MACHINE LEARNING METHOD AND MACHINE LEARNING DEVICE FOR LEARNING FAULT CONDITIONS, AND FAULT PREDICTION DEVICE AND FAULT PREDICTION SYSTEM INCLUDING THE MACHINE LEARNING DEVICE

• US 20170031329A1
• Filed: 07/27/2016
• Published: 02/02/2017
• Est. Priority Date: 07/31/2015
• Status: Active Grant

• Current Assignee: Fanuc Corporation, Preferred Networks, Inc.
• Original Assignee: Fanuc Corporation, Preferred Networks, Inc.
• Inventors: INAGAKI, Shougo, NAKAGAWA, Hiroshi, OKANOHARA, Daisuke, OKUTA, Ryosuke, MATSUMOTO, Eiichi, KAWAAI, Keigo
• Granted Patent: US 10,317,853 B2
• Time in Patent Office: Days
• US Class Current: 1/1
• CPC Class Codes: B25J 9/163 learning, adaptive, model based, rule based expert control
• CPC Class Codes: G05B 13/0265 the criterion being a learning criterion
• CPC Class Codes: G05B 15/02 electric
• CPC Class Codes: G05B 19/4063 Monitoring general control system G05B19/4062 takes precedence
• CPC Class Codes: G05B 2219/31359 Object oriented model for fault, quality control
• CPC Class Codes: G05B 2219/33321 Observation learning
• CPC Class Codes: G05B 23/024 Quantitative history assessment, e.g. mathematical relationships between available data; Functions therefor; Principal component analysis [PCA]; Partial least square [PLS]; Statistical classifiers, e.g. Bayesian networks, linear regression or correlation analysis; Neural networks
• CPC Class Codes: G06N 20/00 Machine learning
• CPC Class Codes: G06N 3/04 Architecture, e.g. interconnection topology
• CPC Class Codes: G06N 3/044 Recurrent networks, e.g. Hopfield networks
• CPC Class Codes: G06N 3/08 Learning methods
• CPC Class Codes: G06N 3/084 Backpropagation, e.g. using gradient descent
• CPC Class Codes: G06N 5/048 Fuzzy inferencing
• CPC Class Codes: Y02P 90/02 Total factory control, e.g. smart factories, flexible manufacturing systems [FMS] or integrated manufacturing systems [IMS]
• CPC Class Codes: Y10S 901/47 Optical

Abstract

A fault prediction system includes a machine learning device that learns conditions associated with a fault of an industrial machine. The machine learning device includes a state observation unit that, while the industrial machine is in operation or at rest, observes a state variable including, e.g., data output from a sensor, internal data of control software, or computational data obtained based on these data, a determination data obtaining unit that obtains determination data used to determine whether a fault has occurred in the industrial machine or the degree of fault, and a learning unit that learns the conditions associated with the fault of the industrial machine in accordance with a training data set generated based on a combination of the state variable and the determination data.

Claims

1. A machine learning device that learns a condition associated with a fault of an industrial machine, the device comprising:

a state observation unit that, while the industrial machine is in operation or at rest, observes a state variable comprising at least one of data output from a sensor that detects a state of one of the industrial machine and a surrounding environment, internal data of control software controlling the industrial machine, and computational data obtained based on one of the output data and the internal data;

a determination data obtaining unit that obtains determination data used to determine one of whether a fault has occurred in the industrial machine and a degree of fault; and

a learning unit that learns the condition associated with the fault of the industrial machine in accordance with a training data set generated based on a combination of the state variable and the determination data.

2. The machine learning device according to claim 1, wherein the learning unit is configured to learn the condition in accordance with the training data set generated for each of a plurality of industrial machines.

3. The machine learning device according to claim 1, wherein the learning unit learns a normal state only in a predetermined period and then detects occurrence of a fault determined by the determination data obtaining unit.

4. The machine learning device according to claim 1, wherein when the determination data obtaining unit obtains determination data indicating a fault of the industrial machine, the learning unit updates the condition by weighting the determination data comprised in the training data set, in accordance with a length of time from when the determination data is obtained until a fault occurs.

5. A fault prediction device that comprises the machine learning device according to claim 1 and predicts a fault of the industrial machine,

the fault prediction device further comprising:

a fault information output unit that outputs fault information indicating one of whether a fault has occurred in the industrial machine and a degree of fault, in response to input of a current state variable of the state variable, based on a result of learning by the learning unit in accordance with the training data set.

6. The fault prediction device according to claim 5, wherein the learning unit learns the condition again in accordance with an additional training data set generated based on a combination of the current state variable and the determination data.

7. The fault prediction device according to claim 5, wherein

the machine learning device is connected to the industrial machine via a network, and

the state observation unit obtains the current state variable via the network.

8. The fault prediction device according to claim 7, wherein the machine learning device is located on a cloud server.

9. The fault prediction device according to claim 5, wherein the machine learning device is mounted in a controller that controls the industrial machine.

10. The fault prediction device according to claim 5, wherein the industrial machine comprises a plurality of industrial machines, and the learning result obtained by the machine learning device is shared by the plurality of industrial machines.

11. A fault prediction system comprising:

the fault prediction device according to claim 5;

a sensor that outputs the output data; and

a fault information notifying unit that notifies an operator of the fault information.

12. The fault prediction system according to claim 11, wherein a time at that the fault information notifying unit notifies the operator of the fault information satisfies at least one of precedence to a time defined by a first predetermined period preceding a time at that a fault occurs, and subsequence to a time defined by a second predetermined period preceding the time at that a fault occurs.

13. A machine learning method for learning a condition associated with a fault of an industrial machine, the method comprising:

while the industrial machine is in operation or at rest, observing a state variable comprising at least one of data output from a sensor that detects a state of one of the industrial machine and a surrounding environment, internal data of control software controlling the industrial machine, and computational data obtained based on one of the output data and the internal data;

obtaining determination data used to determine one of whether a fault has occurred in the industrial machine and a degree of fault; and

learning the condition associated with the fault of the industrial machine in accordance with a training data set generated based on a combination of the state variable and the determination data.