DISTRIBUTED IOT BASED SENSOR ANALYTICS FOR POWER LINE DIAGNOSIS

• US 20170160328A1
• Filed: 12/02/2015
• Published: 06/08/2017
• Est. Priority Date: 12/02/2015
• Status: Active Grant

• Current Assignee: Machinesense LLC
• Original Assignee: Prophecy Sensorlytics Llc
• Inventors: Pal, Biplab, Manna, Utpal, Zaman, Maniruz
• Granted Patent: US 10,481,195 B2
• Time in Patent Office: Days
• CPC Class Codes: G01R 31/088 Aspects of digital computing
• CPC Class Codes: G06N 20/00 Machine learning

Abstract

A method and system of a distributed power line diagnosis includes one or more electrical readings received at a firmware board computation engine and an output of firmware board engine computation being transmitted through a wide communication network to a data hub computation engine. An output of the data hub computation engine is transmitted through the communication network to a big data server. One or more electrical line issues are visualized based on an analysis through the big data server and the same can be indicated through a user interface dynamic and an alarm can be set as well through the processor for the one or more electrical line issues.

Claims

1. A method of predicting electrical line issues, the method comprising of

collecting, through a processor, data associated with at least one electrical line reading from one or more sensors capable of local computation of time series data of electrical sensors transmitted over a communication network,

wherein the data collected is over a finite time period and transmitted to a machine learning engine, and

wherein the machine learning engine is associated with a computer database hosting real time and historical data;

visualizing, through a processor, at least one electrical line issue based on an analysis through a big data engine;

indicating the at least one electrical line issue through a user interface dynamic; and

setting an alarm, through a processor, for the at least one electrical line issue.

2. The method of claim 1, further comprising of determining the at least one electrical line issue based on one or more computations.

3. The method of claim 2, wherein a computation engine enables the one or more computations.

4. The method of claim 1 wherein, the alarm is set through at least one of a rule based engine and a multi-classification machine learning engine.

5. The method of claim 1, wherein the user interface dynamic is a predictive maintenance circular gauge.

6. The method of claim 1, wherein the electrical line issues include at least one of a swell, surge, harmonics, sag and flickering.

7. A distributed power line diagnosis system comprising:

i) a firmware to receive a plurality of electrical line data over a communications network;

ii) a real time data processing system associated with distributed databases;

iii) a local firmware board;

iv) a data hub;

v) an IoT server;

vi) a multi-classification machine learning engine associated with the IoT server;

vii) a display module associated with one or more processors and a user interface, wherein a power line issue is mapped onto a depiction on a user interface, and wherein the power line issue is determined based on a computation through at least one of the local firmware board, the data hub and the IoT server; and

viii) an alarm module to raise an alarm when a pre-set condition is breached, wherein the alarm module is associated with the multi-classification machine learning engine.

8. The system of claim 7, wherein at least one power line issue is determined based on one or more computations.

9. The system of claim 8, wherein a computation engine enables the one or more computations.

10. The system of claim 9, wherein the distributed computation engine is computing in one or more of:

i) the local firmware board;

ii) the data hub; and

iii) the IoT server.

11. The system of claim 7, wherein the alarm is set through at least one of a rule based engine and a multi-classification machine learning engine.

12. The system of claim 7, wherein the depiction on the user interface is a predictive maintenance circular gauge.

13. The system of claim 7, wherein the communication network is one of Wi-Fi, 2G, 3G, 4G, GPRS, EDGE, Bluetooth, ZigBee, Piconet of BLE, Zwave or a combination thereof.

14. The system of claim 7, wherein the alarm is raised over the communication network through one of a notification on the mobile application, Short Message Service (SMS), email or a combination thereof.

15. A method of a distributed power line diagnosis comprising:

receiving at least one electrical reading at a firmware board computation engine;

transmitting an output of firmware board computation engine to a data hub computation engine over a communication network;

transmitting an output of the data hub computation engine to a big data server, wherein a machine learning engine is associated with at least one of the big data server and a computer database hosting historical data;

visualizing at least one electrical line issue based on an analysis through the big data server;

indicating at least one electrical line issue through a user interface dynamic; and

setting, through a processor, an alarm for at least one electrical line issue through at least one of a rule based engine and a multi-classification machine learning engine.

16. The method of claim 15, further comprises determination of at least one electrical line issue based on one or more computations.

17. The method of claim 15, wherein the alarm is set through at least one of a rule based engine and a multi-classification machine learning engine.

18. The method of claim 15, wherein the user interface dynamic is a predictive maintenance circular gauge.

19. The method of claim 15, wherein the communications network is one of WiFi, 2G, 3G, 4G, GPRS, EDGE, Bluetooth, ZigBee, Piconet of BLE, Zwaveor a combination thereof.

20. The method of claim 15, wherein the alarm is raised over the communication network through one of a notification on the mobile application, Short Message Service (SMS), email or a combination thereof.