Vehicle System Comparison for Accurate Maintenance Identification
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Solution Overview
Problem
Existing vehicle system monitoring and maintenance algorithms require significant memory and processing resources and often fail to consider various environmental and operational variables, leading to incorrect maintenance scheduling or missed maintenance needs.
Innovation Solution
A system that compares vehicle system data from multiple systems sharing common characteristics, such as geographic location or environmental conditions, to identify anomalies and schedule maintenance based on deviations from average performance, eliminating the need for complex algorithms.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If complex algorithms including artificial intelligence are used for vehicle system monitoring, then maintenance scheduling capability is improved, but memory space and processing resources are significantly consumed
Solution Approach 1:
The patent segments the fleet into multiple comparison groups based on shared characteristics (e.g., geographic location, vehicle type, operating conditions). Instead of using a single complex algorithm to analyze all vehicles, the system divides the analysis into smaller, manageable segments where each group is compared independently, reducing the computational burden on any single processing unit.
Solution Approach 2:
The patent introduces an intermediary comparison mechanism that acts as a mediator between raw vehicle data and maintenance decisions. Rather than directly applying complex AI algorithms to determine maintenance needs, the system uses intermediate comparison steps that evaluate vehicle performance relative to peer groups, simplifying the decision-making process while maintaining accuracy.
2Extent of automation
If algorithms are used for maintenance determination, then automated decision-making is achieved, but accuracy deteriorates due to failure to consider all environmental and operational variables
Solution Approach 1:
The patent applies local quality by tailoring the comparison criteria to specific local conditions. Each comparison group is defined by local characteristics such as geographic location, environmental conditions, and operational parameters. This allows the automated system to adapt its analysis to local contexts, improving accuracy by considering relevant local variables without requiring a single complex global algorithm.
Solution Approach 2:
The patent dynamically adjusts comparison parameters based on the specific characteristics of each vehicle and its operating environment. Rather than using fixed algorithmic thresholds, the system modifies comparison parameters (such as performance benchmarks, operational ranges, and maintenance thresholds) to reflect actual operating conditions, thereby improving the accuracy of automated maintenance determinations.
3Speed
If algorithms make maintenance determinations without considering all variables, then processing speed is improved, but reliability deteriorates due to incorrect or missed maintenance identification
Solution Approach 1:
By segmenting the fleet into comparison groups with shared characteristics, the system processes vehicles in smaller batches rather than attempting to analyze all vehicles simultaneously. This segmentation maintains fast processing speeds while improving reliability, as each segment can be analyzed with simpler, faster comparisons that still account for relevant variables specific to that group.
Data Source
AI summary
A system is provided that includes a controller having one or more processors. The one or more processors may be configured to obtain first vehicle system data from a first vehicle system of plural vehicle systems based on a common characteristic shared by the plural vehicle systems, and obtain second vehicle system data from a second vehicle system of the plural vehicle systems, the second vehicle system data based on the common characteristic shared by the two or more vehicle systems. The one or more processors may also be configured to compare the first vehicle system data to the second vehicle system data, and identify one of the first vehicle system or the second vehicle system as a candidate vehicle system for maintenance based on comparing the first vehicle system data to the second vehicle system data.