Maintenance Data Weighting Using Gaussian Functions
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Solution Overview
Problem
Current maintenance systems for vehicles lack an effective method to analyze and prioritize diagnostic entries based on the age of corrective actions, leading to potential inefficiencies in maintenance decision-making.
Innovation Solution
A method and system that group diagnostic entries by corrective actions and apply a weighting factor based on the age of the entries, using a Gaussian function to gradually decrease the relevance of older data, ensuring more recent actions are prioritized.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If all diagnostic entries are treated equally without weighting, then the system maintains simplicity in data processing, but the accuracy of maintenance decisions deteriorates due to lack of prioritization based on data recency
Solution Approach 1:
The patent applies parameter changes by introducing a weighting factor that modifies the significance of diagnostic entries based on their age. The weighting parameter is calculated using a Gaussian function that decreases as the age of the diagnostic entry increases, thereby dynamically adjusting the importance of each entry without fundamentally changing the data structure or processing framework.
Solution Approach 2:
The system implements dynamics by making the weighting factor variable rather than static. The weighting factor dynamically adjusts based on the age of each diagnostic entry, allowing the system to automatically prioritize more recent data while maintaining a structured approach to data processing through standardized weighting calculations.
2Reliability
If older diagnostic entries are given equal weight to newer entries, then the system maintains consistency in data treatment, but the reliability of maintenance decisions deteriorates due to outdated information having the same influence as current data
Solution Approach 1:
The patent changes the parameter of data significance by introducing an age-based weighting factor. This parameter modification allows older diagnostic entries to automatically receive lower weights, ensuring that maintenance decisions are more reliably based on current and recent data while maintaining a systematic approach through the Gaussian weighting function.
3Productivity
If the system prioritizes recent diagnostic entries through weighting, then the efficiency of maintenance processes improves by focusing on current issues, but the loss of historical context increases as older data becomes less influential
Solution Approach 1:
The patent applies parameter changes by using a Gaussian weighting function that gradually decreases the importance of older entries rather than eliminating them completely. This parameter-based approach maintains historical context in the database while reducing its influence on current decisions, balancing efficiency gains with information preservation through controlled parameter adjustment.
Data Source
AI summary
Methods and maintenance systems for use in analyzing data related to maintenance of at least one vehicle are disclosed. One example method includes retrieving, by a computing device, a plurality of diagnostic entries associated with at least one fault message from a database of diagnostic entries, each diagnostic entry including an identified corrective action and a date on which the identified corrective action was taken; identifying a plurality of groups of diagnostic entries, wherein the diagnostic entries in a group have a same corrective action, and each group has a confidence level associated with its corrective action; and weighting the confidence level for each group based on an age of the plurality of diagnostic entries in the group.