Checkweigher performance learning systems and related methods

1 . A checkweigher performance learning system, said system comprising:

checkweighers, each comprising at least: a conveyor, a load cell, and a controller configured to:

generate noise data through a fast Fourier transform (FFT) trace of the load cell during operations of the checkweigher, wherein the FFT trace data comprises belt noise and pulley noise; and

generate accuracy data through validation operations for the load cell; and

one or more servers in electronic communication with the checkweighers and comprising historical operations data associated with known fault conditions and software instructions, which when executed, configure the one or more servers to:

receive configuration data for the checkweighers, said configuration data indicating pulley diameter, belt length, object type, belt speed, and frame configuration;

receive operations data from the checkweighers including the noise data and the accuracy data;

classify the noise data by component based on frequency of the noise data, wherein the components include at least a belt, a pulley, and a motor for each of the checkweighers;

correlate the accuracy data with magnitude values of the noise data on a component-specific basis to determine a magnitude threshold for each component providing acceptably accurate readings;

for each of the magnitude thresholds exceeded for a respective component of a respective checkweigher, classify the noise data for the respective component by comparison to known, fault conditions for the respective component to generate a predicted fault condition, including by matching the noise data associated with the exceeded magnitude threshold to a known fault condition in the historical operations data having a frequency and magnitude within a predetermined margin of the noise data associated with the exceeded magnitude threshold;

generate an alert comprising the predicted fault condition;

receive feedback regarding actual diagnosed conditions for the respective checkweigher; and

update the predetermined margin based on the feedback using one or more machine learning techniques.

2 . The system of claim 1 wherein:

the one or more servers are configured to determine the magnitude threshold for each component by performing a linear regression analysis of the noise data on a component-specific basis.

3 . The system of claim 2 wherein:

the one or more servers are configured to receive user input regarding a proposed checkweigher configuration and generate a predicted accuracy for the proposed checkweigher configuration.

4 . The system of claim 3 wherein:

the one or more servers are configured to generate the predicted accuracy for the proposed checkweigher configuration by:

performing a linear regression analysis of the correlated data; and

fitting the proposed checkweigher configuration to the linear regression analysis to provide the predicted accuracy.

5 . The system of claim 4 wherein:

the one or more servers are configured to generate an alert where accuracy data of one of the checkweighers having the proposed checkweigher configuration is below the predicted accuracy level by a predetermined margin.

6 . The system of claim 1 further comprising:

user devices in electronic communication with the one or more servers, wherein the one or more servers are configured to publish an electronically sharable and editable table at the user devices on an on-demand basis, said table comprising the configuration data and the operations data for the checkweighers.

7 . The system of claim 6 wherein:

the one or more servers are configured to:

receive additional configuration data and operations data for the checkweighers from the user devices; and

update the table with the additional configuration data and operations data.

8 . The system of claim 7 wherein:

the one or more servers are configured to:

receive known fault condition information for the checkweighers from the user devices;

update the table with the known fault condition information in association with at least certain of the configuration data and operations data; and

establish magnitude classifications for the known fault conditions.

9 . A method for predicting checkweigher accuracy, said method comprising:

electrically receiving configuration data for checkweighers, said configuration data indicating pulley diameter, belt length, object type, belt speed, and frame configuration;

performing a fast Fourier transform (FFT) trace of load cells of the checkweighers during operations comprising normal operations and accuracy validation operations to develop noise data and accuracy data for the checkweighers, wherein the FFT trace data comprises belt noise and pulley noise;

electronically classifying the noise data by component based on frequency; and

correlating the accuracy data with magnitudes of the noise data to determine threshold magnitudes on a component-specific basis reflecting acceptably accurate readings;

for each of the magnitude thresholds exceeded for a respective component of a respective checkweigher, electronically classifying the noise data for the respective component by comparison to known, fault conditions for the respective component to generate a predicted fault condition, including by matching the noise data associated with the exceeded magnitude threshold to a known fault condition in the historical operations data having a frequency and magnitude within a predetermined margin of the noise data associated with the exceeded magnitude threshold, wherein said components including at least a belt, a pulley, and a motor for each of the checkweighers;

generating an alert comprising the predicted fault condition;

receiving feedback regarding actual diagnosed conditions for the respective checkweigher; and

updating the predetermined margin based on the feedback using one or more machine learning techniques.

10 . The method of claim 9 further comprising:

electronically receiving further noise data;

electronically classifying the further noise data by component based on frequency; and

generating an alert where the further noise data includes a magnitude above the threshold for any of the components.

11 . The method of claim 9 wherein:

the thresholds are determined by electronically performing a linear regression analysis of the noise data for each component.

12 . The method of claim 9 further comprising:

performing a linear regression analysis of the correlated accuracy and configuration data;

receiving user input for a proposed checkweigher configuration; and

generating a predicted accuracy for the proposed checkweigher configuration based on the linear regression analysis.

13 . The method of claim 12 further comprising:

receiving accuracy data from an additional checkweigher having the proposed checkweigher configuration;

determining that the accuracy data from the additional checkweigher data is not within a predetermined margin of the predicted accuracy; and

generating an alert indicating that the additional checkweigher is not performing with an expected level of accuracy.

14 . The method of claim 9 further comprising:

receiving indicators of known fault conditions for at least some of the noise data;

receiving additional checkweigher noise data;

electronically classifying the additional checkweigher noise data by component based on frequency;

determining that the additional checkweigher noise data for any of the components is within a margin of the noise data associated with at least one of the known fault conditions for a given component; and

generating an alert indicating the at least one of the known fault conditions is a likely fault condition for the additional checkweigher.

15 . The method of claim 9 further comprising:

electronically publishing the accuracy data and the noise data in an editable table, wherein the configuration data is provided by manufacturers after creating the checkweighers, and the noise data is provided by a respective customer during use of checkweighers.

16 . A checkweigher performance learning system, said system comprising:

checkweighers, each comprising at least: a conveyor, a load cell, a controller configured to perform a fast Fourier transform (FFT) trace of the load cell to derive noise data and perform a validation of the load cell to derive accuracy data, wherein the FFT trace data comprises belt noise and pulley noise;

user devices; and

one or more servers in electronic communication with the controller each of the checkweighers and the user devices, said one or more servers comprising historical operations data associated with known fault conditions and software instructions, which when executed, configure the one or more servers to:

receive configuration data for the checkweighers from the user devices, said configuration data indicating pulley diameter, belt length, object type, belt speed, and frame configuration;

receive the noise data and the accuracy data from the controllers of the checkweighers;

classify the noise data by component based on frequency of the noise data, wherein the components include at least a belt, a pulley, and a motor for each of the checkweighers;

correlate the accuracy data with magnitudes of the noise data for each component;

publish the correlated data to an electronically sharable and editable spreadsheet;

receive known fault condition information from the user devices and update associated entries of the correlated data with the known fault information;

perform a linear regression analysis of the noise data for each component to develop a magnitude threshold for each component differentiating between the magnitudes associated with the accuracy data above a predetermined acceptable accuracy threshold and the magnitudes associated with the accuracy data below the predetermined acceptable accuracy threshold;

receive additional noise data; and:

classify the additional noise data by component based on frequency;

correlate the accuracy data with magnitude values of the noise data on a component-specific basis to determine a magnitude threshold for each component providing acceptably accurate readings:

for each of the magnitude thresholds exceeded for a respective component of a respective checkweigher, classify the noise data for the respective component by comparison to known, fault conditions for the respective component to generate a predicted fault condition, including by matching the noise data associated with the exceeded magnitude threshold to a known fault condition in the historical operations data having a frequency and magnitude within a predetermined margin of the noise data associated with the exceeded magnitude threshold:

generate an alert if the additional noise data incudes a magnitude above the magnitude threshold for any of the components; and

include, in the alert, an indicator of a likely fault where the magnitude of the additional noise data for a given component is within a predetermined margin of a magnitude of the correlated data associated with one of the known fault conditions for the given component;

receive feedback regarding actual diagnosed conditions for the respective checkweigher; and

update the predetermined margin based on the feedback using one or more machine learning techniques.