Tank Depletion Estimation Using Machine Learning
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Solution Overview
Problem
Existing methods for estimating the time until empty or near empty in tanks of off-road vehicles lack accuracy, making it difficult to plan and coordinate refilling and logistics effectively.
Innovation Solution
A method that utilizes a machine learning model to estimate the time until empty or near empty in a tank by collecting input data on consumption rate, tank capacity, and initial quantity of the material, and then adjusts these estimates based on weighted inputs and equation sets applicable to different operational periods.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If a level gauge or sensor is used to monitor material in the tank, then the tank level can be monitored, but the estimate of when the tank will be empty lacks accuracy
Solution Approach 1:
The system transitions from static level monitoring to dynamic parameter analysis by incorporating consumption rate, tank capacity, and initial quantity variables. The machine learning model processes these changing parameters to generate accurate depletion time predictions, resolving the contradiction between simple monitoring and accurate estimation.
Solution Approach 2:
The system implements feedback mechanisms where the machine learning model continuously refines its predictions based on collected input data and actual consumption patterns. This feedback loop improves the accuracy of empty tank estimates over time, transforming unreliable predictions into reliable information for logistics planning.
2Measurement precision
If traditional estimation methods are used, then the system is simple to operate, but the accuracy of depletion time prediction is insufficient for effective logistics planning
Solution Approach 1:
The patent introduces a machine learning model as an intermediary between simple sensor data collection and complex logistics planning requirements. This intermediary processes raw input data (consumption rate, tank capacity, initial quantity) and transforms it into accurate depletion time predictions, achieving high measurement precision without requiring direct complex estimation systems.
Solution Approach 2:
The system performs preliminary data collection and model initialization during an initialization period before full operational use. This preliminary action prepares the machine learning model with necessary data and configurations, enabling accurate predictions without adding operational complexity during actual tank monitoring and logistics planning activities.
Data Source
AI summary
During an initialization period of a machine learning model, an electronic data processor is configured to estimate an initial depletion estimate of time period until empty for a material in the tank or container of a machine based on summing initial weighted inputs to the machine learning model in accordance with an initial equation set being applicable to the initialization period that is defined by an initial sub-operation period. After the initialization period of the machine learning model, an electronic data processor is configured to estimate a revised depletion time, where the revised depletion time comprises a time duration until empty or near empty.