A computer implemented method and system for determining reliability of a machine includes receiving one of a machine data from one or more locations through an internet of things (IOT) based machine wearable sensor network. The method further includes storing the data in a distributed computer database communicatively coupled to an enterprise resource planning (ERP) system and extracting, through a computer server, one or more entity information from the data to compare against a pre-defined baseline. Further, mapping, though a big data machine learning engine, the extracted one or more entity information into a multi-classification model. The method includes indicating, through a machine learning engine coupled to a predictive analytics engine, on a user interface a set of analytical predictions for machine maintenance, repair and operation.
Please help me write a proper abstract based on the patent claims. CLAIM: 1. A computer implemented method for determining reliability of a machine, comprising: receiving at least one of machine operational condition data, machine historical operational data and machine specific information data from at least one location through an internet of things (IOT) based machine wearable sensor network; storing the data in a distributed computer database communicatively coupled to an enterprise resource planning (ERP) system; extracting, through a computer server, at least one entity information from the data to compare against a pre-defined baseline; mapping, though a big data machine learning engine, the extracted at least one entity information into a multi-classification model, wherein the mapping includes: classifying the data into a root cause analysis engine, mapping the data into one or more levels of predictive maintenance (PM) states, wherein the PM states are associated with color schemes including one of a red, yellow and green; analyzing, through a real-time data feed platform associated with a distributed real-time computation system, the mapped at least one entity information; indicating, through a machine learning engine coupled to a predictive analytics engine, on a user interface a set of analytical predictions for machine maintenance, repair and operation. 2. The method of claim 1, wherein the user interface is associated with an application, 3. The method of claim 1, wherein the application is associated with at least one of a mobile phone, television, kiosk, touch screen device, and a server 4. The method of claim 1, wherein the application includes a fuel gauge associated with the color schemes. 5. The method of claim 1, wherein the color scheme red indicates a worst maintenance condition, yellow indicates an intermediate condition and green indicates a best maintenance condition. 6. The method of claim 1, wherein sensor data associated with the at least one of machine operational condition data, machine historical operational data and machine specific information is received from at least one machine wearable sensor placed on a machine part. 7. The method of claim 1, wherein the internet of things (IOT) sensor network is coupled to a communications network, wherein communications network between machine wearable sensor and a data collection unit, comprises one of WiFi, 2G, 3G, 4G, GPRS, EDGE, Bluetooth, ZigBee, Piconet of BLE, Zwave, or a combination thereof, and wherein the data collection unit is one of a mobile device and a wireless enabled device. 8. The method of claim 1 wherein, the data is received through Industrial bus networks including one of a Canbus, Profibus, Controlnet, Worldfip, Interbus and Fieldbus. 9. The method of claim 1, further comprising: scheduling, based on the set of analytical predictions, at least one of a maintenance, repair and operation. 10. The method of claim 1, wherein at least one of the maintenance and the repair is scheduled over the communications network through one of a notification on a mobile application, a Short message service (SMS), a telephone call, an email, or a combination thereof. 11. The method of claim 1, wherein the set of analytical predictions include at least one of a prediction of machine failure, mean time between failure, additional usage of electrical power, downtime of a machine, and trends of various parts in a machine failure. 12. A real-time IOT based system for determination of machine reliability comprising: one or more machine wearable sensors associated with a communications network; an internet of things (IOT) sensor network communicatively coupled to the communications network; an ERP system communicatively coupled to a distributed database over the communications network; a real-time data feed platform associated with a distributed real-time computation system communicatively coupled to the internet of things (IOT) sensor network over the communications network; and a big data machine learning engine coupled to a predictive analytics engine over the communications network, wherein the at least one of machine operational condition data, machine historical operational data and machine specific information data from at least one location is received through an internet of things (IOT) sensor network, wherein the data is stored in a distributed computer database communicatively coupled to an enterprise resource planning (ERP) system, wherein at least one entity information is extracted from the data to compare against a pre-defined baseline, wherein the at least one entity information is mapped onto a supervised multi-classification machine learning model; wherein the mapping includes at least one of: classifying the data into a root cause analysis engine, and mapping the data into one or more predictive maintenance (PM) states, wherein the PM states are associated with color schemes including one of a red, yellow and green, wherein the data is analyzed, through a real-time data feed platform associated with a distributed real-time computation system, and wherein a set of analytical predictions for machine maintenance is indicated, through a machine learning engine coupled to a predictive analytics engine, on a user interface for at least one of a repair, maintenance and operation of the machine. 13. The system of claim 12, wherein the indication on the user interface is through a color scheme of red, yellow and green associated with a normalized scale of 0-100. 14. The system of claim 12, wherein the PM states are represented on the user interface through a fuel gauge and wherein the color scheme red indicates a worst maintenance condition, yellow indicates an intermediate condition and green indicates a best maintenance condition hole gauge is divided into green, yellow and red zone 15. The system of claim 12, wherein sensor data associated with the at least one of machine operational condition data, machine historical operational data and machine specific information is received from at least one machine wearable sensor placed on a machine part. 16. The system of claim 12, wherein the internet of things (IOT) sensor network is coupled to a communications network, wherein communications network between machine wearable sensor and a data collection unit, comprises one of WiFi, 2G, 3G, 4G, GPRS, EDGE, Bluetooth, ZigBee, Piconet of BLE, Zwave, or a combination thereof, and wherein the data collection unit is one of a mobile device and a wireless enabled device. 17. The system of claim 12, wherein at least one of the maintenance and the repair is scheduled over the communications network through one of a notification on a mobile application, a Short message service (SMS), a telephone call, an email, or a combination thereof. 18. The system of claim 12, wherein the set of analytical predictions include at least one of a prediction of machine failure, days to turn red for a particular PM state, mean time between failure (MTBF), additional usage of electrical power, downtime of a machine, and trends of various parts in a machine failure.