# US 2021/0241544 A1 · Platform for analyzing health of heavy electric machine and analysis method using the same

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- Number: US 2021/0241544 A1
- Document ID: US20210241544A1
- Status: Published application
- Kind: A1
- Application number: 17/239,122
- Filed: 2021-04-23
- Published: 2021-08-05
- Earliest claimed: 2019-10-07
- Assignees: Pact-Alliance Co. Ltd
- Applicants: Pact-Alliance Co. Ltd
- Inventors: Sang Hun LEE
- Web page: https://patentgrep.com/patent/US20210241544A1

## CPC

- G01R 31/12: Testing dielectric strength or breakdown voltage {; Testing or monitoring effectiveness or level of insulation, e.g. of a cable or of an apparatus, for example using partial discharge measurements; Electrostatic testing (G01R31/08, G01R31/327 and G01R31/72 take precedence; measuring in plasmas G01R19/0061; measuring dielectric constants G01R27/2617; ESD, EMC or EMP testing of circuits G01R31/002)}
- G07C 3/005: {during manufacturing process}
- G01R 31/346: {Testing of armature or field windings}
- 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)}
- G06N 20/00: Machine learning
- G07C 3/02: Registering or indicating working or idle time only
- G07C 5/0841: {Registering performance data (recording measured values G01D; information storage G11B)}
- G01R: MEASURING ELECTRIC VARIABLES; MEASURING MAGNETIC VARIABLES (indicating correct tuning of resonant circuits H03J3/12)
- G07C: TIME OR ATTENDANCE REGISTERS; REGISTERING OR INDICATING THE WORKING OF MACHINES; GENERATING RANDOM NUMBERS; VOTING OR LOTTERY APPARATUS; ARRANGEMENTS, SYSTEMS OR APPARATUS FOR CHECKING NOT PROVIDED FOR ELSEWHERE
- 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)
- G06N: COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS

## Abstract

Provided is an analyzing method using a platform for analyzing health of a heavy electric machine. The method includes inputting on-site diagnosis information in which pieces of data collected in a site are received in a state in which operation of the heavy electric machine stops, collecting pieces of online sensor data in which pieces of data of installed sensors are periodically/discontinuously collected through a sensor module and a data collection module, building a database using the data in the inputting of the on-site diagnosis information and the data in the collecting of the pieces of online sensor data, analyzing of the current standard health of the heavy electric machine, and automatically determining a diagnosis result.

## Claims

### Claim 1

A platform for analyzing health of a heavy electric machine, which is a heavy electric machine health analysis platform (1000), the platform comprising:
a sensor module (210);
a data collection module (220) configured to collect pieces of data from the sensor module (210);
a data management module (400) configured to receive and manage pieces of data from the data collection module (220);
a database module (500) configured to record the pieces of data received from the data management module (400); and
a diagnosis and analysis module (600) configured to perform diagnosis of an electric motor by applying the pieces of data recorded in the database module (500),
wherein the diagnosis and analysis module (600) includes a current standard insulation diagnosis system (610) configured to diagnose health of the electric machine on the basis of the pieces of received data,
a trend-based health analysis system (620) configured to estimate and analyze a predicted trend for each year in conjunction with a database of the database module (500),
a degradation prediction simulation and analysis system (630) configured to generate a simulation model and analyze degradation, and
an online sensor data analysis system (640) configured to analyze using the provided online data.

### Claim 2 (depends on 1)

The platform of claim 1, further comprising:
a payment module (110) configured to perform payment between external users;
a report and data management module (120) configured to collect and/or provide reports and pieces of data from and/or to the external users; and
a warning module (130) configured to warn the outside about an event when the event occurs.

### Claim 3 (depends on 1)

The platform of claim 1, wherein:
the sensor module (210) includes a mounting sensor (211), diagnosis equipment (212), and a system sensor (213) which are mounted on the heavy electric machine; and
the data collection module (220) includes a PI system (221) configured to collect pieces of data of the mounting sensor (211), a general diagnosis system (222) linked with the diagnosis equipment (212), and a transducer (223) linked with the system sensor (213).

### Claim 4 (depends on 1)

The platform of claim 1, wherein the database module (500) includes an electric motor specification database (510), an insulation diagnosis database (520), a failure history database (530), an online sensor database (540), and a health determination database (550).

### Claim 5 (depends on 4)

The platform of claim 4, wherein the trend-based health analysis system (620) estimates a predicted trend for each year by estimating the same type of heavy electric machine data parameters using a target electric motor data parameter and the insulation diagnosis database (520).

### Claim 6 (depends on 1)

The platform of claim 1, wherein the degradation prediction simulation and analysis system (630) includes a simulation model and a machine learning-based degradation estimation modeling.

### Claim 7 (depends on 6)

The platform of claim 6, wherein the constructed machine learning-based degradation estimation modeling is modeling in which each piece of individual data is expressed in a unit space (Mahalanobis space (MS)) based on a normal group center point and then it is determined that the pieces of data are normal or abnormal by measuring a unit distance (Mahalanobis distance (MD)) indicating how far the pieces of data are from the center point.

### Claim 8 (depends on 7)

The platform of claim 7, wherein independent variables of the each piece of individual data include a value of an insulation resistance measured for one minute, a value of polarity index determination, a value of a polarity index, a value of dielectric loss tangent determination, a value of a dielectric loss tangent, a value of alternating current (AC) determination, a value of an AC, a value of partial high voltage determination, and a value of a partial discharge high voltage.

### Claim 9 (depends on 1)

The platform of claim 1, wherein the heavy electric machine includes a generator, a transformer, and an electric motor.

### Claim 10

A method of analyzing health of a heavy electric machine, the method comprising:
an operation (S100) of inputting on-site diagnosis information in which pieces of data collected in a site are received in a state in which operation of the heavy electric machine stops;
an operation (S200) of collecting pieces of online sensor data in which pieces of data of installed sensors are periodically/discontinuously collected through a sensor module (210) and a data collection module (220);
an operation (S400) of building a database using the data in the operation (S100) of the inputting of the on-site diagnosis information and the data in the operation (S200) of the collecting of the pieces of online sensor data;
an operation (S300) of analyzing the current standard health which includes an operation (S310) of analyzing a direct current test, an operation (S320) of analyzing an alternating current test, an operation (S330) of analyzing a dielectric loss tangent test, and an operation (S340) of analyzing a partial discharge test and in which health of a current standard heavy electric machine is diagnosed based on the pieces of data input in the operation (S100) of the inputting of the on-site diagnosis information;
an operation (S500) of analyzing trend-based health in which a predicted trend for each year is estimated in connection with the database built in the operation (S400) of the building of the database and the health is analyzed;
an operation (S600) of analyzing degradation prediction simulation in which a simulation model is generated and the health of the heavy electric machine is analyzed;
an operation (S700) of analyzing the pieces of online sensor data in which the health of the heavy electric machine is analyzed using the pieces of online sensor data collected in the operation (S200) of the collecting of the online sensor data; and
an operation (S800) of automatically determining a diagnosis result.

### Claim 11 (depends on 10)

The method of claim 10, wherein:
in the operation (S300) of the analyzing of the current standard health, the current standard health is analyzed using the pieces of data input in the operation (S100) of the inputting of the on-site diagnosis information;
in the operation (S500) of the analyzing of the trend-based health, the trend-based health is analyzed using the pieces of data input in the operation (S100) of the inputting of the on-site diagnosis and the pieces of data of the heavy electric machine which are the same type as those of the field diagnosed heavy electric machine; and
in the operation (S600) of the analyzing of the degradation prediction simulation, the degradation prediction simulation is analyzed using the pieces of data input in the operation (S100) of the inputting of the on-site diagnosis, the pieces of data of the heavy electric machine which are the same type as those of the field diagnosed heavy electric machine, and the pieces of data of the heavy electric machine of the same manufacturer as and a similar period to the field diagnosed heavy electric machine.

### Claim 12 (depends on 10)

The method of claim 10, wherein the operation (S200) of the collecting of the online sensor data includes:
an operation (S211) of classifying the pieces of collected sensor data in which the pieces of collected sensor data collected online are classified into data for each target heavy electric machine;
an operation (S212) of organizing time and/or daily trends in which the trends are organized so as to be analyzed by time and date;
an operation (S213) of inspecting a reference and an event in which values of the sensors are firstly determined and an event is detected; and
an operation (S214) of warning when an event occurs in which a warning is generated when the event occurs.

### Claim 13 (depends on 10)

The method of claim 10, wherein the database built in the operation (S400) of the building of the database includes a heavy electric machine specification database (510), an insulation diagnosis database (520), a failure history database (530), an online sensor database (540), and a health determination database (550).

### Claim 14 (depends on 10)

The method of claim 10, wherein the operation (S500) of the analyzing of the trend-based health includes:
an operation (S510) of classifying basic information of the heavy electric machine using the insulation diagnosis database;
an operation (S520) of extracting a measurement result of a target heavy electric machine;
an operation (S530) of reviewing a maintenance history;
an operation (S540) of estimating a target electric motor data parameter;
an operation (S550) of estimating the same type of heavy electric machine data parameters using the insulation diagnosis database;
an operation (S560) of estimating a machine learning-based parameter using the insulation diagnosis database;
an operation (S570) of verifying suitability of the corresponding parameter using the insulation diagnosis database;
an operation (S580) of estimating a predicted trend for each year; and
an operation (S590) of predicting degradation based on a current standard.

### Claim 15 (depends on 10)

The method of claim 10, wherein the operation (S600) of the analyzing of the degradation prediction simulation includes:
an operation (S610) of analyzing raw data of the heavy electric machine;
an operation (S620) of analyzing start and/or stop and event occurrence weight in which the pieces of online data collected in the operation (S200) of the collecting of the online sensor data are added;
an operation (S630) of generating a simulation model;
an operation (S640) of adjusting optimal values of parameters of the simulation model generated in the operation (S630) of the generating of the simulation model;
an operation (S650) of determining the simulation model in which the model is determined with the values adjusted in the operation (S640) of the adjusting of the optimal values of the parameter;
an operation (S660) of tracking a degradation relationship in which a relationship of the event is tracked based on the result values analyzed in the operation (S620) of the analyzing of start and/or stop and event occurrence weight;
an operation (S670) of estimating conditional failure probability in which failure probability is estimated using the simulation model determined in the operation (S650) of the determining of the simulation model;
an operation (S680) of building a machine learning-based degradation estimation modeling in which a machine learning-based degradation modeling is built; and
an operation (S690) of building a degradation prediction simulation in which a final simulation is completed using the simulation model determined in the operation (S650) of the determining of the simulation model and the machine learning-based degradation estimation modeling determined in the operation (S680) of the building of the machine learning-based degradation estimation modeling.

### Claim 16 (depends on 10)

The method of claim 10, wherein in the operation (S680) of the building of the machine learning-based degradation estimation modeling, modeling is formed in which each piece of individual data is expressed in a unit space (Mahalanobis space (MS)) based on a normal group center point and then it is determined that the pieces of data are normal or abnormal by measuring a unit distance (Mahalanobis distance (MD)) indicating how far the pieces of data are from the center point.

### Claim 17 (depends on 16)

The method of claim 16, wherein independent variables of the each piece of individual data include a value of an insulation resistance measured for one minute, a value of polarity index determination, a value of a polarity index, a value of dielectric loss tangent determination, a value of a dielectric loss tangent, a value of alternating current determination, a value of an alternating current, a value of partial high voltage determination, and a value of a partial discharge high voltage.

### Claim 18 (depends on 10)

The method of claim 10, wherein in the operation (S700) of the analyzing of the online sensor data, a value of a discharge pattern is extracted from the pieces of online sensor data collected in the operation (S200) of the collecting of the online sensor data and a risk and a cause of occurrence are estimated according to the discharge pattern.

### Claim 19 (depends on 10)

The method of claim 10, further comprising an operation (S900) of converting a determining result data for re-applying the result determined in the operation (S800) of the automatic determining of the diagnosis result to the operation (S400) of the building of the database.

### Claim 20 (depends on 19)

The method of claim 19, wherein the simulation model generated in the operation (S600) of the analyzing of the degradation prediction simulation is updated to a more sophisticated model by a model and data-driven approach using the data generated in the operation (S900) of the converting of the determining result data.