# US 10,481,195 B2 · Distributed IoT based sensor analytics for power line diagnosis

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- Number: US 10,481,195 B2
- Document ID: US10481195B2
- Status: Granted
- Kind: B2
- Application number: 14/956,403
- Filed: 2015-12-02
- Published: 2017-06-08
- Granted: 2019-11-19
- Earliest claimed: 2015-12-02
- Assignees: MachineSense, LLC
- Applicants: Prophecy Sensors, LLC
- Inventors: Biplab Pal; Utpal Manna; Maniruz Zaman
- Web page: https://patentgrep.com/patent/US10481195B2

## CPC

- G01R 31/088: {Aspects of digital computing}
- G06N 20/00: Machine learning
- G01R: MEASURING ELECTRIC VARIABLES; MEASURING MAGNETIC VARIABLES (indicating correct tuning of resonant circuits H03J3/12)
- G06N: COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS

## 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

### Claim 1

A method of predicting electrical line issues, the method comprising:
a) 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;
i) wherein the data collected is over a finite time period and transmitted to a machine learning engine, and
ii) wherein the machine learning engine is associated with a computer database hosting real time and historical data;
b) visualizing, through a processor, at least one electrical line issue based on an analysis through a big data engine;
c) determining the at least one electrical line issue based on one or more computations;
d) indicating the at least one electrical line issue through a user interface dynamic; and,
e) setting an alarm, through a processor, for the at least one electrical line issue;
wherein a computation engine enables the one or more computations;
wherein the alarm is set through at least one of a rule based engine and multi-classification machine learning engine;
wherein the user interface dynamic is a predictive maintenance circular gauge; and
wherein the electrical line issue includes at least one of swell, surge, harmonics, sag and flickering.

### Claim 2

A distributed power line diagnosis systenciin which at least one power line issue is determined based on one or more computations comprising;
a) a firmware to receive a plurality of electrical line data over a communications network;
b) a real time data processing system associated with distributed databases;
c) a local firmware board;
d) a data hub;
e) an IoT server;
f) a multi-classification machine learning engine associated with the IoT server;
g) a display module associated with one or more processors and user interface; wherein a power line issue is mapped onto a depiction on a user interfaces; 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
h) 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.

### Claim 3 (depends on 2)

The system of claim 2, wherein the IoT server further comprises a computation engine, operatively connected to the data processing system, the distributed databases, the data hub and the learning engine enabling one or more computations.

### Claim 4 (depends on 3)

The system of claim 3, wherein the distributed computation engine computes in one or more of:
i) the local firmware board;
ii) the data hub; and
iii) the IoT server.

### Claim 5 (depends on 4)

The system of claim 4, wherein the alarm is set through at least one of a rule based engine and a multi-classification machine learning engine;
wherein the depiction on the user interface is a predictive maintenance circular gauge;
wherein the communication network is one of Wi-Fi, 2G, 3G, 4G, GPRS, EDGE, Bluetooth, ZigBee, Piconet, Zwave, or a combination thereof;
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.

### Claim 6

A method of diagnosing distributed power line issues comprising:
a) receiving at least one electrical reading at a firmware board computation engine;
b) transmitting an output of firmware board computation engine to a data hub computation engine over a communication network;
c) 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;
d) determining at least one electrical line issue based on one or more computations on the big data server using the historical data;
e) visualizing at least one electrical line issue based on an analysis through the big data server;
f) indicating at least one electrical line issue through a user interface dynamic; and
g) 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;
wherein the alarm is set through at least one of a rule based engine and multi-classification machine learning engine;
wherein the user interface dynamic is a predicative maintenance circular gauge;
wherein the communications network is one of WiFi, 2G, 3G, 4G, GPRS, EDGE, Bluetooth, ZigBee, Piconet of BLE, Zwave or a combination thereof;
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.