EQUIPMENT REPAIR MANAGEMENT AND EXECUTION

• US 20200134574A1
• Filed: 12/30/2019
• Published: 04/30/2020
• Est. Priority Date: 09/24/2018
• Status: Active Grant

• Current Assignee: Hitachi, Ltd.
• Original Assignee: Hitachi, Ltd.
• Inventors: GHOSH, Dipanjan, FARAHAT, Ahmed Khairy, ZHANG, Chi, VIEIRA, Marcos, GUPTA, Chetan
• Granted Patent: US 11,544,676 B2
• Time in Patent Office: Days
• CPC Class Codes: G06N 3/08 Learning methods
• CPC Class Codes: G06Q 10/0635 Risk analysis of enterprise or organisation activities
• CPC Class Codes: G06Q 10/067 Enterprise or organisation modelling
• CPC Class Codes: G06Q 10/20 Administration of product repair or maintenance

Abstract

In some examples, a computer system may receive historical repair data and may extract features from the historical repair data for use as training data. The computer system may determine, from the historical repair data, a repair hierarchy including a plurality of repair levels which includes repair actions as one of the repair levels. Furthermore, the computer system may train the machine learning model, which performs multiple tasks for predicting values of individual levels of the repair hierarchy, by tuning parameters of the machine learning model using the training data.

Claims

1. A system comprising:

one or more processors; and

one or more non-transitory computer-readable media maintaining executable instructions, which, when executed by the one or more processors, configure the one or more processors to perform operations including;

receiving historical repair data;

extracting features from the historical repair data as training data;

determining, from the historical repair data, a repair hierarchy including a plurality of repair levels which includes repair actions as one of the repair levels; and

training the machine learning model, which performs multiple tasks for predicting values of individual levels of the repair hierarchy, by tuning parameters of the machine learning model using the training data.

2. The system as recited in claim 1, wherein the machine learning model is configured to determine a repair plan based on the one or more repair actions and a respective probability of success determined for each repair action.

3. The system as recited in claim 2, wherein the machine learning model is configured to perform at least one of:

sending an order for a part for a repair;

sending a communication to assign labor to perform the repair;

sending a communication to schedule a repair time for the repair;

or

remotely initiating a procedure on the equipment to effectuate, at least partially, the repair.

4. The system as recited in claim 1, the operations further comprising, during the training of the machine learning model, employing a backpropagation algorithm to calculate gradients with respect to each learnable variable in the machine learning model using a joint loss function.

5. The system as recited in claim 1, wherein the training the machine learning model is conducted by minimizing a joint loss function calculated from losses in each repair level.

6. The system as recited in claim 6, wherein the joint loss function is the sum of cross entropy loss of each repair level.

7. A method for building a machine learning model for determining a repair action corresponding to a repair request, the method comprising;

receiving, by one or more processors, historical repair data;

extracting features from the historical repair data as training data;

determining, from the historical repair data, a repair hierarchy including a plurality of repair levels which includes repair actions as one of the repair levels; and

training the machine learning model, which performs multiple tasks for predicting values of individual levels of the repair hierarchy, by tuning parameters of the machine learning model using the training data.

8. The method as recited in claim 7, wherein the machine learning model is configured to determine a repair plan based on the one or more repair actions and a respective probability of success determined for each repair action.

9. The method as recited in claim 8, wherein the machine learning model is configured to perform at least one of:

sending an order for a part for a repair;

sending a communication to assign labor to perform the repair;

sending a communication to schedule a repair time for the repair;

or

remotely initiating a procedure on the equipment to effectuate, at least partially, the repair.

10. The method as recited in claim 7, further comprising, during the training of the machine learning model, employing a backpropagation algorithm to calculate gradients with respect to each learnable variable in the machine learning model using a joint loss function.

11. The method as recited in claim 7, wherein the training the machine learning model is conducted by minimizing a joint loss function calculated from losses in each repair level.

12. The method as recited in claim 11, wherein the joint loss function is the sum of cross entropy loss of each repair level.

13. One or more non-transitory computer-readable media storing instructions that, when executed by one or more processors, program the one or more processors to perform operations comprising:

receiving historical repair data;

extracting features from the historical repair data as training data;

determining, from the historical repair data, a repair hierarchy including a plurality of repair levels which includes repair actions as one of the repair levels; and

training the machine learning model, which performs multiple tasks for predicting values of individual levels of the repair hierarchy, by tuning parameters of the machine learning model using the training data.

14. The one or more non-transitory computer-readable media as recited in claim 13, wherein the machine learning model is configured to determine a repair plan based on the one or more repair actions and a respective probability of success determined for each repair action.

15. The one or more non-transitory computer-readable media as recited in claim 14, wherein the machine learning model is configured to perform at least one of:

sending an order for a part for a repair;

sending a communication to assign labor to perform the repair;

sending a communication to schedule a repair time for the repair;

or remotely initiating a procedure on the equipment to effectuate, at least partially, the repair.

16. The one or more non-transitory computer-readable media as recited in claim 13, further comprising, during the training of the machine learning model, employing a backpropagation algorithm to calculate gradients with respect to each learnable variable in the machine learning model using a joint loss function.

17. The one or more non-transitory computer-readable media as recited in claim 13, wherein the training the machine learning model is conducted by minimizing a joint loss function calculated from losses in each repair level.

18. The one or more non-transitory computer-readable media as recited in claim 17, wherein the joint loss function is the sum of cross entropy loss of each repair level.