Monitoring systems and methods for electrical machines

Patent metadata

Field	Value
Publication number	US-2017168024-A1
Application number	US-201514969893-A
Country	US
Kind code	A1
Filing date	Dec 15, 2015
Priority date	Dec 15, 2015
Publication date	Jun 15, 2017
Grant date	—

Abstract

A monitoring system includes an acoustic emission monitoring system including acoustic emission sensors, a partial discharge monitoring system including partial discharge sensors and synchronized with the acoustic emission monitoring system, and a computer receiving acoustic emission data from the acoustic emission sensors and electrical data from the partial discharge sensors. The computer is configured to classify a first statistical event as a fatigue cracking event by pattern recognition of the acoustic emission data and determine a first location and a first damage condition resulting from the fatigue cracking event, classify a second statistical event as a partial discharge event by pattern recognition of the acoustic emission data or the electrical data, and fuse the acoustic emission data and the electrical data for the second statistical event and determine a second location and a second damage condition resulting from the partial discharge event. Methods of monitoring are also disclosed.

First claim

What is claimed is:

1 . A method of monitoring a system comprising:
synchronizing an acoustic emission monitoring system comprising a plurality of acoustic emission sensors with a partial discharge monitoring system comprising a plurality of partial discharge sensors; directing collection by the acoustic emission sensors of acoustic emission signals from the component as acoustic emission data and directing collection by the partial discharge sensors of electrical signals as electrical data; and classifying a statistical event as a fatigue cracking event or a partial discharge event by pattern recognition of the acoustic emission data and the electrical data.

2 . The method of claim 1 , wherein the statistical event comprises the partial discharge event, the method further comprising fusing the acoustic emission data and the electrical current data for the statistical event and determining a location and a damage condition resulting from the partial discharge event.

3 . The method of claim 1 , wherein the statistical event comprises the fatigue cracking event, the method comprising classifying the statistical event as the fatigue cracking event by pattern recognition of the acoustic emission data.

4 . The method of claim 3 further comprising determining a location and a damage condition of the fatigue cracking event from the acoustic emission data.

5 . The method of claim 1 wherein the system comprises an electric generator system.

6 . The method of claim 1 wherein the pattern recognition occurs in real time.

7 . The method of claim 6 further comprising implementing adaptive machine learning to enhance the pattern recognition.

8 . The method of claim 1 wherein the acoustic emission sensors comprise fiber optic acoustic emission sensors.

9 . A method of monitoring an electric generator system comprising:
directing collection by a plurality of first sensors on a component of the electric generator system of acoustic emission signals from the component as acoustic emission data; and classifying a first statistical event as a fatigue cracking event by pattern recognition of the acoustic emission data.

10 . The method of claim 9 further comprising determining a location and a damage condition resulting from the fatigue cracking event.

11 . The method of claim 9 wherein the first sensors comprise fiber optic acoustic emission sensors.

12 . The method of claim 9 further comprising:
synchronizing the first sensors with a plurality of second sensors;
directing collection by the second sensors of electrical signals as electrical data; and
confirming the fatigue cracking event by pattern recognition of the electrical data.

13 . The method of claim 9 further comprising directing collection by the first sensors of electrical signals from the component as electrical data.

14 . The method of claim 13 further comprising classifying a second statistical event as a fatigue cracking event or a partial discharge event by pattern recognition of the acoustic emission data and the electrical data.

15 . A monitoring system comprising:
an acoustic emission monitoring system comprising a plurality of acoustic emission sensors; a partial discharge monitoring system comprising a plurality of partial discharge sensors and synchronized with the acoustic emission monitoring system; and a computer receiving acoustic emission data from the acoustic emission sensors and electrical data from the partial discharge sensors, wherein the computer is configured to:
classify a first statistical event as a fatigue cracking event by pattern recognition of the acoustic emission data and determine a first location and a first damage condition resulting from the fatigue cracking event;
classify a second statistical event as a partial discharge event by pattern recognition of the acoustic emission data or the electrical data; and
fuse the acoustic emission data and the electrical data for the second statistical event and determine a second location and a second damage condition resulting from the partial discharge event.

16 . The monitoring system of claim 15 wherein the acoustic emission sensors collect the acoustic emission data from acoustic emission signals and the partial discharge sensors collect the electrical data from electrical signals from a component of an electric generator system.

17 . The monitoring system of claim 15 wherein the computer receives acoustic emission data from the acoustic emission sensors and electrical data from the partial discharge sensors in real time.

18 . The monitoring system of claim 15 wherein the partial discharge sensors are selected from the group consisting of ultra-high frequency sensors, high frequency current transformers, transient earth voltage sensors, coupling capacitors, and combinations thereof.

19 . The monitoring system of claim 15 wherein the acoustic emission sensors comprise fiber optic acoustic emission sensors.

20 . The monitoring system of claim 15 wherein the computer is configured to conduct the pattern recognition in real time.

Assignees

• Gen Electric

Inventors

• Dehghan Niri Ehsan
• Rose Curtis Wayne
• Witney Andrew Batton

Classifications

• G01N2291/0289

  Internal structure, e.g. defects, grain size, texture · CPC title

• G01R31/1209

  using acoustic measurements (acoustic measurements G01H3/00) · CPC title

• G01N2291/0258

  Structural degradation, e.g. fatigue of composites, ageing of oils · CPC title

• G01N29/2418

  using optoacoustic interaction with the material, e.g. laser radiation, photoacoustics (photoacoustic cells G01N21/1702; measuring characteristics of vibrations by using radiation-sensitive means G01H9/00; acousto-optical conversion techniques for short-range imaging G01S15/8965; sound-producing devices using laser bundle G10K15/046) · CPC title

• G06N5/047

  Pattern matching networks; Rete networks · CPC title