METHODS AND SYSTEMS FOR PREDICTING ERRONEOUS BEHAVIOR OF AN ENERGY ASSET USING FOURIER BASED CLUSTERING TECHNIQUE

• US 20160267387A1
• Filed: 06/22/2015
• Published: 09/15/2016
• Est. Priority Date: 03/13/2015
• Status: Active Grant

• Current Assignee: Carrier Corporation (Carrier Global Corporation)
• Original Assignee: Wipro Limited
• Inventors: PRABHA, Baburaj Kaimalilputhenpura, Banerjee, Joy
• Granted Patent: US 10,163,062 B2
• Time in Patent Office: Days
• US Class Current: 1/1
• CPC Class Codes: G05B 23/0221 Preprocessing measurements, e.g. data collection rate adjustment; Standardization of measurements; Time series or signal analysis, e.g. frequency analysis or wavelets; Trustworthiness of measurements; Indexes therefor; Measurements using easily measured parameters to estimate parameters difficult to measure; Virtual sensor creation; De-noising; Sensor fusion; Unconventional preprocessing inherently present in specific fault detection methods like PCA-based methods
• CPC Class Codes: G06F 2218/08 Feature extraction
• CPC Class Codes: G06N 20/00 Machine learning
• CPC Class Codes: G06N 5/02 Knowledge representation; Symbolic representation
• CPC Class Codes: H02J 2203/20 Simulating, e g planning, reliability check, modelling or computer assisted design [CAD]
• CPC Class Codes: H02J 3/00 Circuit arrangements for ac mains or ac distribution networks

Abstract

This disclosure relates generally to predicting health of an energy asset, and more particularly to methods and systems for predicting erroneous behavior of an energy asset using fourier based clustering technique. In one embodiment, a method for determining predicting erroneous behavior of an energy asset is disclosed. The method includes creating one or more energy signatures by performing frequency domain analysis on historical energy data and subsequent clustering of the energy signatures. Further, live energy data is filtered to generate filtered outputs wherein each of the filtered outputs is mapped to a respective cluster. The outlier cluster is identified to predict the erroneous behavior of the energy asset

Claims

1. A method for predicting anomaly associated with at least one energy asset, the method comprising:

receiving, by a processor, time stamped historical energy data associated with the at least one energy asset;

creating, by the processor, one or more frequency components by performing frequency domain analysis on the time stamped historical energy data, each of the one or more frequency components indicative of time stamped energy values associated with the at least one energy asset;

clustering, by the processor, the one or more frequency components to generate one or more clusters based on similarity of time stamped energy values, each of the one or more clusters associated with at least one energy signature, the at least one energy signature being average of time stamped energy values for a cluster;

receiving time stamped energy data in real time from the at least one energy asset;

comparing, by the processor, between the time stamped energy data and the at least one energy signature associated with a cluster;

identifying, by the processor, the cluster comprising outlier data based on the comparison to predict anomaly associated with the at least one energy asset.

2. The method of claim 1, further comprising filtering, by one or more filters, the time stamped energy data through one or more filters to obtain one or more outputs, each of the one or more filters different from each other;

3. The method of claim 1, wherein each of the one or more clusters is associated with at least one operating state of the at least one energy asset.

4. The method of claim 3, wherein the at least one operating state comprise at least one of a:

normal working of the at least one energy asset, the energy asset on verge of malfunctioning, and actual malfunctioning of the at least one energy asset.

5. The method of claim 1, wherein the at least one energy asset comprise one or more power meters, one or more drives, one or more motors, one or more capacitor banks, one or more air compressors, one or more refrigerator units, one or more turbines, one or more generators, one or more energy storage devices, one or more photovoltaic cells, one or more robots, one or more reactors, or any combinations thereof.

6. The method of claim 1, further comprising notifying a user about the predicted anomaly associated with the at least one energy asset.

7. The method of claim 1, wherein comparing, by the processor, between the time stamped energy data and the at least one energy signature comprises:

computing Euclidean distance between the time stamped energy data and the at least one energy signature.

8. The method of claim 4, wherein the time stamped energy data having least Euclidean distance between the time stamped energy data and the at least one energy signature is mapped to the cluster associated with the at least one energy signature.

9. The method of claim 2, wherein the one or more filters comprise a plurality of low pass filters, band pass filters, and high pass filters.

10. The method of claim 9, wherein the one or more filters are digital filters.

11. A system for predicting anomaly associated with at least one energy asset, the system comprising:

a processor;

a memory storing instructions, that when executed by the processor, causes the processor to perform operations comprising;

receiving, by a processor, time stamped historical energy data associated with the at least one asset;

creating, by the processor, one or more frequency components by performing frequency domain analysis on the time stamped historical energy data, each of the one or more frequency components indicative of time stamped energy values associated with the at least one energy asset;

clustering, by the processor, the one or more frequency components to generate one or more clusters based on similarity of time stamped energy values, each of the one or more clusters associated with at least one energy signature, the at least one energy signature being average of time stamped energy values for a cluster;

receiving time stamped energy data in real time from the at least one energy asset;

comparing, by the processor, between the time stamped energy data and the at least one energy signature associated with a cluster;

identifying, by the processor, the cluster comprising outlier data based on the comparison to predict anomaly associated with the at least one energy asset.

12. The system of claim 11, further comprising one or more filters to filter the time stamped energy data through one or more filters to obtain one or more outputs, each of the one or more filters different from each other;

13. The system of claim 11, wherein each of the one or more clusters is associated with at least one operating state of the at least one energy asset.

14. The system of claim 13, wherein the at least one operating state comprise at least one of a:

normal working of the at least one energy asset, the energy asset on verge of malfunctioning, and actual malfunctioning of the at least one energy asset.

15. The system of claim 11, wherein the at least one energy asset comprise one or more power meters, one or more drives, one or more motors, one or more capacitor banks, one or more air compressors, one or more refrigerator units, one or more turbines, one or more generators, one or more energy storage devices, one or more photovoltaic cells, one or more robots, one or more reactors, or any combinations thereof.

16. The system of claim 11, further comprising notifying a user about the predicted anomaly associated with the at least one energy asset.

17. The system of claim 11, wherein comparing, by the processor, between the time stamped energy data and the at least one energy signature comprises:

computing Euclidean distance between the time stamped energy data and the at least one energy signature.

18. The system of claim 17, wherein the time stamped energy data having least Euclidean distance between the time stamped energy data and the at least one energy signature is mapped to the cluster associated with the at least one energy signature.

19. The system of claim 12, wherein the one or more filters comprise a plurality of low pass filters, band pass filters, and high pass filters.

20. The system of claim 19, wherein the one or more filters are digital filters.