Transport Chassis Load Status Detection Using Sensor Profiles
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Solution Overview
Problem
Managing a large fleet of transport chassis across geographical locations to determine their loaded or unloaded status in an automated manner is challenging, affecting usage tracking, billing, maintenance scheduling, and theft detection.
Innovation Solution
Equipping transport chassis with sensors and communication transceivers that send measurement data to a server system for load status determination using profile data and machine-readable instructions, enabling accurate determination of loaded or unloaded status through various sensor types and network communication.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual tracking methods are used for transport chassis, then device complexity is reduced, but measurement precision of load status deteriorates
Solution Approach 1:
The sensor system is designed to perform multiple functions: detecting load status, determining geographic location, and enabling communication. This multi-functional approach improves measurement precision while minimizing the addition of separate devices, thus managing system complexity.
Solution Approach 2:
A server system acts as an intermediary that receives sensor data from transport chassis, processes the information using profile data, and determines load status. This mediator approach allows accurate determination without requiring complex processing at each chassis, distributing system complexity appropriately.
2Productivity
If automated load status determination is implemented across a large fleet, then productivity is improved, but device complexity increases
Solution Approach 1:
The fleet management system is segmented into independent transport chassis units, each with its own sensor and communication capabilities. This allows automated tracking of multiple chassis simultaneously, improving productivity while managing complexity through modular, independent units rather than a monolithic system.
Solution Approach 2:
The system continuously receives sensor data from transport chassis, processes it against profile data, and determines load status in real-time. This feedback loop enables automated fleet management, improving productivity by eliminating manual tracking while the standardized feedback mechanism keeps system complexity manageable.
3Loss of information
If sensors and communication transceivers are equipped on each chassis, then measurement precision is improved, but loss of information is reduced through automated data collection
Solution Approach 1:
Profile data is pre-established and stored in the server system before actual load status determination. This preliminary preparation of reference data enables quick, accurate comparison with sensor readings, ensuring information availability while minimizing the processing complexity required at each chassis.
Data Source
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AI summary
In some examples, a system receives sensor data from a sensor of a transport chassis configured to carry a cargo transportation unit (CTU), and determines, based on the sensor data and a profile learned based on past data, whether the transport chassis is loaded with a CTU or cargo.