DETECTING FAULTS IN ROTOR DRIVEN EQUIPMENT
First Claim
1. A method of detecting faults in a rotor driven equipment comprising:
- a. generating multiple axis vibration data from one or more vibration sensors communicatively coupled to the rotor driven equipment;
b. collecting the data from the one or more machine wearable sensors onto a mobile data collector;
c. sampling, through a processor, the data at random to estimate a maximum value;
d. controlling a sampling error under a predefined value, wherein the sampling error is associated with the data;
e. analyzing the data through a combination of cartesian to spherical transformation making vibrational vectors invariant, statistics of extracted entity of one or more spherical variables, big data analytics engine and a machine learning engine; and
f. displaying on a user interface an alarm indicating a fault associated with the rotor driven equipment as determined by the data analysis performed in limitation “
e”
.
0 Assignments
0 Petitions
Accused Products
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.
0 Citations
8 Claims
-
1. A method of detecting faults in a rotor driven equipment comprising:
-
a. generating multiple axis vibration data from one or more vibration sensors communicatively coupled to the rotor driven equipment; b. collecting the data from the one or more machine wearable sensors onto a mobile data collector; c. sampling, through a processor, the data at random to estimate a maximum value; d. controlling a sampling error under a predefined value, wherein the sampling error is associated with the data; e. analyzing the data through a combination of cartesian to spherical transformation making vibrational vectors invariant, statistics of extracted entity of one or more spherical variables, big data analytics engine and a machine learning engine; and f. displaying on a user interface an alarm indicating a fault associated with the rotor driven equipment as determined by the data analysis performed in limitation “
e”
. - View Dependent Claims (2, 3, 4, 5, 6, 7, 8)
-
Specification