Combustion Engine Failure Diagnosis Using Intake-Exhaust Signatures
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Solution Overview
Problem
Combustion engines, particularly turbocharged systems, face component failures that exhibit similar performance symptoms, making it difficult to diagnose the specific failing component, leading to extended downtime and sub-optimal fuel efficiency and emissions.
Innovation Solution
A system utilizing temperature and pressure sensors on the exhaust and intake manifolds, coupled with a computing device, analyzes delta values and trend lines to identify component failures by comparing sensor data to stored signatures, enabling accurate classification of the failure.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional diagnostic methods are used, then the system complexity is low, but the measurement precision and diagnostic accuracy are insufficient to distinguish between different component failures
Solution Approach 1:
The diagnostic system segments the engine into multiple monitored zones (exhaust manifold, intake manifold, turbocharger) and assigns specific sensors to each zone. This segmentation allows targeted monitoring of different components, improving diagnostic accuracy by isolating failure locations while keeping the overall system manageable through modular sensor placement
Solution Approach 2:
The computing device performs multiple functions: it collects data from various sensors, processes temperature and pressure signals, compares against failure signatures, and provides diagnostic outputs. This multi-functionality consolidates what would otherwise require separate diagnostic systems into a single integrated unit, improving accuracy without proportionally increasing complexity
2Measurement precision
If extended diagnostic investigation is performed to identify the failing component, then the diagnostic accuracy improves, but the loss of time increases
Solution Approach 1:
The system continuously collects and stores sensor data before failure occurs, maintaining baseline information about engine operation. When a failure happens, this pre-collected data provides immediate context for diagnosis, eliminating the need for time-consuming investigation and enabling rapid identification of the failing component
Solution Approach 2:
The computing device continuously monitors sensor readings and compares them against stored failure signatures, providing real-time feedback on system health. This continuous feedback loop enables the system to detect and identify failures as they occur rather than investigating them after symptoms manifest, significantly reducing diagnostic time
3Productivity
If the engine is continued to run during sub-optimal conditions to avoid downtime, then the productivity is maintained, but the loss of energy increases due to poor fuel efficiency and emissions
Solution Approach 1:
The diagnostic system enables the engine to self-diagnose and self-report its condition through automated sensor monitoring and failure detection. This self-service capability allows operators to make informed decisions about whether to continue operation or shut down, optimizing the balance between maintaining productivity and avoiding energy waste from prolonged operation under poor conditions
Data Source
AI summary
A system for diagnosing component failures of a combustion engine may include a pair of temperature sensors arranged on respective left and right exhaust manifolds of a combustion engine, a pair of pressure sensors arranged on respective left and right intake manifolds of the combustion engine, and a computing device in data communication therewith. The computing device may be configured for receiving temperature values and pressure signals and calculating delta temperature values and delta pressure values. The computing device may establish trend lines for the temperature values, the pressure values, the delta temperature values, and the delta pressure values and may compare the trend lines to a plurality of signatures stored on the computer readable storage medium. The computing device may identify a signature of the plurality of signatures that corresponds to the trend lines and classify the time as exhibiting a particular component failure.