# US 2024/0201680 A1 · Systems and methods for displaying renewable energy asset health risk information

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- Number: US 2024/0201680 A1
- Document ID: US20240201680A1
- Status: Published application
- Kind: A1
- Application number: 18/540,452
- Filed: 2023-12-14
- Published: 2024-06-20
- Earliest claimed: 2022-12-16
- Assignees: Utopus Insights, Inc.
- Applicants: Utopus Insights, Inc.
- Inventors: Kim Fluchel
- Web page: https://patentgrep.com/patent/US20240201680A1

## CPC

- G05B 23/0272: {Presentation of monitored results, e.g. selection of status reports to be displayed; Filtering information to the user}
- 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)}
- G06Q 10/06313: {Resource planning in a project environment}
- G06Q 10/20: Administration of product repair or maintenance
- G05B 2223/06: Remote monitoring
- 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)
- G06Q: INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR

## Abstract

An example method comprises receiving sensor data from multiple wind turbines. A wind turbine includes a gearbox, a generator, and multiple gearbox and generator subcomponents. Health indicators may be determined for the gearbox and generator subcomponents with varying lead times. The health indicators correspond to alerts for current or predicted problems of the gearbox and generator subcomponents and have either low severity, medium severity, or high severity risk levels. A machine learning model trained on sensor data may generate the alerts. The multiple wind turbines may be displayed in a list that may be sortable by health indicators for the gearbox subcomponents and the generator subcomponents. The list may be filterable by alerts for the gearbox subcomponents or the generator subcomponents.

## Claims

### Claim 1

A non-transitory computer readable medium comprising executable instructions, the executable instructions being executable by one or more processors to perform a method, the method comprising:
receiving first current sensor data of a first time period from multiple wind turbines in one or more wind turbine farms in one or more geographies, a wind turbine including a gearbox and a generator, the gearbox including a first gearbox bearing subcomponent, a gear set subcomponent, and a second gearbox bearing subcomponent, and the generator including a first generator bearing subcomponent, a rotor subcomponent, and a second generator bearing subcomponent; the first current sensor data including sensor data from sensors monitoring the gearbox subcomponents and the generator subcomponents;
determining health indicators for the gearbox subcomponents, the health indicators corresponding to alerts for current or predicted problems of the gearbox subcomponents with varying lead time, the alerts including a low severity risk alert, a medium severity risk alert, and a high severity risk alert, the alerts being generated by a machine learning model trained on second historical sensor data of a second time period, including sensor data from the gearbox subcomponents;
determining health indicators for the generator subcomponents, the health indicators corresponding to alerts for current or predicted problems of the generator subcomponents with varying lead time, the alerts including a low severity risk alert, a medium severity risk alert, and a high severity risk alert, the alerts being generated by a machine learning model trained on second historical sensor data of a second time period, including sensor data from sensors monitoring the generator subcomponents;
receiving the health indicators for the gearbox subcomponents and the health indicators for the generator subcomponents; and
displaying a list of the multiple wind turbines, the health indicators for the gearbox subcomponents and the health indicators for the generator subcomponents, the list being sortable by health indicators for the gearbox subcomponents and/or by the health indicators for the generator subcomponents, and the list being filterable by alerts for the gearbox subcomponents and/or alerts for the generator subcomponents.

### Claim 2 (depends on 1)

The non-transitory computer readable medium of claim 1, the method further comprises:
receiving a selection to filter the list of the multiple wind turbines by one or more alerts for at least one gearbox subcomponent and/or for at least one generator subcomponent;
filtering the list of the multiple wind turbines to include wind turbines with the selected one or more alerts for at least one gearbox subcomponent and/or for at least one generator subcomponent; and
displaying in the list wind turbines with the selected one or more alerts for at least one gearbox subcomponent and/or for at least one generator subcomponent.

### Claim 3 (depends on 1)

The non-transitory computer readable medium of claim 1, the method further comprises:
receiving a selection of a wind turbine, the wind turbine having a health indicator for a gearbox subcomponent or a generator subcomponent corresponding to either a low severity risk alert, a medium severity risk alert, or a high severity risk alert;
receiving alert status and date information for the gearbox subcomponent or the generator subcomponent of the wind turbine; and
displaying the health indicator, the alert status and the date information for the gearbox subcomponent or the generator subcomponent of the wind turbine.

### Claim 4 (depends on 3)

The non-transitory computer readable medium of claim 3, the method further comprises:
receiving a selection of the gearbox of the wind turbine;
determining an overall health indicator of the gearbox, the overall health indicator based at least in part upon any alerts for the gearbox subcomponents;
displaying the overall health indicator of the gearbox;
determining an overall health indicator of the generator, the overall health indicator based at least in part upon any alerts for the generator subcomponents; and
displaying the overall health indicator of the generator.

### Claim 5 (depends on 3)

The non-transitory computer readable medium of claim 3, the method further comprises:
receiving a request to initiate a work order for the gearbox subcomponent or the generator subcomponent of the wind turbine; and
sending the work order to a work order system.

### Claim 6 (depends on 3)

The non-transitory computer readable medium of claim 3, the method further comprises:
receiving completed service events for the wind turbine for a third historical time period the completed service events including completion date and service detail information;
receiving open service events for the wind turbine for a fourth future time period; the open service events including date and service detail information; and
displaying either the completed service events or the open service events.

### Claim 7 (depends on 3)

The non-transitory computer readable medium of claim 3, the method further comprises:
receiving a selection of the gearbox or the generator;
determining which gearbox subcomponents or generator subcomponents are monitored by sensors and which gearbox subcomponents or generator subcomponents are not monitored by sensors;
displaying a list of the gearbox subcomponents or generator subcomponents, for the gearbox subcomponents or generator subcomponents that are monitored by sensors, the health indicators for the gearbox subcomponents or generator subcomponents, and for the gearbox subcomponents or generator subcomponents that are not monitored by sensors, indications that the gearbox subcomponents or generator subcomponents are not monitored by sensors; and
displaying a cross-sectional outline view of the outline of the gearbox or generator, the outlines of the gearbox subcomponents or generator subcomponents, the health indicators of the gearbox subcomponents or generator subcomponents that are monitored by sensors, and the indications that the gearbox subcomponents or generator subcomponents are not monitored by sensors.

### Claim 8 (depends on 3)

The non-transitory computer readable medium of claim 3, the method further comprises:
determining a status of the selected wind turbine;
receiving the status of the wind turbine; and
displaying on a zoomable and scrollable map an icon for the selected wind turbine and an indication of the status of the selected wind turbine.

### Claim 9 (depends on 3)

The non-transitory computer readable medium of claim 3, the method further comprises:
receiving a selection of a gearbox subcomponent alert or a generator subcomponent alert;
receiving time series data for the selected gearbox subcomponent;
displaying the time series data in one or more data charts;
receiving a request to display one or more service events for the wind turbine; and
displaying the one or more service events overlaid on the time series data in the one or more data charts.

### Claim 10 (depends on 1)

The non-transitory computer readable medium of claim 1, the method further comprises:
receiving a request to analyze data for the wind turbine, the data including one or more of temperature data, signals data, CMS data, and vibration data;
receiving one or more of the temperature data, signals data, CMS data, and vibration data; and
displaying an analysis of the one or more temperature data, signals data, CMS data, and vibration data.

### Claim 11 (depends on 1)

The non-transitory computer readable medium of claim 1, the method further comprises:
receiving a request to display the multiple wind turbines in one or more wind turbine farms in one or more geographies in a map;
displaying a map of the multiple wind turbines in one or more wind turbine farms in one or more geographies;
receiving status, performance deviation and/or active alerts for the multiple wind turbines; and
displaying status, performance deviation and/or active alerts for the multiple wind turbines grouped by the one or more wind turbine farms.

### Claim 12

A system, comprising at least one processor; and memory containing instructions, the instructions being executable by the at least one processor to:
receive first current sensor data of a first time period from multiple wind turbines in one or more wind turbine farms in one or more geographies, a wind turbine including a gearbox and a generator, the gearbox including a first gearbox bearing subcomponent, a gear set subcomponent, and a second gearbox bearing subcomponent, and the generator including a first generator bearing subcomponent, a rotor subcomponent, and a second generator bearing subcomponent; the first current sensor data including sensor data from sensors monitoring the gearbox subcomponents and the generator subcomponents;
determine health indicators for the gearbox subcomponents, the health indicators corresponding to alerts for current or predicted problems of the gearbox subcomponents with varying lead time, the alerts including a low severity risk alert, a medium severity risk alert, and a high severity risk alert, the alerts being generated by a machine learning model trained on second historical sensor data of a second time period, including sensor data from the gearbox subcomponents;
determine health indicators for the generator subcomponents, the health indicators corresponding to alerts for current or predicted problems of the generator subcomponents with varying lead time, the alerts including a low severity risk alert, a medium severity risk alert, and a high severity risk alert, the alerts being generated by a machine learning model trained on second historical sensor data of a second time period, including sensor data from sensors monitoring the generator subcomponents;
receive the health indicators for the gearbox subcomponents and the health indicators for the generator subcomponents; and
display a list of the multiple wind turbines, the health indicators for the gearbox subcomponents and the health indicators for the generator subcomponents, the list being sortable by health indicators for the gearbox subcomponents and/or by the health indicators for the generator subcomponents, and the list being filterable by alerts for the gearbox subcomponents and/or alerts for the generator subcomponents.

### Claim 13 (depends on 12)

The system of claim 12, the instructions being further executable by the at least one processor to:
receive a selection to filter the list of the multiple wind turbines by one or more alerts for at least one gearbox subcomponent and/or for at least one generator subcomponent;
filter the list of the multiple wind turbines to include wind turbines with the selected one or more alerts for at least one gearbox subcomponent and/or for at least one generator subcomponent; and
display in the list wind turbines with the selected one or more alerts for at least one gearbox subcomponent and/or for at least one generator subcomponent.

### Claim 14 (depends on 12)

The system of claim 12, the instructions being further executable by the at least one processor to:
receive a selection of a wind turbine, the wind turbine having a health indicator for a gearbox subcomponent or a generator subcomponent corresponding to either a low severity risk alert, a medium severity risk alert, or a high severity risk alert;
receive alert status and date information for the gearbox subcomponent or the generator subcomponent of the wind turbine; and
display the health indicator, the alert status and the date information for the gearbox subcomponent or the generator subcomponent of the wind turbine.

### Claim 15 (depends on 14)

The system of claim 14, the instructions being further executable by the at least one processor to:
receive a selection of the gearbox of the wind turbine;
determine an overall health indicator of the gearbox, the overall health indicator based at least in part upon any alerts for the gearbox subcomponents;
display the overall health indicator of the gearbox;
determine an overall health indicator of the generator, the overall health indicator based at least in part upon any alerts for the generator subcomponents; and
display the overall health indicator of the generator.

### Claim 16 (depends on 14)

The system of claim 14, the instructions being further executable by the at least one processor to:
receive a request to initiate a work order for the gearbox subcomponent or the generator subcomponent of the wind turbine; and
send the work order to a work order system.

### Claim 17 (depends on 14)

The system of claim 14, the instructions being further executable by the at least one processor to:
receive completed service events for the wind turbine for a third historical time period the completed service events including completion date and service detail information;
receive open service events for the wind turbine for a fourth future time period; the open service events including schedule date and service detail information; and
display either the completed service events or the open service events.

### Claim 18 (depends on 14)

The system of claim 14, the instructions being further executable by the at least one processor to:
receive a selection of the gearbox or the generator;
determine which gearbox subcomponents or generator subcomponents are monitored by sensors and which gearbox subcomponents or generator subcomponents are not monitored by sensors;
display a list of the gearbox subcomponents or generator subcomponents, for the gearbox subcomponents or generator subcomponents that are monitored by sensors, the health indicators for the gearbox subcomponents or generator subcomponents, and for the gearbox subcomponents or generator subcomponents that are not monitored by sensors, indications that the gearbox subcomponents or generator subcomponents are not monitored by sensors; and
display a cross-sectional outline view of the outline of the gearbox or generator, the outlines of the gearbox subcomponents or generator subcomponents, the health indicators of the gearbox subcomponents or generator subcomponents that are monitored by sensors, and the indications that the gearbox subcomponents or generator subcomponents are not monitored by sensors.

### Claim 19 (depends on 14)

The system of claim 14, the instructions being further executable by the at least one processor to:
receive a request to display the multiple wind turbines in one or more wind turbine farms in one or more geographies in a map;
display a map of the multiple wind turbines in one or more wind turbine farms in one or more geographies;
receive status, performance deviation and/or active alerts for the multiple wind turbines; and
display status, performance deviation and/or active alerts for the multiple wind turbines grouped by the one or more wind turbine farms.

### Claim 20

A method comprising:
receiving first current sensor data of a first time period from multiple wind turbines in one or more wind turbine farms in one or more geographies, a wind turbine including a gearbox and a generator, the gearbox including a first gearbox bearing subcomponent, a gear set subcomponent, and a second gearbox bearing subcomponent, and the generator including a first generator bearing subcomponent, a rotor subcomponent, and a second generator bearing subcomponent, the first current sensor data including sensor data from sensors monitoring the gearbox subcomponents and the generator subcomponents;
determining health indicators for the gearbox subcomponents, the health indicators corresponding to alerts for current or predicted problems of the gearbox subcomponents with varying lead time, the alerts including a low severity risk alert, a medium severity risk alert, and a high severity risk alert, the alerts being generated by a machine learning model trained on second historical sensor data of a second time period, including sensor data from the gearbox subcomponents;
determining health indicators for the generator subcomponents, the health indicators corresponding to alerts for current or predicted problems of the generator subcomponents with varying lead time, the alerts including a low severity risk alert, a medium severity risk alert, and a high severity risk alert, the alerts being generated by a machine learning model trained on second historical sensor data of a second time period, including sensor data from sensors monitoring the generator subcomponents;
receiving the health indicators for the gearbox subcomponents and the health indicators for the generator subcomponents; and
displaying a list of the multiple wind turbines, the health indicators for the gearbox subcomponents and the health indicators for the generator subcomponents, the list being sortable by health indicators for the gearbox subcomponents and/or by the health indicators for the generator subcomponents, and the list being filterable by alerts for the gearbox subcomponents and/or alerts for the generator subcomponents.