Concrete Mixer Slump Estimation Using Motor and Gearbox Data
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Solution Overview
Problem
Existing concrete mixing drums face issues with indirect and expensive sensors for slump measurement, leading to high rejection rates and inefficient drum maintenance due to concrete build-up, which affects performance and energy consumption.
Innovation Solution
A system using machine-learning models to predict slump and monitor drum build-up by analyzing motor and gearbox data, adjusting rotation power, and adding water/plasticizer as needed, while monitoring drum health through sensors and AI models.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If sensors are placed inside the drum to measure batch quality, then slump measurement capability is improved, but device complexity and maintenance requirements increase
Solution Approach 1:
The patent uses motor data and gearbox data as intermediary measurements to indirectly determine slump values. Instead of placing sensors directly in the drum, the system measures motor current, voltage, and speed along with gearbox parameters, then uses these intermediary measurements to calculate slump through machine learning models. This resolves the contradiction by achieving measurement capability without direct sensor installation in the drum.
Solution Approach 2:
The patent replaces mechanical sensors inside the drum with an electrical/mechanical system outside the drum. By measuring motor electrical parameters (current, voltage, speed) and gearbox data, the system substitutes direct mechanical measurement with indirect electrical measurement, reducing device complexity while maintaining measurement capability.
2Duration of action of stationary object
If drum build-up is not monitored, then operational time between maintenance is extended, but performance and energy consumption deteriorate
Solution Approach 1:
The patent implements continuous feedback monitoring of drum build-up using motor and gearbox data. The system constantly measures operational parameters, compares them against baseline values, and provides feedback on build-up accumulation. This allows operators to monitor performance in real-time and schedule maintenance at optimal intervals, extending operational time while preventing excessive energy consumption.
Solution Approach 2:
The system performs preliminary detection of drum build-up trends by continuously analyzing motor data and gearbox data. By detecting build-up accumulation before it reaches critical levels, the system enables proactive maintenance scheduling that extends operational intervals while preventing the performance deterioration and energy consumption increases associated with severe build-up.
3Reliability
If slump testing is performed on all deliveries, then quality control is improved, but rejection rates increase
Solution Approach 1:
The patent replaces traditional mechanical slump cone testing with an electrical measurement system using motor and gearbox data. This substitution enables continuous, non-intrusive quality assessment that does not require stopping delivery operations, thereby maintaining reliability while preserving productivity.
Solution Approach 2:
The system uses motor current, voltage, and speed as intermediary measurements to assess concrete quality. These intermediary electrical measurements provide continuous quality control data without requiring physical slump cone testing, allowing all deliveries to be monitored without causing rejections due to testing interruptions.
4Reliability
If drum build-up is removed frequently, then drum performance is maintained, but productivity decreases due to downtime
Solution Approach 1:
The patent uses continuous feedback from motor and gearbox data to monitor drum build-up accumulation in real-time. This feedback enables maintenance to be performed only when actually needed, rather than on fixed schedules. Operators can maintain drum performance while minimizing downtime by cleaning only when build-up reaches levels that affect performance.
Solution Approach 2:
The system performs preliminary assessment of build-up levels using motor data analysis, allowing operators to delay maintenance until necessary. By detecting build-up trends early and monitoring their progression, the system enables extended operational intervals while maintaining drum performance, thus maximizing productivity.
Data Source
AI summary
This specification describes systems, apparatus and methods for measuring the slump of a concrete mix using one or more machine-learning models. According to a first aspect of this specification, there is described a method for predicting a slump value of a concrete mix in a mixing drum (104) of a concrete mixer (100). The method comprises: receiving, from one or more sensors (110A, 110B, 110C, 110D), motor data comprising a state a motor (106) driving the mixing drum (104), gearbox data comprising a state 2023/237862 of a gearbox (108) arranged between the motor (106) and the mixing drum (104), and load data relating to a mass of the concrete mix in the mixing drum (104); determining, by a machine-learned rotation power model, an initial rotation power of the motor (106) from the motor data; determining, by a gearbox efficiency model, a gearbox efficiency from the gearbox data; determining an adjusted rotation power by adjusting the initial rotation power of the motor (106) of the motor based on the gearbox efficiency; and determining, by a slump prediction model, an estimated slump value of the concrete mix in the mixing drum (104) from the load data and the adjusted rotation power.