# US 2018/0373233 A1 · Failure predicting apparatus and machine learning device

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- Number: US 2018/0373233 A1
- Document ID: US20180373233A1
- Status: Published application
- Kind: A1
- Application number: 16/012,803
- Filed: 2018-06-20
- Published: 2018-12-27
- Earliest claimed: 2017-06-27
- Assignees: Fanuc Corporation
- Applicants: FANUC Corporation
- Inventors: Kazuya GOTO
- Web page: https://patentgrep.com/patent/US20180373233A1

## CPC

- G05B 23/0283: {Predictive maintenance, e.g. involving the monitoring of a system and, based on the monitoring results, taking decisions on the maintenance schedule of the monitored system; Estimating remaining useful life [RUL] (preventive maintenance, i.e. planning maintenance according to the available resources without monitoring the system G06Q10/06)}
- G05B 13/0265: {the criterion being a learning criterion}
- G05B 19/4065: Monitoring tool breakage, life or condition
- G05B 23/0229: {knowledge based, e.g. expert systems; genetic algorithms}
- 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}
- G05B 23/0254: {based on a quantitative model, e.g. mathematical relationships between inputs and outputs; functions: observer, Kalman filter, residual calculation, Neural Networks}
- G06F 11/3051: {Monitoring arrangements for monitoring the configuration of the computing system or of the computing system component, e.g. monitoring the presence of processing resources, peripherals, I/O links, software programs (verification or detection of system hardware configuration G06F11/2247)}
- 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)}
- G06N 3/0499: Feedforward networks
- G06N 3/08: Learning methods
- G06N 3/09: Supervised learning
- G06N 20/00: Machine learning
- G05B 17/02: electric
- G05B 2219/37251: Selfcorrecting, counter for tool life adapts correction
- G06F 11/008: {Reliability or availability analysis}
- G06F 11/3031: {where the computing system component is a motherboard or an expansion card}
- G06F 11/3062: {where the monitored property is the power consumption (power management in a computing system G06F1/3203)}
- G05B: CONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS (systems for controlling or regulating non-electric variables G05D; systems for regulating electric or magnetic variables G05F; control devices or systems insofar as characterised by mechanical features only G05G)
- G06F: ELECTRIC DIGITAL DATA PROCESSING (computer systems based on specific computational models G06N)
- G06N: COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS

## Abstract

A machine learning device included in a failure predicting apparatus includes a state observing unit that observes, as state variables indicating a current environmental state, operating state data indicating an operating state of the management target device and device configuration data indicating a device configuration of the management target device, a label data acquiring unit that acquires, as label data, maintenance history data indicating a maintenance history of the management target device, and a learning unit that, by using the state variables and the label data, learns a failure timing of a printed circuit board included in the management target device, the operating state data, and the device configuration data such that the failure timing is associated with the operating state data and the device configuration data.

## Claims

### Claim 1

A failure predicting apparatus for predicting a failure timing of a printed circuit board included in a management target device, the failure predicting apparatus comprising
a machine learning device that learns the failure timing of the printed circuit board included in the management target device with respect to an operating state of the management target device, wherein
the machine learning device includes
a state observing unit that observes, as state variables indicating a current environmental state, operating state data indicating an operating state of the management target device and device configuration data indicating a device configuration of the management target device,
a label data acquiring unit that acquires, as label data, maintenance history data indicating a maintenance history of the management target device, and
a learning unit that, by using the state variables and the label data, learns the failure timing of the printed circuit board included in the management target device, the operating state data, and the device configuration data such that the failure timing is associated with the operating state data and the device configuration data.

### Claim 2 (depends on 1)

The failure predicting apparatus according to claim 1, wherein
the operating state data includes at least any one of an accumulated operating time, an accumulated power consumption, an input voltage/current, an output voltage/current, an environmental temperature, an environmental humidity, and a vibration, a usage state of cutting fluid, and a rotation speed of a cooling fan, which are of the management target device.

### Claim 3 (depends on 1)

The failure predicting apparatus according to claim 1, wherein
the label data includes failure component information indicating a failure in a component mounted on the printed circuit board, and
the learning unit learns the failure timing of the printed circuit board included in the management target device, a component in which a failure has occurred, the operating state data, and the device configuration data such that the failure timing and the component are associated with the operating state data and the device configuration data.

### Claim 4 (depends on 1)

The failure predicting apparatus according to claim 1, wherein
the learning unit includes
a difference calculating unit that calculates a difference between a correlation model for predicting, from the state variables, a failure timing of a printed circuit board included in the management target device and a correlation feature recognizable from teacher data prepared in advance, and
a model updating unit that updates the correlation model so as to reduce the difference.

### Claim 5 (depends on 1)

The failure predicting apparatus according to claim 1, wherein
the learning unit computes the state variables and the label data by a multilayer structure.

### Claim 6 (depends on 1)

The failure predicting apparatus according to claim 1, further comprising
a predicting unit that outputs a predicted value of a failure timing of a printed circuit board included in the management target device in accordance with a result of learning by the learning unit.

### Claim 7 (depends on 1)

The failure predicting apparatus according to claim 1, wherein
the machine learning device exists in a cloud server.

### Claim 8

A machine learning device for learning a failure timing of a printed circuit board included in a management target device with respect to an operating state of the management target device, the machine learning device comprising
a state observing unit that observes, as state variables indicating a current environmental state, operating state data indicating an operating state of the management target device and device configuration data indicating a device configuration of the management target device,
a label data acquiring unit that acquires, as label data, maintenance history data indicating a maintenance history of the management target device, and
a learning unit that, by using the state variables and the label data, learns the failure timing of the printed circuit board included in the management target device, the operating state data, and the device configuration data such that the failure timing is associated with the operating state data and the device configuration data.