Inference-Driven Repair Analytics for Machine Failure Diagnosis
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Solution Overview
Problem
Current systems for machine performance and fault monitoring require users to manually interpret vast amounts of data to determine appropriate repair actions, which can be time-consuming and may lead to suboptimal maintenance decisions, especially for complex machines like aircraft, resulting in extended downtime and costs.
Innovation Solution
A knowledge-based system utilizing an inference engine and knowledge base to analyze historical data, identify patterns in operating conditions, and recommend repair actions based on similar past failures, enabling quick and accurate repairs.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If users manually interpret all performance data to determine repair actions, then complete data analysis is achieved, but time consumption increases and repair efficiency decreases
Solution Approach 1:
The patent introduces an automated intermediary system comprising a processor and algorithm that mediates between raw performance data and repair decisions. This intermediary automatically analyzes performance data, compares it against historical data and known fault patterns, and generates repair recommendations, eliminating the need for users to manually interpret all data while ensuring comprehensive analysis.
Solution Approach 2:
The patent replaces the manual mechanical process of data interpretation with an automated electronic system. The processor executes algorithms that automatically compare current performance data with historical data and fault patterns, substituting human manual analysis with machine-based automated analysis that is both faster and more comprehensive.
2Reliability
If users manually sort through all performance data, then accurate repair decisions can be made, but the complexity of the process increases
Solution Approach 1:
The patent extracts and isolates the critical decision-making logic into a separate automated system. The processor extracts relevant patterns from performance data by comparing against historical data and known fault patterns, separating the complex analysis task from the user and presenting only the essential repair recommendations.
Solution Approach 2:
The patent changes the parameters of data processing by transforming raw performance data into meaningful comparisons with historical data and fault patterns. The system automatically adjusts and weights different data parameters to identify the most relevant indicators of faults, simplifying the complex data landscape for the user.
3Productivity
If the vehicle remains in service longer, then productivity increases, but the risk of failure increases requiring more frequent monitoring
Solution Approach 1:
The patent enables preliminary detection and diagnosis of faults before they lead to actual failures. By continuously monitoring performance data and comparing it against historical patterns, the system identifies early signs of problems and generates repair recommendations in advance, allowing maintenance to be performed before failures occur and keeping vehicles in service longer.
Solution Approach 2:
The patent implements a feedback loop where performance data is continuously monitored, analyzed against historical data, and used to generate real-time repair recommendations. This feedback mechanism allows the system to adapt to changing conditions and provide ongoing guidance for maintaining vehicle reliability and availability.
Data Source
AI summary
A method is provided for repair of a mechanical or electromechanical system of a machine. An inference engine receives indication of a failure mode of the system, and measurements of operating conditions of the machine, and the inference engine defines a current problem including (a) the failure mode of the system, and (b) a pattern in the measurements. The inference engine searches a knowledge base with historical problems including (a) failure modes of systems of the machine, and (b) patterns in measurements of the operating conditions, and respective solutions with (c) repair actions performed to address the respective ones of the failure modes, for a respective solution to a historical problem most similar to the current problem. This is inferred as a solution to the current problem, and the inference engine generates an output display indicating the repair action of the solution to address the failure mode of the system.