FAULT DETECTION IN ROTOR DRIVEN EQUIPMENT USING ROTATIONAL INVARIANT TRANSFORM OF SUB-SAMPLED 3-AXIS VIBRATIONAL DATA

• US 20160245686A1
• Filed: 12/22/2015
• Published: 08/25/2016
• Est. Priority Date: 02/23/2015
• Status: Abandoned Application

• Current Assignee: Machinesense LLC
• Original Assignee: Machinesense LLC
• Inventors: Bansal, Anshul, Dutta, Sneha, Karar, Pratyay, Boral, Soumya, Dey, Abhisek, Pal, Biplab
• Application Number:
  US14/977,675
• Publication Number:
  US 20160245686A1
• Time in Patent Office: Days
• US Class Current: 1/1
• CPC Class Codes: G01H 1/003 of rotating machines G01H1/10 takes precedence
• CPC Class Codes: G01M 13/045 Acoustic or vibration analysis

Abstract

A method and system of detecting faults in rotor driven equipment includes generating data from one or more vibration sensors communicatively coupled to the rotor driven equipment. The data from the one or more machine wearable sensors is collected onto a mobile data collector. The data is sampled at random to estimate a maximum value. Further, a sampling error may be controlled under a predefined value. The data may be analyzed through a combination of Cartesian to Spherical transformation, statistics of the entity extraction (such as variance of azimuthal angle), big data analytics engine and a machine learning engine. A fault is displayed on a user interface associated with the rotor driven equipment.

Claims

1. A method of detecting faults in a rotor driven equipment comprising:

generating multiple axis vibration data from one or more vibration sensors communicatively coupled to the rotor driven equipment;

collecting the data from the one or more machine wearable sensors onto a mobile data collector;

sampling, through a processor, the data at random to estimate a maximum value;

controlling a sampling error under a predefined value, wherein the sampling error is associated with the data;

analyzing the data through a combination of Cartesian to Spherical transformation, statistics of extracted entity of one or more spherical variables, big data analytics engine and a machine learning engine,

wherein the Cartesian to spherical transformation is to make vibrational vectors invariant; and

displaying on a user interface a fault associated with the rotor driven equipment.

2. The method of claim 1, further comprising determining the at least one rotor driven equipment issue based on one or more computations.

3. The method of claim 2, wherein a computation engine enables the one or more computations including at least one of a series of entity extraction of vibrational data, RMS, variance and kurtosis of azimuthal angle, peak to RMS ratio, percentiles ratio, ratio of variance of each individual vibration axis.

4. The method of claim 1, wherein the alarm is set through at least one of a rule based engine and a multi-classification machine learning engine.

5. The method of claim 1, wherein the user interface dynamic is a predictive maintenance circular gauge.

6. The method of claim 1,

wherein the rotor driven equipment issues include at least one of a belt tension, filter condition, abusive operation, oil level, and viscosity of oil;

wherein the issues are discovered through a machine learning multi-classification; and

wherein the machine learning multi-classification includes at least one of a neural network, random forest, logistical regression, and support vector machine (SVM).

7. The method of claim 1 wherein the communication network is one of Wi-Fi, 2G, 3G, 4G, GPRS, EDGE, Bluetooth, ZigBee, Piconet of BLE, Zwave or a combination thereof.

8. The method of claim 1, wherein the alarm is raised over the communication network through one of a notification on the mobile application, Short Message Service (SMS), email or a combination thereof.

9. A method of predicting rotor driven equipment issues, the method comprising:

collecting, through a processor, data associated with at least one machine wearable sensor associated with a rotor driven equipment;

transmitting the data collected at the at least one machine wearable sensor over a communication network to a mobile data collector,

wherein the data collected is over a finite time period and transmitted to a machine learning engine, and

wherein the machine learning engine is associated with a computer database hosting real time and historical data;

visualizing, through a processor, at least one rotor driven equipment issue based on an analysis through a combination of a big data engine and a machine learning engine;

indicating the at least one rotor driven equipment issue through a user interface dynamic; and

setting an alarm, through a processor, for the at least one rotor driven equipment issue.

10. The method of claim 9, further comprising of determining the at least one rotor driven equipment issue based on one or more computations.

11. The method of claim 10, wherein a computation engine enables the one or more computations.

12. The method of claim 9, wherein the alarm is set through at least one of a rule based engine and a multi-classification machine learning engine.

13. The method of claim 9, wherein the user interface dynamic is a predictive maintenance circular gauge.

14. The method of claim 9, wherein the rotor driven equipment issues include at least one of a belt tension, abusive operation, oil level, and oil state.

15. The method of claim 9, wherein the communication network is one of Wi-Fi, 2G, 3G, 4G, GPRS, EDGE, Bluetooth, ZigBee, Piconet of BLE, Zwave or a combination thereof.

16. The method of claim 9, wherein the alarm is raised over the communication network through one of a notification on the mobile application, Short Message Service (SMS), email or a combination thereof.