eliminates the need for complex real-time analysis systems, as the critical data is already captured and stored for later diagnostic processing, thereby expanding abnormality detection capability without significantly increasing system complexity.
4Measurement precision
If elapsed time and fuel temperature data are stored as diagnosis data, then failure spots can be precisely identified, but storage requirements increase
Solution Approach 1:
The system selectively stores only the critical state data (elapsed time since fuel pump start and fuel temperature) that occurs at the moment of minimum fuel pressure, rather than continuously storing all operational parameters. This targeted local quality approach provides sufficient information for precise failure spot identification while minimizing overall data storage requirements.
Data Source
AI summary
An abnormality diagnosis system is applied to a fuel supply system including a fuel pump that pumps fuel from a fuel tank and a fuel pipe in which fuel discharged from the fuel pump flows. The abnormality diagnosis system stores a minimum fuel pressure in the fuel pipe in one trip after a main switch of the fuel supply system is turned on and until the main switch is turned off and data indicating a state when the minimum fuel pressure was recorded as diagnosis data in a storage device. In the abnormality diagnosis system, an execution device determines a failure spot associated with a decrease in fuel pressure in the fuel pipe using the diagnosis data stored in the storage device and diagnoses an abnormality of the fuel supply system.