Method for calculating bulk material feed rates or bulk material loads of a vibratory machine

A device for determination of a bulk material conveyor quantity or a bulk material load of a vibratory conveyor machine, comprising:

• at least one acceleration sensor, velocity sensor or route sensor arranged to acquire raw measurement data of the vibratory conveyor machine,
• an electronic evaluation unit for:
• processing the raw measurement data into at least one feature consisting of a directional vibration measurand from the list:
• Amplitude, frequency, and phase
• creating feature sets consisting of at least the one feature,
• creating a regression model using feature datasets, wherein for training of the regression model, model-based bulk material loading values are matched with a reference signal or reference load signal of a bulk material conveyor quantity or bulk material loading,
• determining at least one of a model-based bulk material load value and a model-based bulk material feed rate, and
• dynamically controlling at least one of a conveyor capacity, discharge of a bulk material, and one or more vibratory parameters of the vibratory conveyor machine based on at least one of the model-based bulk material load value and the model-based bulk material feed rate, and
• a screen or a display showing the at least one of the model-based bulk material load value and the model-based bulk material feed rate of the vibratory conveyor machine.

A method, comprising:

• acquiring raw measurement data of a vibratory conveyor machine with at least one acceleration, velocity or displacement sensor at at least two points in time with different loading states,
• processing the raw measurement data into at least one vibration data feature from a list:
• Amplitude, frequency, phase,
• creating and storing feature data sets consisting of including at least one vibration data feature,
• creating a regression model using feature datasets, wherein the regression model is validated using feature data sets that have not been used for training the regression model,
• determining and displaying a current actual load of the vibratory conveyor machine based on the created regression model and at least one current feature data set, and
• dynamically controlling at least one of a conveyor capacity, discharge of a bulk material, and one or more vibratory parameters of the vibratory conveyor machine based on the current actual load.