US 11,899,442 B2
System and method for structural health monitoring using internet of things and machine learning
Pradit Kulshreshtha, Gurugram (IN)
Assigned to Livehooah Technologies Private Limited, Gurugram (IN)
Appl. No. 17/438,534
Filed by Livehooah Technologies Private Limited, Gurugram (IN)
PCT Filed Sep. 30, 2019, PCT No. PCT/IN2019/050722
§ 371(c)(1), (2) Date Sep. 13, 2021,
PCT Pub. No. WO2020/188585, PCT Pub. Date Sep. 24, 2020.
Claims priority of application No. 201911010335 (IN), filed on Mar. 16, 2019.
Prior Publication US 2022/0155773 A1, May 19, 2022
Int. Cl. G05B 23/02 (2006.01); G06N 20/00 (2019.01); H04W 84/18 (2009.01)
CPC G05B 23/0283 (2013.01) [G06N 20/00 (2019.01); H04W 84/18 (2013.01)] 20 Claims
OG exemplary drawing
 
1. A system for structural health monitoring using internet of things and machine learning, said system comprising:
at least one sensor (S) connected to at least one node processor (1, 1a, 1b, 1c, . . . 1n);
two or more types of gateways;
a communication module comprising a closed network communication module and an open network communication module;
a trigger function software client; and
a graphic user interface or dashboard,
wherein said system communicates between the sensor (S), the node processor (1, 1a, 1b, 1c, . . . 1n) and the two or more gateways through said communication module (C) to identify structural anomaly on real-time basis and to predict structural integrity of civil structures by evaluating feasibility of wireless structural health monitoring (SHM) of civil structures encompassing internet-of-things (TOT) and machine learning models,
wherein said two or more types of gateways comprising:
a first type of gateway, wherein the first type of gateway is a physical device gateway that communicates data wirelessly with the node processor via closed network communication module; and
a second type of gateway, wherein the second type of gateway is a cloud gateway that wirelessly authenticates and communicates data with the physical device gateway via open network communication module,
wherein
said node processor converts raw sensor data into engineering data and transfers said engineering data to said physical device gateway via said closed network communication module,
said physical device gateway aggregates and validates said engineered data to the cloud gateway through a secure communication protocol,
said trigger function software client evaluates the incoming real-time data on the cloud gateway and allows trigger function to route said engineering data to cloud storage and cloud analytics and alert depending upon predefined keywords or threshold on the cloud gateway,
said system being capable of connecting one or more machine learning models to help user identify structural behavior anomalies and forecast trends.