METHOD FOR CALCULATING BULK MATERIAL FEED RATES OR BULK MATERIAL LOADS OF A VIBRATORY MACHINE

Abstract

In a method for calculating a bulk material conveying rate or a bulk material load of a vibratory conveyor machine, in which method raw measured data from the vibratory conveyor machine are acquired at at least two times with different load states by at least one acceleration, speed or travel sensor and raw measured data are then processed to give at least one vibration data feature from the list: amplitude, frequency and phase, provision is made to create and to store feature datasets consisting of at least one vibration data feature and to create a regression model on the basis thereof. Based on the created regression model and at least one current feature dataset, the current actual load or bulk material conveying rate of a vibratory conveyor machine is then ascertained and displayed.

Claims

1. A method for calculating the bulk material feed rate or the bulk material loading of a vibratory conveyor machine, comprising: a) acquiring raw measurement data of the vibratory conveyor machine with at least one acceleration, velocity or displacement sensor at at least two points in time with different loading states, b) processing the raw measurement data into at least one vibration data feature from the list: Amplitude, frequency, phase, characterized by the following steps: c) creating and storing feature data sets consisting of at least one vibration data feature, d) creating a regression model using the stored feature datasets, and e) 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.

2. The method according to claim 1, wherein the step d) of creating the regression model is repeated after a period of time Δt, after the occurrence of wear on the vibratory conveyor machine, after maintenance measures and/or after other system changes, such as change of the loading, the machine components, the drive properties or of material properties.

3. The method according to claim 2, wherein the step a) of acquiring acquisition of the raw measurement data is carried out at least at 0% bulk material loading and 100% nominal load bulk material loading.

4. The method according to claim 1, wherein, for training of the regression model, the model-based bulk material loading values are matched with a reference signal or reference load signal of the bulk material conveyor quantity or bulk material loading.

5. The method according to claim 4, wherein the reference load signal or reference signal is a force measurement signal or a motor current signal resulting from an upstream, alternative or indirect measurement process of the bulk material feed rate or bulk material load.

6. The method according to claim 5, wherein for training of the regression model only forecasting variables are used whose model-based bulk material loading value has a high correlation to the reference load signal of the bulk material conveyor quantity or bulk material loading.

7. The method according to claim 1, wherein a multivariate regression method is used in the training phase of the regression model.

8. The method according to claim 7, wherein the regression model is created in the form C1*X1+C2*X2+C3*X2{circumflex over ( )}2 . . . CN*Xn{circumflex over ( )}n=bulk loading, wherein the forecasting variables X are considered as linear or nonlinear factors and/or by using coefficients C1, C2, . . . ;CN

9. The method according to claim 1, wherein the regression model is validated using feature data sets that have not been used for training the regression model.

10. A device for the 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 the stored feature datasets, and a screen or a display showing a model-based bulk material load value or model-based bulk material feed rate of the vibratory conveyor machine.

11. The method according to claim 1, wherein the step a) of acquiring raw measurement data is carried out at least at 0% bulk material loading and 100% nominal load bulk material loading.

12. The method according to claim 4, wherein for training of the regression model only forecasting variables are used whose model-based bulk material loading value has a high correlation to the reference load signal of the bulk material conveyor quantity or bulk material loading.

Description

[0034] The process according to the invention is explained in more detail below by means of a process diagram, and further features and advantages of the invention are disclosed.

[0035] FIG. 1 shows a schematic representation of the operations of the process according to the invention.

[0036] FIG. 1 schematically shows the method according to the invention for calculating the bulk material feed rate of a vibratory machine 1 in the form of a vibratory screen. At least one sensor 12 is attached to the vibratory machine 1, which is in data connection with a computing unit of an evaluation device 2. The data connection, which is shown dashed in the figure, can be made via a radio connection or wired connection, via a permanent or temporary connection. The measurement data supplied by the sensor 12 are processed and stored in the computing unit to form feature data sets 13. A regression model 6 is formed from the feature data sets 13, which serve as input variables, and reference signals 7, which originate from an upstream or separate measurement process of the bulk material load. The regression model 6 based on the feature data sets 13 is validated and trained with feature data sets 9 that did not serve to create the model.

[0037] The validated regression model 8 is then transferred to a software 10 and transferred to the evaluation device 2 to display the calculation of the bulk material load.