Fleet Asset Inspection Assistant for Defect-Focused Checks
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Solution Overview
Problem
Current asset inspection processes for fleets rely heavily on human experience and visual checks, leading to high human error rates, particularly due to time constraints for commercial vehicle drivers, which compromises safety.
Innovation Solution
A system comprising sensors, user input/output devices, and a server that collects historical asset sensor data and inspection report data, uses algorithms to identify correlations, and calculates likely defects in real-time, providing indications to users for focused inspections.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If users perform manual asset inspections based on experience and visible defects, then inspection process remains simple and quick, but human error increases and inspection thoroughness decreases
Solution Approach 1:
The patent introduces an intermediary system consisting of sensors, server device with algorithms, and user output terminals that mediates between the asset and the user. This intermediary processes sensor data automatically to identify likely defects, reducing reliance on human judgment while maintaining a relatively simple user interface. The complexity is shifted from the user's cognitive process to the automated system.
Solution Approach 2:
The patent replaces the mechanical system of human visual inspection and experience-based judgment with an automated electronic system. Sensors collect data, servers process it using algorithms to identify correlations and likely defects, and present results to users. This substitution eliminates human error in defect identification while maintaining operational simplicity through automated processes.
2Reliability
If users perform thorough manual inspections of all checklist items, then inspection completeness improves, but inspection time increases significantly
Solution Approach 1:
The system performs preliminary actions by continuously collecting and analyzing sensor data before the user conducts the manual inspection. The server device pre-identifies likely defects based on sensor correlations, so when the user receives the inspection checklist, it already prioritizes items that are most likely to have defects. This preliminary analysis reduces the time needed for thorough inspection by directing attention to critical areas first.
Solution Approach 2:
The system applies partial action by focusing inspection efforts on specific high-probability defect areas rather than requiring equal attention to all checklist items. The algorithm identifies correlations in sensor data to pinpoint likely defects, allowing users to perform targeted inspections on prioritized items while maintaining high inspection completeness for critical components.
3Reliability
If manual inspection checklists include all possible defect items, then inspection coverage is comprehensive, but users become overwhelmed and skip items under time pressure
Solution Approach 1:
The patent applies local quality by providing different inspection checklist compositions to different users based on their specific asset's sensor data and historical patterns. Rather than a uniform comprehensive checklist for all users, the system tailors the checklist to highlight areas of concern for each specific asset, making the inspection process easier while maintaining appropriate coverage. Each user receives a customized view focused on their local context.
Solution Approach 2:
The system performs preliminary analysis of sensor data to pre-determine which checklist items are most relevant for each specific inspection. By analyzing correlations in historical and current sensor data before generating the checklist, the system pre-identifies which items require user attention. This preliminary filtering reduces the apparent complexity of the checklist while ensuring comprehensive coverage of critical items.
Data Source
AI summary
A system for identifying likely defects with an asset within a fleet of assets comprises sensors to detect asset sensor data, user input devices/user output terminals associated with the plurality of assets, and a server device. The server device collects historical data comprising historical asset sensor data generated by the sensors and historical inspection report data from the user input devices. The server device further uses an algorithm to identify correlations in the historical data, obtains current asset sensor data generated by sensors on a given asset and/or obtains current inspection report data from a user input device associated with the given asset, calculates one or more likely defects with the asset based on the identified correlations, and based on the current asset sensor data and/or the current inspection report data, and sends the calculated likely defects to user output terminal associated with the given asset for display thereof.