Data Processing for Industrial Machine Learning

Patent metadata

Field	Value
Publication number	US-2023019404-A1
Application number	US-202217956117-A
Country	US
Kind code	A1
Filing date	Sep 29, 2022
Priority date	Mar 31, 2020
Publication date	Jan 19, 2023
Grant date	—

Abstract

A computer-implemented method for automating the development of industrial machine learning applications includes one or more sub-methods that, depending on the industrial machine learning problem, may be executed iteratively. These sub-methods include at least one of a method to automate the data cleaning in training and later application of machine learning models, a method to label time series (in particular signal data) with help of other timestamp records, feature engineering with the help of process mining, and automated hyper-parameter tuning for data segmentation and classification.

First claim

What is claimed is:

1 . A computer-implemented method for machine learning, the method comprising:
acquiring a first time series of data from a sensor of an industrial asset or from a control system for an industrial process or plant; processing the first time series of data to obtain an event log; and applying process mining to the event log to provide a conformity analysis and/or bottleneck identification.

2 . The computer-implemented method of claim 1 , further comprising determining a condition indicator of the industrial asset based on the conformity analysis and/or bottleneck identification.

3 . The computer-implemented method of claim 1 , further comprising training and/or applying a first machine learning model to determine process deviations, to determine potential improvements, to perform condition-based monitoring, to perform predictive maintenance, and/or to predict how a batch process will evolve, wherein input parameters to the first machine learning model are based on the conformity analysis and/or bottleneck identification.

4 . The computer-implemented method of claim 1 , wherein the processing of the first time series of data to obtain the event log comprises encoding the first time series of data by applying the symbolic aggregate approximation or artificial intelligence techniques.

5 . The computer-implemented method of claim 4 , wherein the processing of the first time series of data to obtain the event log further comprises performing abstractions on the encoded first time series of data.

6 . The computer-implemented method of claim 5 , wherein the abstractions performed on the encoded first time series of data comprise data aggregations and/or noise suppression filters.

7 . The computer-implemented method of claim 1 , further comprising:
acquiring a second time series of data; cleaning the second time series of data to obtain a third time series of data; and training a data cleaning machine learning model using a plurality of first training samples; wherein a first training sample comprises a clean data point from the third time series of data and a plurality of raw data points from the second time series of data.

8 . The computer-implemented method of claim 7 , wherein the cleaning of the second time series of data comprises handling missing values, removing noise, and/or removing outliers.

9 . The computer-implemented method of claim 1 , further comprising:
acquiring a fourth time series of data from the sensor or from the control system; and applying a data cleaning machine learning model to the fourth time series of data to obtain the first time series of data.

10 . The computer-implemented method of claim 1 , further comprising:
acquiring a first set of labels for training a machine learning model for automatic labelling; acquiring one or more data sources; extracting a first set of features from the one or more data sources; and training the machine learning model for automatic labelling using a plurality of second training samples; wherein a second training sample comprises a label from the first set of labels and one or more features from the first set of features.

11 . The computer-implemented method of claim 10 , wherein the one or more data sources comprise at least one of a shift book, an alarm list, an events list, and/or a data source from a computerized maintenance management system; and/or wherein the machine learning model for automatic labelling is a probabilistic model.

12 . The computer-implemented method of claim 10 , further comprising:
extracting a second set of features from the one or more data sources; and applying the machine learning model for automatic labelling to features from the second set of features to obtain a second set of labels.

13 . The computer-implemented method of claim 2 , wherein the first machine learning model is trained using a plurality of third training samples; and wherein a third training sample comprises a label from the first or second sets of labels and/or the condition indicator of the industrial asset.

Assignees

• Abb Schweiz Ag

Inventors

• Kloepper Benjamin
• Schmidt Benedikt
• Amihai Ido
• Chioua Moncef
• Schlake Jan Christoph
• Kotriwala Arzam Muzaffar
• Hollender Martin
• Janka Dennis
• Lenders Felix
• Abukwaik Hadil

Classifications

• G06N20/20 Primary

  Ensemble learning · CPC title

• G05B23/024

  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 title

• G06N7/01

  Probabilistic graphical models, e.g. probabilistic networks · CPC title

• G06F17/40

  Data acquisition and logging (for input to computer G06F3/00) · CPC title

• G05B23/0283

  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) · CPC title