FUEL GAUGE VISUALIZATION OF IOT BASED PREDICTIVE MAINTENANCE SYSTEM USING MULTI-CLASSIFICATION BASED MACHINE LEARNING

• US 20160313216A1
• Filed: 07/02/2015
• Published: 10/27/2016
• Est. Priority Date: 04/25/2015
• Status: Abandoned Application

• Current Assignee: Machinesense LLC
• Original Assignee: Prophecy Sensorlytics Llc
• Inventors: Pal, Biplab, Purohit, Amit
• Application Number:
  US14/790,084
• Publication Number:
  US 20160313216A1
• Time in Patent Office: Days
• US Class Current: 1/1
• CPC Class Codes: B23Q 17/0971 by measuring mechanical vibrations of parts of the machine arrangements for measuring vibrations B23Q17/12
• CPC Class Codes: B65G 53/66 Use of indicator or control devices, e.g. for controlling gas pressure, for controlling proportions of material and gas, for indicating or preventing jamming of material controlling the flow of coal firing systems C21B5/003
• CPC Class Codes: G01D 7/005 Indication of measured value by colour change
• CPC Class Codes: G05B 19/0428 Safety, monitoring G05B19/0423 takes precedence
• CPC Class Codes: G05B 19/4184 characterised by fault tolerance, reliability of production system
• CPC Class Codes: G06F 11/0709 in a distributed system consisting of a plurality of standalone computer nodes, e.g. clusters, client-server systems
• CPC Class Codes: G16Y 40/40 Maintenance of things

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.

Claims

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.

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.

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.

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.