Slump Estimation for Concrete Mixers

The method of any preceding claim, wherein the gearbox efficiency model and/or slump prediction model comprise one or more of:

• a machine-learned model;
• a neural network;
• a lookup table;
• or a control surface.

A method for monitoring concrete build up in a mixing drum, the method comprising:

• receiving, from one or more sensors, drum mass data comprising a current mass of the mixing drum when empty;
• for each of a plurality of drum rotation speeds:
• receiving, from one or more sensors, motor data comprising a state a motor driving the mixing drum and gearbox data comprising a state of a gearbox arranged between the motor and the mixing drum;
• determining, by a machine-learned rotation power model, an initial rotation power of the motor from the motor data;
• determining, by a gearbox efficiency model, a gearbox efficiency from the gearbox data;
• and
• determining an adjusted rotation power for the drum rotation speed by adjusting the initial rotation power of the motor of the motor based on the gearbox efficiency;
• determining, using a build-up model, a drum build-up state of the concrete mixer from the adjusted rotation powers for the plurality of drum rotation speeds.

The method of any preceding claim, wherein the motor is an electric motor, and wherein the motor data comprises:

• a motor speed;
• an electrical current and/or electrical voltage supplied to the electric motor;
• and
• a temperature of the electric motor.

The method of any preceding claim, wherein the gearbox data comprises:

• an input torque;
• a motor or drum speed;
• a gearbox temperature;
• and
• a gearbox fluid level.

A system comprising:

• a mixing drum coupled to a motor via a gearbox;
• a plurality of sensors;
• one or more processors;
• and
• a memory,
• wherein the system is configured to perform the method of any preceding claim