Digital Twin FRU Identification for Engine Diagnostics
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current diagnostic methods for engines require manual pinpoint testing, leading to substantial vehicle downtime, labor costs, and warranty costs due to the need for manual evaluation of individual component health.
Innovation Solution
A computing system that receives operating data from a telematics circuit associated with an engine system, determines field-replaceable units (FRUs) based on the data, generates computer-based simulations for degradation levels of these FRUs, identifies the most probable failure by ranking the simulations, and transmits an electronic notification with the failure data to a computing device.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual pinpoint testing is used to diagnose engine problems, then diagnostic accuracy is improved, but vehicle downtime and labor costs increase substantially
Solution Approach 1:
The system performs preliminary diagnostic actions by continuously monitoring engine parameters and comparing them against known failure patterns before actual failures occur. The digital twin simulates various failure scenarios in advance, allowing the system to pre-identify potential issues and their signatures, thus eliminating the need for time-consuming manual pinpoint testing when problems actually occur.
Solution Approach 2:
The patent creates a digital copy (digital twin) of the physical engine system that replicates its behavior and failure modes. This virtual model allows diagnostic testing and analysis to be performed on the copy rather than the actual engine, enabling comprehensive diagnostic evaluation without taking the physical vehicle out of service.
2Measurement precision
If manual pinpoint testing is used to evaluate component health, then diagnostic thoroughness is improved, but labor costs increase substantially
Solution Approach 1:
The diagnostic system performs self-service by automatically monitoring its own engine parameters, comparing them against the digital twin's simulated failure patterns, and identifying issues without human intervention. The system autonomously evaluates component health, generates diagnostic reports, and recommends repairs, eliminating the need for expensive manual labor while maintaining thorough diagnostic evaluation.
Solution Approach 2:
The patent replaces manual mechanical diagnostic procedures with an automated electronic system. Instead of technicians physically testing components with manual tools, the system uses electronic sensors, data processing, and digital twin simulation to automatically evaluate component health, thereby reducing labor costs while maintaining or improving diagnostic quality.
3Loss of information
If comprehensive manual diagnostics are performed, then identification of specific faulty parts is improved, but operational downtime increases
Solution Approach 1:
The system uses a digital copy of the engine (digital twin) to perform comprehensive diagnostic analysis. By simulating various failure scenarios and comparing actual sensor data against these virtual models, the system can rapidly identify specific faulty parts without physically inspecting or testing each component on the actual engine, thus minimizing operational downtime while maintaining accurate fault identification.
Solution Approach 2:
The digital twin pre-simulates various failure modes and stores their characteristic signatures. When diagnostic data is received, the system quickly matches actual readings against these pre-prepared failure patterns, enabling rapid identification of faulty parts without time-consuming manual evaluation procedures.
Data Source
AI summary
A system can include one or more processors coupled with memory in communication with a vehicle system comprising a plurality of field-replaceable units (FRUs). The one or more processors can be configured to: generate, for each FRU of the plurality of FRUs of the vehicle system, a computer-based simulation; determine a degradation level for each FRU of the plurality of FRUs based on the computer-based simulation and operating data of each FRU of the plurality of FRUs; identify, from the plurality of FRUs, an FRU corresponding to a failure in the vehicle system based on the degradation level for the FRU; and provide an electronic notification identifying the failure in the vehicle system as corresponding to the FRU.