Systems and methods for printer maintenance operations

• US 10,372,389 B2
• Filed: 09/22/2017
• Issued: 08/06/2019
• Est. Priority Date: 09/22/2017
• Status: Active Grant

• Current Assignee: Hand Held Products Incorporated (Honeywell International Inc.)
• Original Assignee: Datamax-O'Neil Corporation (Honeywell International Inc.)
• Inventors: Celinder, Thomas Axel Jonas, Unemyr, Erik Karl Henning
• Primary Examiner(s): Shiferaw, Henok
• Application Number:
  US15/712,367
• Publication Number:
  US 20190095148A1
• Time in Patent Office: 683 Days
• Field of Search: None
• CPC Class Codes: G03G 15/553 Monitoring or warning means for exhaustion or lifetime end of consumables, e.g. indication of insufficient copy sheet quantity for a job
• CPC Class Codes: G06F 11/008 Reliability or availability analysis
• CPC Class Codes: G06F 11/0751 Error or fault detection not based on redundancy power supply failures G06F1/30; network fault management H04L41/06
• CPC Class Codes: G06F 11/3013 where the computing system is an embedded system, i.e. a combination of hardware and software dedicated to perform a certain function in mobile devices, printers, automotive or aircraft systems testing or monitoring of control systems or parts thereof G05B23/02
• CPC Class Codes: G06F 11/3055 Monitoring arrangements for monitoring the status of the computing system or of the computing system component, e.g. monitoring if the computing system is on, off, available, not available error or fault processing without redundancy G06F11/0703; error detection or correction by redundancy in data representation G06F11/08; error detection or correction by redundancy in operation G06F11/14; error detection or correction by redundancy in hardware G06F11/16
• CPC Class Codes: G06F 11/3058 Monitoring arrangements for monitoring environmental properties or parameters of the computing system or of the computing system component, e.g. monitoring of power, currents, temperature, humidity, position, vibrations thermal management in cooling arrangements of a computing system G06F1/206
• CPC Class Codes: G06F 11/3452 Performance evaluation by statistical analysis
• CPC Class Codes: G06F 3/121 Facilitating exception or error detection and recovery, e.g. fault, media or consumables depleted
• CPC Class Codes: G06F 3/1229 Printer resources management or printer maintenance, e.g. device status, power levels
• CPC Class Codes: G06F 3/1285 Remote printer device, e.g. being remote from client or server
• CPC Class Codes: G06F 3/1293 Printer information exchange with computer
• CPC Class Codes: H04N 1/32609 Fault detection or counter-measures, e.g. original mis-positioned, shortage of paper

Abstract

The present disclosure is generally directed to systems and methods for executing various types of maintenance operations upon various types of printers. The maintenance operations can include preemptive actions that are taken upon a printer that is being operated in a certain environment, the preemptive actions based on evaluating operational statistics associated with a set of similar printers operated in a similar environment. The operational statistics, which are automatically accumulated over a period of time for various printers under various operating conditions, can be implemented in various ways, such as in the form of behavioral models and/or predictive models. In some embodiments, the behavioral models and/or predictive models indicate an expected-time-to-failure of a component when a printer is operated in a certain environment. The expected-time-to-failure information can be utilized to execute preemptive printer maintenance operations such as scheduling a replacement of the component ahead of the expected-time-to-failure.

Claims

1. A method comprising:

deriving a first behavioral model based on an operating environment of a first set of printers;

deriving a second behavioral model based on an operating environment of a second set of printers;

determining at least one of a current physical condition or a current operating environment of a printer;

associating the printer with one of the first behavioral model or the second behavioral model based on the at least one of a current physical condition or a current operating environment of the printer;

determining from the associated one of the first behavioral model or the second behavioral model, a failure statistic comprising an expected-time-to-failure of a first component of the printer; and

scheduling a replacement of the first component of the printer based on the expected-time-to-failure of the first component.

2. The method of claim 1, wherein determining the at least one of a current physical condition or a current operating environment of the printer comprises utilizing one or more sensors to detect at least one of a time-of-usage of the first component, an environmental condition, a wear condition of the first component, or a characteristic of a print media installed in the printer.

3. The method of claim 2, wherein the first component is a printhead installed in the printer.

4. The method of claim 1, wherein the first behavioral model is a first predictive model based on a first set of failure statistics of the first set of printers and the second behavioral model is a second predictive model based on a second set of failure statistics of the second set of printers.

5. The method of claim 4, wherein the first set of failure statistics comprises a first mean time between failures (MTBF) of the first set of printers and the second set of failure statistics comprises a second MTBF of the second set of printers.

6. The method of claim 5, wherein the first MTBF is determined, at least in part, on a first environmental condition in which the first set of printers is operated and the second MTBF is determined, at least in part, on a second environmental condition in which the second set of printers is operated.

7. The method of claim 1, wherein the first component is present in each of the first set of printers and further comprising:

defining the first set of printers based, at least in part, on the first component that is present in each of the first set of printers; and

defining the second set of printers based, at least in part, on a second component that is present in each of the second set of printers.

8. A method comprising:

deriving a first behavioral model based on an operating environment of a first set of printers;

deriving a second behavioral model based on an operating environment of a second set of printers;

determining at least one of a current physical condition or a current operating environment of a printer;

associating the printer with one of the first behavioral model or the second behavioral model based on the at least one of a current physical condition or a current operating environment of the printer;

determining from the associated one of the first behavioral model or the second behavioral model, a failure statistic comprising an expected-time-to-failure of a printhead of the printer; and

scheduling a replacement of the printhead of the printer based on the expected-time-to-failure of the first component.

9. The method of claim 8, wherein determining the at least one of a current physical condition or a current operating environment of the printer comprises detecting a time-of-usage of the printhead, an environmental condition, a wear condition of the printhead, or a characteristic of a print media installed in the printer.

10. The method of claim 8, wherein the first behavioral model is a first predictive model based on a first set of failure statistics of the first set of printers and the second behavioral model is a second predictive model based on a second set of failure statistics of the second set of printers.

11. The method of claim 10, wherein the first set of failure statistics comprises a first mean time between failures (MTBF) of the first set of printers and the second set of failure statistics comprises a second MTBF of the second set of printers.

12. The method of claim 11, wherein the first MTBF is determined, at least in part, on a first environmental condition in which the first set of printers is operated and the second MTBF is determined, at least in part, on a second environmental condition in which the second set of printers is operated.

13. The method of claim 8, wherein the printhead is present in each of the first set of printers and further comprising:

defining the first set of printers based, at least in part, on the printhead that is present in each of the first set of printers; and

defining the second set of printers based, at least in part, on a second component that is present in each of the second set of printers.

14. A non-transitory computer-readable storage medium containing instructions executable by a processor for performing operations comprising:

deriving a first behavioral model based on an operating environment of a first set of printers;

deriving a second behavioral model based on an operating environment of a second set of printers;

determining at least one of a current physical condition or a current operating environment of a printer;

associating the printer with one of the first behavioral model or the second behavioral model based on the at least one of a current physical condition or a current operating environment of the printer;

determining from the associated one of the first behavioral model or the second behavioral model, a failure statistic comprising an expected-time-to-failure of a first component of the printer; and

scheduling a replacement of the first component of the printer based on the expected-time-to-failure of the first component.

15. The non-transitory computer-readable storage medium of claim 14, wherein determining the at least one of a current physical condition or a current operating environment of the printer comprises utilizing one or more sensors to detect at least one of a time-of-usage of the first component, an environmental condition, a wear condition of the first component, or a characteristic of a print media installed in the printer.

16. The non-transitory computer-readable storage medium of claim 15, wherein the first component is a printhead installed in the printer.

17. The non-transitory computer-readable storage medium of claim 14, wherein the first behavioral model is a first predictive model based on a first set of failure statistics of the first set of printers and the second behavioral model is a second predictive model based on a second set of failure statistics of the second set of printers.

18. The non-transitory computer-readable storage medium of claim 17, wherein the first set of failure statistics comprises a first mean time between failures (MTBF) of the first set of printers and the second set of failure statistics comprises a second MTBF of the second set of printers.

19. The non-transitory computer-readable storage medium of claim 18, wherein the first MTBF is determined, at least in part, on a first environmental condition in which the first set of printers is operated and the second MTBF is determined, at least in part, on a second environmental condition in which the second set of printers is operated.

20. The non-transitory computer-readable storage medium of claim 14, wherein the first component is present in each of the first set of printers and further comprising:

defining the first set of printers based, at least in part, on the first component that is present in each of the first set of printers; and

defining the second set of printers based, at least in part, on a second component that is present in each of the second set of printers.