Disclosed herein are systems, devices, and methods related to assets and predictive models and corresponding workflows that are related to the operation of assets. In particular, examples involve assets configured to receive and locally execute predictive models, locally individualize predictive models, and/or locally execute workflows or portions thereof.
Please help me write a proper abstract based on the patent claims. CLAIM: 1. A local analytics device configured to monitor operating conditions of an asset, the local analytics device comprising: an asset interface configured to couple the local analytics device to the asset; a network interface configured to facilitate communication between the local analytics device and a computing system (i) configured to monitor operating conditions of the asset and (ii) located remote from the local analytics device; at least one processor; a non-transitory computer-readable medium; and program instructions stored on the non-transitory computer-readable medium that are executable by the at least one processor to cause the local analytics device to: identify a predictive model that is related to the operation of the asset that is to be executed; based on one or more execution functions corresponding to the identified predictive model, determine whether the local analytics device should execute the predictive model; if the local analytics device should execute the predictive model, execute the predictive model based on operating data for the asset received via the asset interface; and otherwise, transmit to the computing system, via the network interface, (i) an instruction for the computing system to execute the predictive model, and (ii) operating data for the asset received via the asset interface. 2. The local analytics device of claim 1, wherein the identified predictive model is a first predictive model, and wherein the program instructions stored on the non-transitory computer-readable medium are further executable by the at least one processor to cause the local analytics device to: before identifying the first predictive model, receiving from the computing system, via the network interface, a plurality of predictive models comprising the first predictive model, wherein each of the plurality of predictive models corresponds to a likelihood that a particular failure will occur at the asset within a given period of time in the future. 3. The local analytics device of claim 1, wherein the one or more execution functions comprise a first performance score function for the local analytics device and a second performance score function for the computing system, and wherein determining whether the local analytics device should execute the predictive model is based on a first performance score for the local analytics device and a second performance score for the computing system. 4. The local analytics device of claim 3, wherein the first performance score comprises a first expected value associated with the local analytics device executing the predictive model, and wherein the second performance score comprises a second expected value associated with the computing system executing the predictive model. 5. The local analytics device of claim 3, wherein determining whether the local analytics device should execute the predictive model comprises: executing (i) the first performance score function to determine the first performance score, and (ii) the second performance score function to determine the second performance score; and comparing the first performance score and the second performance score. 6. The local analytics device of claim 5, wherein determining whether the local analytics device should execute the predictive model comprises: based on the comparison, determining that the predictive model should be executed by the local analytics device when the first performance score is greater than or equal to the second performance score. 7. The local analytics device of claim 5, wherein determining whether the local analytics device should execute the predictive model comprises: based on the comparison, determining that the predictive model should be executed by the local analytics device when the first performance score is greater than the second performance score by more than a threshold amount. 8. The local analytics device of claim 5, wherein the program instructions stored on the non-transitory computer-readable medium are further executable by the at least one processor to cause the local analytics device to: before determining whether the local analytics device should execute the predictive model, receive from the computing system, via the network interface, the first and second performance score functions, wherein the first and second performance score functions correspond to the identified predictive model and are defined by the computing system. 9. The local analytics device of claim 1, wherein the operating data for the asset received via the asset interface comprises signal data from (i) at least one sensor of the asset, (ii) at least one actuator of the asset, or (iii) at least one sensor of the asset and at least one actuator of the asset. 10. The local analytics device of claim 1, wherein the program instructions stored on the non-transitory computer-readable medium are further executable by the at least one processor to cause the local analytics device to: before determining whether the local analytics device should execute the predictive model, modify the one or more execution functions based on one or more dynamic factors. 11. The local analytics device of claim 10, wherein the local analytics device and the computing system are communicatively coupled via a wide-area network, and wherein the one or more dynamic factors comprise at least one of (i) an amount of time since an update to the one or more execution functions, (ii) an amount of time since an update to the predictive model, or (iii) one or more network conditions of the wide-area network. 12. A non-transitory computer-readable medium having instructions stored thereon that are executable to cause a local analytics device coupled to an asset, via an asset interface of the local analytics device, to: identify a predictive model that is related to the operation of the asset that is to be executed; based on one or more execution functions corresponding to the identified predictive model, determine whether the local analytics device should execute the predictive model; if the local analytics device should execute the predictive model, execute the predictive model based on operating data for the asset received via the asset interface; and otherwise, transmit to a computing system that is configured to monitor operating conditions of the asset and wirelessly communicatively coupled to the local analytics device (i) an instruction for the computing system to execute the predictive model, and (ii) operating data for the asset received via the asset interface. 13. The non-transitory computer-readable medium of claim 12, wherein determining whether the local analytics device should execute the predictive model comprises: executing (i) a first performance score function to determine a first performance score for the local analytics device, and (ii) a second performance score function to determine a second performance score for the computing system; and comparing the first performance score and the second performance score. 14. The non-transitory computer-readable medium of claim 13, wherein determining whether the local analytics device should execute the predictive model comprises: based on the comparison, determining that the predictive model should be executed by the local analytics device when the first performance score is greater than or equal to the second performance score. 15. The non-transitory computer-readable medium of claim 13, wherein determining whether the local analytics device should execute the predictive model comprises: based on the comparison, determining that the predictive model should be executed by the local analytics device when the first performance score is greater than the second performance score by more than a threshold amount. 16. The non-transitory computer-readable medium of claim 12, wherein the instructions are further executable to cause the local analytics device to: before determining whether the local analytics device should execute the predictive model, modify the one or more execution functions based on one or more dynamic factors. 17. The non-transitory computer-readable medium of claim 16, wherein the local analytics device and the computing system are wirelessly communicatively coupled via a wide-area network, and wherein the one or more dynamic factors comprise at least one of (i) an amount of time since an update to the one or more execution functions, (ii) an amount of time since an update to the predictive model, or (iii) one or more network conditions of the wide-area network. 18. A method for facilitating optimal execution of a predictive model related to the operation of an asset coupled to a local analytics device, the method comprising: identifying, by the local analytics device, the predictive model that is to be executed; based on one or more execution functions corresponding to the identified predictive model, determining whether the local analytics device should execute the predictive model; if the local analytics device should execute the predictive model, executing, by the local analytics device, the predictive model based on operating data for the asset received via an asset interface of the local analytics device; and otherwise, transmit, by the local analytics device to a computing system configured to monitor operating conditions of the asset located remote from the local analytics device, via a network interface of the local analytics device, (i) an instruction for the computing system to execute the predictive model, and (ii) operating data for the asset received via the asset interface. 19. The method of claim 18, further comprising: before determining whether the local analytics device should execute the predictive model, modifying the one or more execution functions based on one or more dynamic factors. 20. The method of claim 18, wherein determining whether the local analytics device should execute the predictive model comprises: executing (i) a first performance score function to determine a first performance score for the local analytics device, and (ii) a second performance score function to determine a second performance score for the computing system; and comparing the first performance score and the second performance score.