# US 12,602,614 B2 · Training an artificial intelligence module for industrial applications

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- Number: US 12,602,614 B2
- Document ID: US12602614B2
- Status: Granted
- Kind: B2
- Application number: 17/956,097
- Filed: 2022-09-29
- Published: 2023-03-30
- Granted: 2026-04-14
- Earliest claimed: 2020-03-31
- Assignees: ABB Schweiz AG
- Applicants: ABB Schweiz AG
- Inventors: Benedikt Schmidt; Ido Amihai; Arzam Muzaffar Kotriwala; Moncef Chioua; Felix Lenders; Dennis Janka; Martin Hollender; Jan Christoph Schlake; Hadil Abukwaik; Benjamin Kloepper
- Web page: https://patentgrep.com/patent/US12602614B2

## CPC

- G06N 20/00: Machine learning
- 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}
- G06N 3/0475: Generative networks
- G06N 3/08: Learning methods
- G06N 3/09: Supervised learning
- G06N 3/094: Adversarial learning
- G06N 3/045: Combinations of networks
- G06N 3/047: Probabilistic or stochastic networks
- Y02P 90/80: Management or planning
- G06N: COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
- 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)
- Y02P: CLIMATE CHANGE MITIGATION TECHNOLOGIES IN THE PRODUCTION OR PROCESSING OF GOODS

## Abstract

A computer-implemented method of generating a training data set for training an artificial intelligence module includes providing first and second data sets, the first data set including first data elements indicative of a first operational condition, the second data set including second data elements indicative of a second operational condition that matches the first operational condition. The method further comprises determining a data transformation for transforming the first data elements into the second data elements; applying the data transformation to the first data elements and/or to further data elements of further data sets, thereby generating a transformed data set; and generating a training data set for training the AI module based on at least a part of the transformed data set.

## Claims

### Claim 1

A computer-implemented method of generating a training data set for training an artificial intelligence module (AI module), comprising:
providing, on a data storage, a first data set and a second data set, wherein the first data set includes one or more first data elements indicative of a first operational condition of an industrial system, wherein the second data set includes one or more second data elements indicative of a second operational condition of the industrial system, and wherein the first operational condition substantially matches the second operational condition;
determining a data transformation for transforming the one or more first data elements of the first data set into the one or more second data elements of the second data set;
applying the determined data transformation to the one or more first data elements of the first data set and/or to one or more further data elements of one or more further data sets, thereby generating a transformed data set; and
generating a training data set for training the AI module based on at least a part of the transformed data set, wherein at least one of the first data set and the second data set includes failure data descriptive of an operational failure of the industrial system, and wherein at least one of the first data set and the second data set includes nominal operational data descriptive of a nominal operation of the industrial system.

### Claim 2 (depends on 1)

The method according to claim 1, wherein generating the training data set comprises providing an initial training data set for training the AI module; and supplementing the initial training data set with the at least part of the transformed data set.

### Claim 3 (depends on 2)

The method according to claim 2, wherein supplementing the initial training data set comprises at least one of: adding the at least part of the transformed data set to the initial training data set; and/or replacing at least a part of the initial training data set with the at least a part of the transformed data set.

### Claim 4 (depends on 1)

The method according to claim 1, further comprising:
identifying and/or selecting, in the first data set, the one or more first data elements indicative of the first operational condition of the industrial system; and
identifying and/or selecting, in the second data set, the one or more second data elements indicative of the second operational condition of the industrial system.

### Claim 5 (depends on 4)

The method according to claim 4, wherein the one or more first data elements of the first data set are time-related; and wherein the one or more second data elements of the second data set are time-related.

### Claim 6 (depends on 4)

The method according to claim 4, further comprising matching, with respect to at least one operational parameter of the industrial system, the one or more first data elements to the one or more second data elements, wherein the at least one operational parameter is indicative of an operation of the industrial system.

### Claim 7 (depends on 6)

The method according to claim 6, wherein the at least one operational parameter is an operation time of the industrial system; and/or wherein the one or more first data elements are matched to the one or more second data element with respect to an operation time of the industrial system.

### Claim 8 (depends on 1)

The method according to claim 1, wherein the data transformation is determined element-wise for each of the one or more first data elements.

### Claim 9 (depends on 1)

The method according to claim 1, wherein the data transformation is determined using a machine learning algorithm.

### Claim 10 (depends on 1)

The method according to claim 1, wherein the data transformation is determined using at least one of Kernel-density estimation, Gaussian regression, an artificial neural network, a generative adversarial network, a sum product network, and an adversarial neural network.

### Claim 11 (depends on 1)

The method according to claim 1, wherein the data transformation is determined in a non-probabilistic manner.

### Claim 12 (depends on 1)

The method according to claim 1, wherein at least one of the first data set and the second data set includes simulation data descriptive of simulating an operation of the industrial system; and wherein at least one of the first data set and the second data set includes operational data descriptive of an actual operation of the industrial system.

### Claim 13 (depends on 1)

The method according to claim 1, wherein the first data set includes simulation data descriptive of simulating an operation of the industrial system and the second data set includes operational data descriptive of an actual operation of the industrial system; and wherein determining the data transformation comprises determining a noise of the one or more second data elements of the second data set, such that by applying the data transformation the noise is reproduced in the transformed data.

### Claim 14 (depends on 13)

The method according to claim 13, wherein the determined noise is representative of a measurement noise of the one or more second data elements, and wherein the determined noise is representative of an environmental influence affecting the actual operation of the industrial system.

### Claim 15 (depends on 1)

The method according to claim 1, wherein the first data set includes the nominal operational data descriptive of the nominal operation of the industrial system and the second data set includes the failure data descriptive of the operational failure of the industrial system; and wherein determining the data transformation comprises determining, in the second data set, the failure data descriptive of the operational failure of the industrial system, such that by applying the data transformation the operational failure of the industrial system is reproduced in the transformed data.