Machine Operation Analysis via Targeted Input Parameter Generation
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Solution Overview
Problem
Existing simulation models for analyzing machine operations select input parameters randomly without considering desired customer outputs, limiting their effectiveness in understanding and optimizing machine performance.
Innovation Solution
A system and method that utilize a data warehouse, telematics system, data extraction module, failure injection module, and machine model to generate input parameters based on customer inputs and failure information, deriving output parameters that reflect machine operations and failures, enabling targeted optimization.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If input parameters are selected randomly from database, then the simulation model can operate, but the analysis effectiveness is limited and cannot meet customer-specific optimization needs
Solution Approach 1:
The system transforms the approach from random parameter selection to targeted parameter generation by changing how input parameters are determined. Instead of selecting parameters randomly from a database, the system now generates parameters based on customer-specified output parameters and failure patterns, fundamentally altering the parameter selection mechanism to improve both analysis precision and customer adaptability
Solution Approach 2:
The system implements feedback by using desired output parameters to guide input parameter generation. The customer specifies what output they want to analyze, and the system works backward to generate appropriate input parameters that will produce those outputs, creating a feedback loop that ensures the simulation addresses specific customer needs rather than operating with random parameters
2Adaptability or versatility
If simulation model uses general random parameters, then it can process any case, but it cannot provide targeted analysis for specific customer goals or failure patterns
Solution Approach 1:
The system performs preliminary action by generating input parameters in advance based on customer specifications before running the simulation. Instead of using random parameters during simulation execution, the system pre-generates parameters tailored to the customer's desired output parameters and failure patterns, ensuring that customer-specific information is incorporated from the outset
Solution Approach 2:
The system applies inversion by working backward from the desired output parameters to generate the input parameters. Rather than starting with random inputs and seeing what outputs are produced, the system takes the customer's specified output goals and failure patterns and generates the corresponding input parameters that will achieve those specific outcomes
Data Source
AI summary
A system for analyzing one or more operations associated with a component of a machine is disclosed. The system includes a data warehouse for storing data associated with the machine. The system includes a data extraction module, which extracts the data from the data warehouse and receives a customer input. The data extraction module generates an input parameter based on the data from the data warehouse and the customer input. The system includes a failure injection module for storing information of the one or more failures associated with the component of the machine. The system also includes a machine model, which is in communication with the data extraction module and the failure injection module. The machine model derives an output parameter associated with the one or more operations of the component of the machine, based on the input parameter and the information of the one or more failures.