Maintenance intervention predicting

Patent metadata

Field	Value
Publication number	US-11216329-B2
Application number	US-201816769355-A
Country	US
Kind code	B2
Filing date	Jan 26, 2018
Priority date	Jan 26, 2018
Publication date	Jan 4, 2022
Grant date	Jan 4, 2022

Abstract

Examples include a non-transitory machine-readable storage medium having stored thereon machine-readable instructions executable to cause a processing resource to monitor sensory inputs related to a device, monitor a first maintenance intervention related to the device, store data relating to the monitored sensory inputs and the first maintenance intervention in a centralized database, and predict a second maintenance intervention based on the data stored in the centralized database.

First claim

What is claimed:

1. A non-transitory machine-readable storage medium having stored thereon machine-readable instructions executable to cause a processing resource to:
monitor sensory inputs related to a device;
monitor a first maintenance intervention related to the device;
store data relating to the monitored sensory inputs and the first maintenance intervention in a centralized database;
validate a result of a selected supervised machine learning model used to analyze the data stored in the centralized database;
predict a second maintenance intervention based on the data stored in the centralized database and the selected supervised machine learning model; and
perform the second maintenance intervention based on the prediction.

2. The medium of claim 1 , wherein the instructions are executable to select a time interval to monitor the sensory inputs.

3. The medium of claim 1 , wherein the instructions are executable to store the data relating to the monitored sensory inputs in a system log.

4. The medium of claim 1 , wherein the device is a printer.

5. The medium of claim 1 , wherein the instructions are executable to provide a notification of the second maintenance intervention.

6. The medium of claim 1 , wherein the instructions are executable to analyze the data stored in the centralized database via supervised machine learning.

7. A system, comprising:
a processing resource; and
a memory resource storing machine-readable instructions executable to cause the processing resource to:
monitor sensory inputs related to a device;
monitor a first maintenance intervention related to the device;
store data relating to the monitored sensory inputs and the first maintenance intervention in a centralized database;
analyze, via supervised machine learning, the data stored in the centralized database;
validate a result of a selected supervised machine learning model used to analyze the data stored in the centralized database;
predict a second maintenance intervention based on the analyzed data stored in the centralized database and the selected supervised machine learning model; and
perform the second maintenance intervention based on the prediction.

8. The system of claim 7 , wherein the instructions are executable to select a supervised machine learning model to analyze the data stored in the centralized database.

9. The system of claim 7 , wherein the first and the second maintenance intervention are related to a device failure, wherein the device failure is based on the device exceeding an estimated lifetime.

10. The system of claim 7 , wherein the centralized database includes a machine learning model library, and wherein the machine learning model library includes at least one machine learning model.

11. A computer implemented method, comprising:
monitoring sensory inputs related to a device;
monitoring a first maintenance intervention related to the device;
storing data relating to the monitored sensory inputs and the first maintenance intervention in a centralized database;
analyzing, via supervised machine learning, the data stored in the centralized database;
validating a result of a selected supervised machine learning model used to analyze the data stored in the centralized database;
predicting a second maintenance intervention based on the analyzed data stored in the centralized database and the selected supervised machine learning model;
providing a notification of the second maintenance intervention; and
performing the second maintenance intervention based on the prediction.

12. The method of claim 11 , wherein monitoring sensory inputs related to the machine includes generating system log information.

13. The method of claim 11 , wherein providing a notification of the second maintenance intervention includes providing an amount of time until the second maintenance intervention.

14. The method of claim 11 , further comprising providing a health status of the device.

Assignees

• Hewlett Packard Development Co

Inventors

• Alves Luciano
• Friedrich Fernando
• Neves Anderson
• Oliveira Douglas
• Reinhardt Kelvin
• Urbano Paulo
• Neto Paulo

Classifications

• G06F11/0787

  Storage of error reports, e.g. persistent data storage, storage using memory protection · CPC title

• G06F11/3476

  Data logging (G06F11/14, G06F11/2205 take precedence) · CPC title

• G06F11/008 Primary

  Reliability or availability analysis · CPC title

• G06N20/00

  Machine learning · CPC title

• G06F11/3495

  for systems · CPC title