EP-4735990-A1 - PRINTER MANAGEMENT USING CLUSTERED COMPONENT MODELS

Abstract

A method includes: receiving sensor data obtained by sensors in different printers, wherein the sensor data includes measured values for different physical parameters associated with physical components of the different printers; grouping the different printers into respective clusters based on similarities among the measured values for a proper subset of the different physical parameters; and for at least one cluster of the respective clusters, based on the sensor data of the different printers grouped into the cluster, determining models characterizing failure likelihoods of at least two of the physical components, which are included in a first printer assigned to the at least one cluster, based on at least one of the models, predicting a failure event for a first physical component of the first printer, and outputting information indicating the failure event to trigger servicing of the first physical component of the first printer.

Inventors

• DEV, Bodhayan
• KAMBLE, ATISH P.
• AHMED, Walid Ben
• SWAROOP, Prem

Assignees

• Dover Europe Sàrl

Dates

Publication Date

20260506

Application Date

20240814

Claims (20)

1. 1. A system comprising: one or more processors; and one or more computer-readable mediums encoding instructions that, when executed by the one or more processors, cause the one or more processors to receive sensor data obtained by sensors in different printers, wherein the sensor data comprises measured values for different physical parameters associated with physical components of the different printers, group the different printers into respective clusters based on similarities among the measured values for a proper subset of the different physical parameters, and for at least one cluster of the respective clusters, based on the sensor data of the different printers grouped into the cluster, determine models characterizing failure likelihoods of at least two of the physical components, which are included in a first printer assigned to the at least one cluster, based on at least one of the models, predict a failure event for a first physical component of the first printer, and output information indicating the failure event to trigger servicing of the first physical component of the first printer.
2. 2. The system of claim 1, wherein the physical components comprise one or more of a print head, a motor, a vacuum module, a filter, or a laser controller.
3. 3. The system of claim 1, wherein the instructions, when executed by the one or more processors, cause the one or more processors to, for each cluster of the at least one cluster: determine a joint model characterizing an overall failure likelihood of the first printer, based on the models characterizing the failure likelihoods of the at least two of the physical components of the first printer.
4. 4. The system of claim 1, wherein the different physical parameters comprise one or more of temperature data, inkjet speed data, motor speed data, viscosity data, or pressure data.
5. 5. The system of claim 1, wherein a second printer and a third printer are co-located at a production site and are assigned to respective clusters, and wherein the instructions, when executed by the one or more processors, cause the one or more processors to based on the determined models of the respective clusters to which each of the second printer and the third printer is assigned, determine that a reliability of the second printer is higher than a reliability of the third printer, and based on determining that the reliability of the second printer is higher than the reliability of the third printer, route a print job to the second printer.
6. 6. The system of claim 1, wherein the instructions, when executed by the one or more processors, cause the one or more processors to, for the at least one cluster of the respective clusters provide a user interface displaying pairwise correlations among the different physical parameters, and subsequent to providing the user interface, receive a user input indicative of the proper subset of the different physical parameters.
7. 7. The system of claim 1, wherein grouping the different printers is based on at least one of: usage data indicating amounts of usage of the different printers, or consumption data indicating consumables consumption amounts of the different printers.
8. 8. The system of claim 1, wherein grouping the different printers comprises: determining, for each printer of the different printers, a product of the measured values for the proper subset of the different physical parameters, and grouping the different printers based on the products corresponding to the different printers.
9. 9. The system of claim 1, wherein each printer of the different printers is associated with failure data for the printer, and wherein, for each cluster of the at least one cluster, determining the models characterizing the failure likelihoods is based on the failure data for printers grouped into the cluster.
10. 10. The system of claim 9, wherein, for each cluster of the at least one cluster, determining models characterizing the failure likelihoods comprises training machine learning models using, as input training data, the sensor data of the different printers grouped into the cluster, wherein the failure data is used as a label for the sensor data of the different printers grouped into the cluster, and wherein the machine learning models are trained to output the failure likelihoods.
11. 11. The system of claim 1, wherein the instructions, when executed by the one or more processors, cause the one or more processors to based on the determined models of a cluster to which a second printer is assigned, adjust a setting of the second printer to increase a predicted availability of the second printer.
12. 12. The system of claim 1, wherein the instructions, when executed by the one or more processors, cause the one or more processors to apply a physics-based model to generate simulated failure data associated with accelerated environmental conditions of a simulated printer of the different printers, wherein, for each cluster of the at least one cluster, the simulated printer is grouped into the cluster, and determining the models characterizing the failure likelihoods is based on the simulated failure data.
13. 13. The system of claim 12, wherein the physics-based model comprises an Arrhenius model or a Basquin model.
14. 14. The system of claim 1, wherein the instructions, when executed by the one or more processors, cause the one or more processors to receive a user input of a target objective, and wherein, for each cluster of the at least one cluster, outputting information indicating the failure event comprises generating a maintenance plan based on the models, subject to the target objective.
15. 15. The system of claim 14, wherein the target objective comprises at least one of a cost objective, a printer availability objective, or a time-between-failures objective.
16. 16. The system of claim 14, wherein receiving the user input of the target objective comprises providing a user interface operable to select whether the target objective is a uni -variate objective or a multi -variate objective.
17. 17. The system of claim 14, wherein the maintenance plan comprises a customer maintenance plan based on the sensor data of one or more printers, of the different printers, that are associated with a customer.
18. 18. The system of claim 1, wherein, for each cluster of the at least one cluster, predicting the failure event comprises determining a mean time to failure for the first printer.
19. 19. The system of claim 1, wherein the instructions, when executed by the one or more processors, cause the one or more processors to generate simulated sensor data included in the sensor data using a machine learning model, wherein the machine learning model is trained using as input training data, non-sensor data characterizing the different printers, and as labels for the non-sensor data, the measured values for the different physical parameters, and wherein the machine learning model is trained to receive, as input, non-sensor data characterizing one or more printers of the different printers, and to determine, as output, simulated sensor data for the one or more printers of the different printers.
20. 20. The system of claim 19, wherein the non-sensor data comprise at least one of printer age, printer region, or printer industrial sector.

Description

PRINTER MANAGEMENT USING CLUSTERED COMPONENT MODELS CROSS-REFERENCE TO RELATED APPLICATION [001] This application claims the benefit of priority to U.S. Provisional Patent Application No. 63/532,619, filed on August 14, 2023, the entire contents of which are incorporated herein by reference. FIELD OF THE DISCLOSURE [002] Technologies are described for managing servicing and operation of printers, such as inkjet, laser, and thermal transfer overprinting (TTO) printers. BACKGROUND [003] Printers include multiple components that can fail and thus interrupt printer operation. Based on an estimated mean time between failures (MTBF) for a printer, the printer can be proactively serviced to avoid breakdowns. SUMMARY [004] Some aspects of this disclosure describe a system. The system includes one or more processors; and one or more computer-readable mediums encoding instructions that, when executed by the one or more processors, cause the one or more processors to perform operations. The operations include receiving sensor data obtained by sensors in different printers. The sensor data includes measured values for different physical parameters associated with physical components of the different printers. The operations include grouping the different printers into respective clusters based on similarities among the measured values for a proper subset of the different physical parameters. The operations include, for at least one cluster of the respective clusters, based on the sensor data of the different printers grouped into the cluster, determining models characterizing failure likelihoods of at least two of the physical components, which are included in a first printer assigned to the at least one cluster; based on at least one of the models, predicting a failure event for a first physical component of the first printer; and outputting information indicating the failure event to trigger servicing of the first physical component of the first printer. [005] This and other systems described herein can have one or more of at least the following characteristics. [006] In some implementations, the physical components include one or more of a print head, a motor, a vacuum module, a filter, or a laser controller. [007] In some implementations, the operations include determining a joint model characterizing an overall failure likelihood of the first printer, based on the models characterizing the failure likelihoods of the at least two of the physical components of the first printer. [008] In some implementations, the different physical parameters include one or more of temperature data, inkjet speed data, motor speed data, viscosity data, or pressure data. [009] In some implementations, a second printer and a third printer are co-located at a production site and are assigned to respective clusters, and the operations include: based on the determined models of the respective clusters to which each of the second printer and the third printer is assigned, determining that a reliability of the second printer is higher than a reliability of the third printer; and based on determining that the reliability of the second printer is higher than the reliability of the third printer, routing a print job to the second printer. [010] In some implementations, the operations include providing a user interface displaying pairwise correlations among the different physical parameters; and subsequent to providing the user interface, receiving a user input indicative of the proper subset of the different physical parameters. [OH] In some implementations, grouping the different printers is based on at least one of usage data indicating amounts of usage of the different printers, or consumption data indicating consumables consumption amounts of the different printers. [012] In some implementations, grouping the different printers includes determining, for each printer of the different printers, a product of the measured values for the proper subset of the different physical parameters; and grouping the different printers based on the products corresponding to the different printers. [013] In some implementations, each printer of the different printers is associated with failure data for the printer, and, for each cluster of the at least one cluster, determining the models characterizing the failure likelihoods is based on the failure data for printers grouped into the cluster. [014] In some implementations, for each cluster of the at least one cluster, determining models characterizing the failure likelihoods includes training machine learning models using, as input training data, the sensor data of the different printers grouped into the cluster. The failure data is used as a label for the sensor data of the different printers grouped into the cluster, and the machine learning models are trained to output the failure likelihoods. [015] In some implementations, the operations include, based on the determined models of a cluster to which