Fuel Injector Failure Detection Using AI Field Data Models
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Solution Overview
Problem
Existing diagnostics for fuel injector failures in internal combustion engines face challenges in accuracy, precision, reliability, feasibility, and speed of data processing.
Innovation Solution
A big data-based artificial intelligence model is employed to analyze field performance data from multiple vehicles, using a failure mode model to identify injector failures by evaluating injection pressures and quantities, and performing compensatory actions such as modifying engine operation or scheduling service.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional diagnostic methods are used for fuel injector failures, then the system structure is simple, but the accuracy and reliability of failure identification deteriorates
Solution Approach 1:
The patent replaces traditional mechanical/electronic diagnostic systems with an artificial intelligence-based diagnostic system. The AI model analyzes sensor data from the fuel injection system to identify failure modes, substituting complex physical diagnostic equipment with software-based intelligence that achieves higher accuracy without requiring additional hardware complexity.
Solution Approach 2:
The patent introduces an artificial intelligence model as an intermediary between the fuel injection system and the diagnostic output. This AI intermediary processes sensor data, identifies patterns indicating failure modes, and provides diagnostic recommendations, acting as a intelligent mediator that enhances accuracy while maintaining system simplicity.
2Reliability
If comprehensive data analysis is performed for injector diagnostics, then the reliability improves, but the speed of data processing deteriorates
Solution Approach 1:
The patent performs preliminary actions by pre-training the AI model on extensive historical data and failure mode patterns before actual diagnostic use. This pre-processing of knowledge allows the system to make rapid, reliable decisions during operation without performing computationally intensive analysis in real-time, thus maintaining both reliability and processing speed.
Solution Approach 2:
The patent transforms raw sensor data into meaningful diagnostic parameters through the AI model. By changing the representation of data from raw sensor readings to interpreted failure indicators, the system achieves reliable diagnostics with faster processing, as the AI has already performed the complex analysis during training and can quickly match patterns during operation.
3Measurement precision
If detailed field performance data is collected from multiple vehicles, then the precision of failure mode identification improves, but the quantity of data to be processed increases
Solution Approach 1:
The patent extracts only the most relevant features and patterns from the large volume of field performance data during the AI training phase. By taking out and storing only the critical diagnostic information and failure mode patterns, the system achieves high precision identification while avoiding the need to process the entire raw data set during actual diagnostics.
Solution Approach 2:
The patent creates a simplified copy or representation of the complex field data through the AI model's learned parameters and patterns. Instead of storing and processing all original sensor data, the system uses the AI model's internal representation that captures the essential diagnostic information, reducing data volume while maintaining identification precision.
Data Source
AI summary
A process for identifying fueling system failure modes includes operating a failure mode model derived from a big data dataset, the big data dataset comprising field performance data received from a plurality of engine fueling systems including one or more injectors, the field performance data comprising injection pressures and injection quantitates for a plurality of injections performed by one or more injectors of the engine fueling systems, the failure mode model including a plurality of predetermined rules for evaluating operation of engine fueling systems; receiving target field performance data from a target fueling system including one or more target injectors configured to provide fuel to a target engine system for performance evaluation; evaluating the target field target performance data using the failure mode model to identify a failure mode of the target fueling system; and performing a compensatory action in response to the evaluating.