Fuel gauge visualization of iot based predictive maintenance system using multi-classification based machine learning

Abstract

A method and system of a predictive maintenance IoT system comprises receiving a plurality of sensor data over a communications network and determining one or more clusters from the sensor data based on a pre-determined rule set. Further, the sensor data is classified through a machine learning engine and the sensor data is further base-lined through a combination of database architecture, data training architecture, and a base-lining algorithm. Intensity or degree of fault state is mapped to a fuel gauge to be depicted on a user interface and a predictive maintenance state is predicted through a regression model and appropriate alarm is raised for user action.

What is claimed is: 
     
         1 . A method of a predictive maintenance IoT system comprising:
 receiving, at a predictive maintenance IoT system, a plurality of sensor data over a communications network;   determining one or more clusters from the sensor data based on a pre-determined rule set;   classifying the sensor data through a machine learning engine;   base-lining the sensor data through a combination of database architecture, data training architecture, and a base-lining algorithm;   visualizing the machine state on a fuel gauge representation based on a predictive maintenance state calculated through a user regression model.   
     
     
         2 . The method of  claim 1 , wherein the machine learning engine is associated with at least one of a physics based model, a rule based model and a vector classifier model. 
     
     
         3 . The method of  claim 1 , wherein data training architecture receives as input, one of a baseline reading and anomalous reading for a component with a sensor attached. 
     
     
         4 . The method of  claim 1 ,
 wherein the fuel gauge is associated with color schemes,
 wherein the color scheme:
 red indicates a worst maintenance condition, 
 yellow indicates an intermediate condition, and 
 green indicates a best maintenance condition. 
 
   
     
     
         5 . The method of  claim 1 , wherein the sensor data is received from a vacuum conveying system. 
     
     
         6 . The method of  claim 1 , wherein a user defined alarm system is set based on a field calibration of the fuel gauge representation. 
     
     
         7 . The method of  claim 1 , further comprising:
 raising an alarm when color scheme is at least one of the yellow and the red; and   receiving sensor data from at least one machine wearable sensor.   
     
     
         8 . The method of  claim 1 , wherein the communications network is one of WiFi, 2G, 3G, 4G, GPRS, EDGE, Bluetooth, ZigBee, Piconet of BLE, Zwave, or a combination thereof. 
     
     
         9 . The method of  claim 1 ,
 wherein the sensor data is at least one of a vibration, magnetic field, power factor, and temperature.   
     
     
         10 . The method of  claim 1 , further comprising:
 wherein at least one of a mobile, a web and a desktop application acts as a mobile middleware, and   wherein the mobile middleware calibrates and base-lines the sensor data.   
     
     
         11 . The method of  claim 5 , wherein the alarm is raised over the communications network through one of a notification on the mobile application, Short message service (SMS), email, or a combination thereof. 
     
     
         12 . A predictive maintenance IoT system comprising:
 a mobile middleware to receive a plurality of sensor data over a communications network;   a real time data processing system associated with distributed databases;   a clustering module to determine one or more clusters from the sensor data based on a pre-determined rule set;   a computer database to store the pre-determined rule set;   a machine learning engine to classify the sensor data;   a base-lining architecture to base-line the sensor data,
 wherein the base-lining architecture is a combination of database architecture, data training architecture, and a base-lining algorithm; and 
   a regression module associated with a processor to predict a predictive maintenance state,   wherein the predictive maintenance state is mapped onto a depiction on a user interface.   
     
     
         13 . The system of  claim 10 , wherein the machine learning engine is associated with at least one of a physics based model, a rule based model and a vector classifier model. 
     
     
         14 . The system of  claim 10 , wherein the data training architecture receives as input, one of a baseline reading and anomalous reading from a component associated with a sensor. 
     
     
         15 . The system of  claim 12 ,
 wherein the depiction on a user interface is a fuel gauge, and   wherein the fuel gauge is associated with color schemes,
 wherein the color scheme:
 red indicates a worst maintenance condition, 
 yellow indicates an intermediate condition, and 
 green indicates a best maintenance condition. 
 
   
     
     
         16 . The system of  claim 12 , further comprising:
 raising an alarm when color scheme is at least one of the yellow and the red; and   receiving sensor data from at least one machine wearable sensor.   
     
     
         17 . The system of  claim 12 , wherein the communications network is one of WiFi, 2G, 3G, 4G, GPRS, EDGE, Bluetooth, ZigBee, Piconet of BLE, Zwave, or a combination thereof. 
     
     
         18 . The system of  claim 12 ,
 wherein the sensor data is at least one of a vibration, magnetic field, power factor, and a temperature.   
     
     
         19 . The system of  claim 12 ,
 wherein at least one of a mobile, a web and a desktop application acts as a mobile middleware, and   wherein the mobile middleware calibrates and base-lines the sensor data.   
     
     
         20 . The system of  claim 15 , wherein the alarm is raised over the communications network through one of a notification on the mobile application, Short message service (SMS), email, or a combination thereof.