Method and system for predicting a maintenance period of equipment used in a facility

A computer system comprising:

• one or more processors;
• and
• a memory coupled to the one or more processors, the memory for storing instructions which, when executed by the one or more processors, cause the one or more processors to perform a method for predicting a maintenance time of a plurality of equipment used in a facility, the method comprising:
• receiving, at a facility management system, a sensor data from one or more sensors, wherein the one or more sensors is installed at the plurality of equipment, wherein the plurality of equipment is installed at various locations in the facility, wherein the sensor data is received in real time;
• collecting, at a cloud platform associated with the facility management system, the sensor data, wherein the cloud platform is associated with a server, wherein the sensor data is collected in real time;
• analysing, at a defect diagnostic engine associated with the facility management system, the sensor data for detection of one or more defects in the one or more sensors installed at the plurality of equipment by using machine learning algorithms, wherein the sensor data is analysed in real time;
• detecting, at the defect diagnostic engine, the one or more defects in the one or more sensors and the plurality of equipment installed at various locations in the facility based on the analysis of the sensor data, wherein the one or more defect is detected in real time;
• creating, at the defect diagnostic engine, a planned maintenance chart, wherein the planned maintenance chart is based on detection of the one or more defects and the analysis of the sensor data by using machine learning algorithms,
• wherein the defect diagnostic engine comprising a financial analyser and a longevity estimator, a probability of failure predictor, or both;
• and
• sending, at the defect diagnostic engine, the planned maintenance chart to a user with facilitation of a plurality of media devices, wherein the planned maintenance chart is sent to the user in real time.

A non-transitory computer-readable storage medium encoding computer executable instructions that, when executed by at least one processor, performs a method for predicting a maintenance time of a plurality of equipment used in a facility, the method comprising:

• receiving, at a computing device, a sensor data from one or more sensors, wherein the one or more sensors is installed at the plurality of equipment, wherein the plurality of equipment is installed at various locations in the facility, wherein the sensor data is received in real time;
• collecting, at a cloud platform associated with the computing device, the sensor data, wherein the cloud platform is associated with a server, wherein the sensor data is collected in real time;
• analysing, at a defect diagnostic engine associated with the computing device, the sensor data for detection of one or more defects in the one or more sensors installed at the plurality of equipment by using machine learning algorithms, wherein the sensor data is analysed in real time;
• detecting, at the computing device, the one or more defects in the one or more sensors and the plurality of equipment installed at various locations in the facility based on the analysis of the sensor data, wherein the one or more defect is detected in real time;
• creating, at the computing device, a planned maintenance chart, wherein the planned maintenance chart is based on detection of the one or more defects and the analysis of the sensor data by using machine learning algorithms,
• wherein the defect diagnostic engine comprising a financial analyser and a longevity estimator, a probability of failure predictor, or both;
• and
• sending, at the computing device, the planned maintenance chart to a user with facilitation of a plurality of media devices, wherein the planned maintenance chart is sent to the user in real time.