# US 11,755,007 B2 · System and method for determining a health condition and an anomaly of an equipment using one or more sensors

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- Number: US 11,755,007 B2
- Document ID: US11755007B2
- Status: Granted
- Kind: B2
- Application number: 17/471,030
- Filed: 2019-10-25
- Published: 2022-09-29
- Granted: 2023-09-12
- Earliest claimed: 2019-05-20
- Assignees: Jef Techno Solutions, Pvt Ltd
- Applicants: Jef Techno Solutions Pvt Ltd
- Inventors: Prashanth Belur Gururaja Rao
- Web page: https://patentgrep.com/patent/US11755007B2

## CPC

- G05B 23/024: {Quantitative history assessment, e.g. mathematical relationships between available data; Functions therefor; Principal component analysis [PCA]; Partial least square [PLS]; Statistical classifiers, e.g. Bayesian networks, linear regression or correlation analysis; Neural networks}
- G05B 23/0283: {Predictive maintenance, e.g. involving the monitoring of a system and, based on the monitoring results, taking decisions on the maintenance schedule of the monitored system; Estimating remaining useful life [RUL] (preventive maintenance, i.e. planning maintenance according to the available resources without monitoring the system G06Q10/06)}
- G05B 23/0232: {based on qualitative trend analysis, e.g. system evolution}
- G05B 2219/24058: Remote testing, monitoring independent from normal control by pc
- G05B 2219/24097: Camera monitors controlled machine
- G05B: CONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS (systems for controlling or regulating non-electric variables G05D; systems for regulating electric or magnetic variables G05F; control devices or systems insofar as characterised by mechanical features only G05G)

## Abstract

A system for determining a health condition and an anomaly of a field equipment 104 is provided. The system includes sensors 106A-N which sense information associated with the equipment 104, a field device 110 which receives the sensor data from sensors 106A-N, a camera unit 108 that captures visual data of the equipment 104 and a server 112. The server 112 includes a database 114 that stores the sensor data and the visual data. The server 112 further includes a fault detection module 202 that processes the sensor data to determine a fault or the health condition of the equipment 104, an image processing module 204 that is trained to detect the irregularities/anomaly in the equipment 104 by processing the visual data, and a report generation module 206 that generates an automated health report 212 based on the detected anomaly and the health condition of the equipment 104.

## Claims

### Claim 1

A system for determining a health condition and an anomaly of an equipment (104), wherein the system comprises:
a plurality of sensors (106A-N) that is attached to the equipment (104), wherein the plurality of sensors (106A-N) senses information associated with a plurality of parameters of the equipment (104) to provide sensor data, wherein the sensor data comprises values of the plurality of parameters associated with the equipment (104);
a field device (110) that is communicatively connected to the plurality of sensors (106A-N), wherein the plurality of sensors (106A-N) is configured to wirelessly communicate the sensor data to the field device (110);
a camera unit (108) that captures visual data associated with the equipment being analyzed, wherein the visual data comprises a plurality of images and videos of the equipment (104) captured at different angles, wherein the camera unit (108) is configured to wirelessly communicate the captured visual data to the field device (110); and
a server (112) that is communicatively connected to the field device (110) for receiving the sensor data and the visual data associated with the equipment (104), wherein said server (112) comprises:
a database (114) that stores (i) the sensor data and (ii) the visual data; and
a processor that executes a machine learning model (410), wherein the processor performs
(i) determining, using the machine learning model (410), a fault or the health condition of the equipment (104) when the sensor data is provided as an input to the machine learning model (410), wherein the machine learning model (410) analyzes the sensor data and determines the fault or the health condition of the equipment (104) based on the analysis;
(ii) combining, using the machine learning model (410), the plurality of images of the equipment (104) to generate a combined photograph of the equipment (104) which provides a position of the equipment (104) in a physical world;
(iii) detecting, using the machine learning model (410), an anomaly in the equipment (104) when the combined photograph of the equipment (104) is provided as an input to the machine learning model (410), wherein the machine learning model (410) analyzes the combined photograph of the equipment (104) and determines the anomaly in the equipment (104) based on the analysis; and
(iv) automatically generating a health report (212) with the health condition of the equipment (104) and the anomaly detected in the equipment (104), wherein the health report (212) comprises (i) a location of the equipment (104), (ii) an observation based on the sensor data and the visual data, (iii) a recommendation to rectify the fault or the anomaly detected in the equipment (104), (iv) a criticality level of the fault or the anomaly detected in the equipment (104) and (v) a reference to industry standards.

### Claim 2 (depends on 1)

The system as in claimed in claim 1, wherein the machine learning model (410) processes the plurality of photographs to identify the position of the equipment (104) in the physical world.

### Claim 3 (depends on 1)

The system as claimed in claim 1, wherein the machine learning model (410) identifies a cause that is attributed to the anomaly, wherein the causes of the anomaly is attributed to a design of the equipment (104), installation of the equipment (104), maintenance of the equipment (104) and operating conditions of the equipment (104).

### Claim 4 (depends on 1)

The system as claimed in claim 1, wherein the processor communicates with the sensor data and the visual data stored in the database (114) and employs the machine learning module (410) to determine an anomaly if there is a fault, wherein the machine learning model (410) is trained by providing historical data comprising historical sensor data and historical visual data associated with the equipment (104) as training data for detecting the fault or anomaly in the equipment (104).

### Claim 5 (depends on 1)

The system as claimed in claim 1, wherein the health report (212) is communicated to the client device.

### Claim 6 (depends on 1)

The system as claimed in claim 1, wherein the field device (110) communicates (i) the sensor data and (ii) the visual data to the server (112) in a plurality of stages, wherein after each of the plurality of stages, the server (112) generates an alert to the field device, wherein the alert comprises data regarding consistency and relevance of (i) the sensor data and (ii) the visual data.

### Claim 7

A method for determining the health condition and an anomaly of equipment (104), wherein the method comprises the steps of
sensing, using a plurality of sensors (106A-N), information associated with a plurality of parameters of the equipment (104) to provide sensor data, wherein the plurality of sensors (106A-N) is attached to the equipment (104), wherein the sensor data comprises values of the plurality of parameters associated with the equipment (104);
communicating, using the plurality of sensors (106A-N), the sensor data to a field device (110), wherein the field device (110) is communicatively connected to the plurality of sensors (106A-N);
capturing, using a camera unit (108), visual data associated with the equipment being analysed, wherein the visual data comprises a plurality of images and videos of the equipment (104) captured at different angles, wherein the camera unit (108) is configured to wirelessly communicate the captured visual data to the field device (110) or a server (112);
generating a database (114), in said server (112), with (i) the sensor data and (ii) the visual data;
determining, using a machine learning model (410), a fault or the health condition of the equipment (104) when the sensor data is provided as an input to the machine learning model (410), wherein the machine learning model (410) analyzes the sensor data and determines the fault or the health condition of the equipment (104) based on the analysis;
combining, using the machine learning model (410), the plurality of photographs of the equipment (104) to generate a combined photograph of the equipment (104) which provides a position of the equipment (104) in a physical world;
detecting, using the machine learning model (410), an anomaly in the equipment (104) when the combined photograph of the equipment (104) is provided as an input to the machine learning model (410), wherein the machine learning model (410) analyzes the combined photograph of the equipment (104) and determines the anomaly in the equipment (104) based on the analysis; and
automatically generating, using the server (112), a health report (212) with the health condition of the equipment (104) and the anomaly detected in the equipment (104), wherein the health report (212) comprises (i) a location of the equipment (104), (ii) an observation based on the sensor data and the visual data, (iii) a recommendation to rectify the fault or the anomaly detected in the equipment (104), (iv) a criticality level of the fault or the anomaly detected in the equipment (104) and (v) a reference to industry standards.

### Claim 8 (depends on 7)

The method as claimed in claim 7, wherein the method comprises generating, using the server (112), a work order (210) when initiated by a client device, wherein the work order (210) comprises a request to determine a health condition of the equipment (104) and an anomaly in the equipment (104).

### Claim 9 (depends on 7)

The method as claimed in claim 7, wherein the method comprises tagging (i) the sensor data and (ii) the visual data with at least one of (i) a customer identifier, (ii) a work order (210) identifier, (iii) a location identifier, (iv) a facility identifier, (v) a floor identifier, (vi) an equipment (104) type, (vii) an anomaly type or (vii) an image identifier or a video identifier.

### Claim 10 (depends on 7)

The method as claimed in claim 7, wherein the method comprises communicating (i) the sensor data and (ii) the visual data to the server (112) in a plurality of stages from the field device (110), wherein after each of the plurality of stages, the server (112) generates an alert to the field device (110), wherein the alert comprises data regarding consistency and relevance of (i) the sensor data and (ii) the visual data.