Fault detection in rotor driven equipment using rotational invariant transform of sub-sampled 3-axis vibrational data

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.

What is claimed is: 
     
         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.