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

a technology of vibrational data and rotor drive, which is applied in the direction of machine parts testing, structural/machine measurement, instruments, etc., and can solve problems such as controlling sampling errors

Inactive Publication Date: 2016-08-25
MACHINESENSE LLC
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  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Benefits of technology

[0015]In one aspect, the present invention relates to a method of detecting faults in rotor driven equipment, the method includes generating data from one or more machine wearable 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 big data analytics engine and a machine learning engine. A fault is displayed on a user interface associated with the rotor driven equipment.
[0016]In another aspect, the present invention relates to a method of predicting rotor driven equipment issues, the method comprises: collecting, through a processor, data associated with one or more machine wearable sensors associated with a rotor driven equipment; and transmitting the data collected at the one or more machine wearable sensors over a communication network to a mobile data collector. The data is collected over a finite time period and transmitted to a machine learning engine. The machine learning engine is associated with a computer database hosting real time and historical data. The method further includes visualizing, through a processor, one or more rotor driven equipment issue based on an analysis through a combination of a big data engine and a machine learning engine. Further, the method includes indicating the one or more rotor driven equipment issues through a user interface dynamic and setting an alarm, through a processor, for the one or more rotor driven equipment issue.

Problems solved by technology

Further, a sampling error may be controlled under a predefined value.

Method used

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

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Embodiment Construction

[0030]Example embodiments as described below may be used to provide a method, an apparatus and / or a system for fault detection in rotor driven equipment through big data analytics. Although the present embodiments have been described with reference to specific example embodiments, it will be evident that various modifications and changes may be made to these embodiments without departing from the broader spirit and scope of the various embodiments.

[0031]In one or more embodiments, vibrations of a fault free rotor in time domain may be represented by

x_j(t)=Σi=1∞αi*sin(i*Ω*t)|j=1,2,2

Ω is fundamental frequency and i, j, and k are three Cartesian vectors. In vibration analysis of a rotor, revolution per second of rotor may be multiplied by 2π.

[0032]Fundamental harmonics and other harmonics generated by a rotor may depend on the rotor's speed. Rotation speed of the rotor may commonly vary between 200-3000 RPM (Revolutions Per Minute). With such rotation speeds, the fundamental harmonics ...

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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.

Description

FIELD OF TECHNOLOGY[0001]The present invention generally relates to fault detection in rotor equipment. More specifically it relates to fault detection in rotor driven equipment based on a combination of low frequency vibration data, spherical transformation, machine learning and big data architecture.BACKGROUND[0002]Internet of Things (IoT) is a network of uniquely-identifiable, purposed “Things” that are enabled to communicate data pertaining thereto over a wide communication network, whereby the communicated data form a basis for manipulating the operation of the “Things”. The “Thing” in the Internet of Things could virtually be anything that fits into a common purpose thereof. For example, the “Thing” could be a person with a heart rate monitor implant, a farm animal with a biochip transponder, an automobile that has built-in sensors to alert its driver when tire pressure is low, or the like, or any other natural or man-made entity that can be assigned a unique IP address and pr...

Claims

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Application Information

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Patent Type & Authority Applications(United States)
IPC IPC(8): G01H1/00G01M13/04
CPCG01M13/045G01H1/003
Inventor PAL, BIPLABBANSAL, ANSHULDUTTA, SNEHAKARAR, PRATYAYBORAL, SOUMYADEY, ABHISEK
Owner MACHINESENSE LLC
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