Method and system of a noise pattern data marketplace for a power station
Systems and methods for interactions with power station noise patterns are disclosed. A system can include a data collector communicatively coupled to a plurality of input channels, wherein at least one of the plurality of input channels is operatively coupled to a vibration detection facility structured to detect a noise pattern of a power station, a library structured to store the detected noise pattern, an interface circuit structured to make the noise pattern available to a noise pattern marketplace, the noise pattern marketplace including a plurality of noise patterns from a plurality of power stations; and a user interface for accessing at least one of the plurality noise patterns of the noise pattern marketplace.
1. A system, comprising:
a data collector communicatively coupled to a plurality of input channels, wherein at least one of the plurality of input channels is operatively coupled to a vibration detection facility structured to detect a noise pattern of a power station;
a library structured to store the detected noise pattern;
an interface circuit structured to make the noise pattern available to a noise pattern marketplace, the noise pattern marketplace comprising a plurality of noise patterns from a plurality of power stations; and
a user interface for accessing at least one of the plurality of noise patterns of the noise pattern marketplace;
wherein the noise pattern marketplace is a self-organizing marketplace organized based on a machine-learning self-organizing facility that learns based on a measure of marketplace success with respect to stored collected data, and
wherein the measure of marketplace success comprises at least one of: a profit measure, a yield measure, a rating, or an indicator of interest.
2. The system of claim 1 , wherein the noise pattern marketplace comprises at least one of a data pool or a data stream to provide the plurality of noise patterns to the noise pattern marketplace.
3. The system of claim 1 , wherein the at least one of the plurality of noise patterns comprises a characteristic representative of a power station performance parameter.
4. The system of claim 3 , wherein the power station performance parameter comprises at least one of a machine start-up category, a machine shut-down category, a normal machine operation category, or an operational failure mode category.
5. The system of claim 1 , wherein the user interface enables identification of a power station performance parameter based on a match between the detected noise pattern and the at least one of the plurality of noise patterns of the noise pattern marketplace.
6. The system of claim 1 , wherein the vibration detection facility is further structured to analyze frequency components to assist in detecting the noise pattern.
7. The system of claim 1 , wherein the noise pattern marketplace receives noise patterns from a plurality of industrial data collectors.
8. The system of claim 1 , wherein at least one parameter of the noise pattern marketplace is automatically configured by a machine learning facility based on a metric of success of the noise pattern marketplace.
9. The system of claim 1 , wherein the rating comprises at least one of: a user rating, a purchaser rating, a licensee rating, or a reviewer rating.
10. The system of claim 1 , wherein the indicator of interest comprises at least one of: a clickstream activity, a time spent on a page, a time spent reviewing elements, or a link to a data element.
11. The system of claim 1 , further comprising a rights management engine configured to manage permissions to access the noise pattern in the noise pattern marketplace.
12. The system of claim 1 , further comprising a data brokering engine configured to execute a data transaction among at least two noise pattern marketplace participants.
13. The system of claim 1 , further comprising a pricing engine configured to set a price for at least one data element within the noise pattern marketplace.
14. A method comprising:
detecting a noise pattern of a first power station;
analyzing the detected noise pattern to determine a match between the detected noise pattern of the first power station and a noise pattern of a second power station from a library of noise patterns;
if a match is determined, setting a power station performance parameter of the first power station to a specified power station performance parameter of the second power station, wherein the noise pattern of the second power station is characteristic of the specified power station performance parameter; and
receiving the library of noise patterns from a noise pattern marketplace, wherein the noise pattern marketplace is a self-organizing marketplace organized based on a machine-learning self-organizing facility that learns based on a measure of marketplace success with respect to stored collected data, and wherein the measure of marketplace success comprises at least one of: a profit measure, a yield measure, a rating, or an indicator of interest.
15. The method of claim 14 , further comprising setting an alarm based on the power station performance parameter of the first power station.
16. The method of claim 14 , wherein the power station performance parameter of the first power station comprises at least one of a machine start-up category, a machine shut-down category, a normal machine operation category, or an operational failure mode category.
17. The method of claim 14 , wherein detecting the noise pattern comprises at least one of: filtering an incoming signal, signal conditioning, spectral analysis, or trend analysis.
18. The method of claim 14 , further comprising isolating vibration noise of the first power station to obtain a vibration fingerprint of the first power station.
19. The method of claim 14 , wherein data of the noise pattern marketplace is organized into at least one of data batches, data streams, or data pools based on the measure of marketplace success.
20. A method comprising:
detecting a noise pattern of a power station;
storing the detected noise pattern in a library;
making the noise pattern available to a noise pattern marketplace, wherein the noise pattern marketplace includes a plurality of noise patterns from a plurality of power stations;
accessing at least one of the plurality of noise patterns of the noise pattern marketplace; and
receiving the library of noise patterns from the noise pattern marketplace, wherein the noise pattern marketplace is a self-organizing marketplace organized based on a machine-learning self-organizing facility that learns based on a measure of marketplace success with respect to stored collected data, and wherein the measure of marketplace success comprises at least one of: a profit measure, a yield measure, a rating, or an indicator of interest.
21. The method of claim 20 , wherein at least one parameter of the noise pattern marketplace is automatically configured by a machine learning facility based on a metric of success of the noise pattern marketplace.