Systems and methods for monitoring and adjusting operation of a mover system

1 . A non-transitory computer-readable medium comprising instructions that, when executed by processing circuitry, are configured to cause the processing circuitry to perform operations comprising:

receiving a first dataset associated with a mover system comprising a track and a plurality of mover assemblies, wherein each mover assembly comprises one or more magnets configured to cause the mover assembly to independently move along the track based on an electromagnetic force provided via one of more coils integrated within the track, wherein the first dataset is associated with calibrating the mover system for a plurality of operating modes, wherein the mover system is calibrated for each of the plurality of operating modes with respect to different types of objects to be transported on the plurality of mover assemblies, different movement directions of the plurality of mover assemblies, and a level of energy output of the mover system;

identifying a baseline operating state for each operating mode of the plurality of operating modes based on one or more parameters included in the first dataset;

receiving a second dataset associated with the mover system after receiving the first dataset, wherein the second dataset corresponds to the mover system operating in at least two different operating modes;

identifying a plurality of features within the second dataset, wherein the plurality of features is associated with the mover system operating in a first operating mode and a second operating mode, wherein the first operating mode is different from the second operating mode;

identifying a portion of the plurality of features that correspond to the mover system operating in the first operating mode or the second operating mode;

determining that the mover system is operating according to the first operating mode based on the portion of the plurality of features;

identifying an anomaly signature dataset of a plurality of anomaly signature datasets that is present in the second dataset, wherein the plurality of anomaly signature datasets is identified based on a machine learning model trained using the first dataset and a plurality of anomaly states associated with the first dataset, and wherein each anomaly signature dataset of the plurality of anomaly signature datasets corresponds to an operating mode of the plurality of operating modes;

determining that the second dataset is indicative of an anomaly state of the mover system based on the anomaly signature dataset being present in the second dataset and an indication that the mover system is operating according to the first operating mode;

determining an operational adjustment for the mover system based on the anomaly state, wherein each of the plurality of anomaly signature datasets is associated with a respective operational adjustment of a plurality of operational adjustments for the mover system; and

adjusting an operation of the mover system based on the operational adjustment associated with the anomaly signature dataset, wherein the operation is adjusted to cause at least one mover assembly of the plurality of movers assemblies to change a direction, a velocity, or an acceleration by selectively energizing the one or more coils integrated within the track, wherein each mover assembly of the plurality of mover assemblies is configured to move based on the one or more coils of the track being energized.

2 . The non-transitory computer-readable medium of claim 1 , wherein the instructions corresponding to determining that the second dataset is indicative of the anomaly state comprises additional instructions configured to cause the processing circuitry to:

determine that a positioning of a mover assembly deviated from a target positioning; and

adjusting the operation of the mover system to adjust movement of the mover assembly in response to determining the positioning of the mover assembly deviated from the target positioning.

3 . The non-transitory computer-readable medium of claim 1 , wherein the first dataset is received via at least one sensor while operating the mover system in a calibration mode.

4 . The non-transitory computer-readable medium of claim 1 , wherein the instructions, when executed by the processing circuitry, are configured to cause the processing circuitry to perform operations comprising:

receiving a third dataset during the operation of the mover system, wherein the third dataset comprises a set of differences from the first dataset;

determining that the third dataset does not correspond to the anomaly signature dataset; and

suspending operation of the mover system in response to determining that the third dataset comprises the set of differences from the first dataset and that the third dataset does not correspond to any anomaly signature datasets of the plurality of anomaly signature datasets.

5 . The non-transitory computer-readable medium of claim 1 , wherein the instructions, when executed by the processing circuitry, are configured to cause the processing circuitry to perform the operations comprising:

receiving a third dataset during operation of the mover system;

determining a set of differences between the third dataset and the first dataset is less than a set of thresholds; and

continuing the operation of the mover system in response to determining the set of differences between the third dataset and the first dataset is less than the set of thresholds.

6 . The non-transitory computer-readable medium of claim 1 , wherein the instructions, when executed by the processing circuitry, are configured to cause the processing circuitry to perform the operations comprising:

determining that the mover system is operating in a new operating state based on the second dataset comprising a set of differences from the first dataset;

suspending the operation of the mover system based on the new operating state; and

receiving an indication classifying the new operating state as an anomaly signature dataset.

7 . The non-transitory computer-readable medium of claim 1 , wherein the first dataset comprises an electrical current associated with each of the plurality of mover assemblies, a temperature of each of the plurality of mover assemblies, a power consumption of each of the plurality of mover assemblies, or any combination thereof.

8 . A method, comprising:

receiving, via processing circuitry, an anomaly state model for a mover system comprising a track and a plurality of mover assemblies movable along the track, wherein each mover assembly comprises one or more magnets configured to cause the mover assembly to independently move along the track based on an electromagnetic force provided via one of more coils integrated within the track, wherein the anomaly state model comprises a plurality of signatures, and each signature of the plurality of signatures is indicative of the mover system operating in an operating mode of a plurality of operating modes, an anomaly state of the mover system, and an operational adjustment for the mover system, wherein the mover system is calibrated for each of the plurality of operating modes with respect to different types of objects to be transported on the plurality of mover assemblies, different movement directions of the plurality of mover assemblies, and a level of energy output of the mover system, wherein the anomaly state model is generated based on a sensor dataset associated with calibrating the plurality of mover assemblies to operate in the plurality of operating modes and a machine learning model trained to identify each of the plurality of signatures based on one or more patterns within the sensor dataset, and wherein each of the plurality of signatures is associated with a respective operational adjustment of a plurality of operational adjustments for the mover system;

identifying, via the processing circuitry, a baseline operating state for each operating mode of the plurality of operating modes based on one or more parameters included in the sensor dataset;

receiving, via the processing circuitry, an additional sensor dataset during operation of the mover system, wherein the additional sensor dataset is acquired after the sensor dataset, wherein the additional sensor dataset corresponds to the mover system operating in at least two different operating modes;

identifying a plurality of features within the additional sensor dataset, wherein the plurality of features is associated with the mover system operating in a first operating mode and a second operating mode, wherein the first operating mode is different from the second operating mode;

identifying a portion of the plurality of features that correspond to the mover system operating in the first operating mode or the second operating mode;

determining that the mover system is operating according to the first operating mode based on the portion of the plurality of features;

determining, via the processing circuitry, the additional sensor dataset corresponds to a signature of the plurality of signatures of the anomaly state model;

determining, via the processing circuitry, an adjustment operation of the mover system based on the signature and an indication that the mover system is operating according to the first operating mode; and

adjusting, via the processing circuitry, the operation of the mover system by causing at least one mover assembly of the plurality of mover assemblies to perform a change in a direction, a velocity, or an acceleration by selectively energizing one or more coils within the track, wherein each mover assembly of the plurality of mover assemblies is configured to move based on the one or more coils of the track being energized in accordance with the adjustment operation.

9 . The method of claim 8 , comprising receiving, via the processing circuitry, a normal operation dataset associated with the mover system, wherein a subset of the normal operation dataset is associated with a normal operating state of the mover system, and each signature of the plurality of signatures of the anomaly state model is associated with a respective anomaly dataset comprising a set of differences from the subset of the normal operation dataset.

10 . The method of claim 9 , comprising:

determining, via the processing circuitry, a first set of differences between the additional sensor dataset and the normal operation dataset;

determining, via the processing circuitry, the first set of differences does not correspond to the plurality of signatures; and

suspending, via the processing circuitry, the operation of the mover system in response to determining the first set of differences does not correspond to the plurality of signatures.

11 . The method of claim 10 , comprising:

receiving, via the processing circuitry, feedback indicative of an additional signature associated with the additional sensor dataset; and

adjusting, via the processing circuitry, the anomaly state model to include the additional signature upon receiving the feedback.

12 . The method of claim 11 , comprising:

receiving, via the processing circuitry, a subsequent sensor dataset during the operation of the mover system;

determining, via the processing circuitry, the subsequent sensor data corresponds to the additional signature;

determining, via the processing circuitry, an additional adjustment operation of the mover system based on the additional signature; and

adjusting, via the processing circuitry, the operation of the mover system in accordance with the additional adjustment operation.

13 . The method of claim 8 , comprising:

receiving, via the processing circuitry, information associating a new sensor dataset with a new signature indicative of a new anomaly state of the mover system; and

updating, via the processing circuitry, the anomaly state model to include the new signature in response to receiving the information.

14 . The method of claim 8 , wherein the sensor dataset is received from one or more sensors coupled to the track and configured to record vibrations caused by a movement of the plurality of mover assemblies.

15 . A mover system, comprising:

a track comprising a plurality of coils;

a plurality of mover assemblies, wherein each mover assembly of the plurality of mover assemblies comprises one or more magnets configured to cause the mover assembly to independently move along the track based on an electronic magnetic force provided by magnetically engaging with a coil of the plurality of coils of the track; and

control circuitry configured to perform operations comprising:

receiving a first dataset associated with calibrating the plurality of mover assemblies to move along the track in a plurality of operating modes, wherein the mover system is calibrated for each of the plurality of operating modes with respect to different types of objects to be transported on the plurality of mover assemblies, different movement directions of the plurality of mover assemblies, and a level of energy output of the mover system;

identifying a baseline operating state for each operating mode of the plurality of operating modes based on one or more parameters included in the first dataset;

receiving a second dataset during operation of the mover system, wherein the second dataset is received after the first dataset, and wherein the second dataset corresponds to the mover system operating in at least two different operating modes;

identifying a plurality of features within the second dataset, wherein the plurality of features is associated with the mover system operating in a first operating mode and a second operating mode, wherein the first operating mode is different from the second operating mode;

identifying a portion of the plurality of features that correspond to the mover system operating in the first operating mode or the second operating mode;

determining that the mover system is operating according to the first operating mode based on the portion of the plurality of features;

identifying an anomaly signature dataset of a plurality of anomaly signature datasets that is present in the second dataset, wherein the plurality of anomaly signature datasets is identified based on a machine learning model trained using the first dataset and a plurality of anomaly states associated with the first dataset, and wherein each anomaly signature dataset of the plurality of anomaly signature datasets corresponds to an operating mode of the plurality of operating modes;

determining that the second dataset corresponds to an anomaly state of the mover system based on the anomaly signature dataset being present in the second dataset;

determining an operational adjustment for the mover system based on the anomaly signature dataset and an indication that the mover system is operating according to the first operating mode; and

adjusting an operation of the mover system based on the operational adjustment associated with the anomaly signature dataset, wherein the operation is adjusted to cause at least one mover assembly of the plurality of movers assemblies to change a direction, a velocity, or an acceleration by selectively energizing at least one of the plurality of coils to drive movement of at least one of the plurality of mover assemblies along the track.

16 . The mover system of claim 15 , wherein the control circuitry is configured to perform operations comprising:

receiving a third dataset;

establishing the anomaly signature dataset based on the third dataset; and

determining the second dataset corresponds to the anomaly signature dataset in response to determining a set of differences between the second dataset and the third dataset is less than a threshold.

17 . The mover system of claim 15 , wherein the control circuitry is configured to perform operations comprising:

determining a respective condition of the track, of each mover assembly of the plurality of mover assemblies, or both based on the second dataset;

presenting a plurality of visual indicators, wherein each visual indicator of the plurality of visual indicators corresponds to a section of the track or to a mover assembly of the plurality of mover assemblies; and

adjust an appearance of each visual indicator of the plurality of visual indicators based on the respective condition of a corresponding section of the track or a corresponding mover assembly.

18 . The mover system of claim 17 , wherein the control circuitry is configured to perform operations comprising:

receiving a user input indicative of a selection of a visual indicator of the plurality of visual indicators; and

presenting additional information associated with the section of the track or associated with the mover assembly corresponding to the visual indicator in response to receiving the user input.

19 . The mover system of claim 15 , wherein the anomaly state corresponding to the anomaly signature dataset is associated with a positioning of a mover assembly deviating from a target positioning, and the control circuitry is configured to selectively energize a particular coil of the plurality of coils of the track to adjust movement of the mover assembly in response to determining the anomaly state is associated with the positioning of the mover assembly deviating from the target positioning.

20 . The mover system of claim 15 , wherein calibrating the plurality of mover assemblies to move along the track comprises moving the plurality of mover assemblies upon the track for a threshold number of cycles.