System and method for determining a health condition and an anomaly of an equipment using one or more sensors

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.