ts1860 developing a wind turbine condition monitoring system.pdf

7/29/2019 TS1860 Developing a Wind Turbine Condition Monitoring System.pdf

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A Systematic Approach to Wind Turbine

Prognostics & Health Management (PHM)

Mohamed AbuAli, Ph.D.

Postdoctoral Fellow

Center for Intelligent Maintenance Systems (IMS)

University of Cincinnati


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Introduction and Background

Smart Wind Turbine PHM Platform

Overall Approach

Global Health Estimator Local Damage Estimator

Turbine-to-Turbine Prognostics Technique for Wind Farms

Case Study

IMS Wind Turbine Fact Sheet

Outline


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By the end of 2010, the cumulativeinstalled capacity has risen to 40,180

MW [AWEA].

This represents 0.38% of thepotential wind capacity of10,459 GW

from the contiguous states of the

USA [DOE].

Survey of Wind Energy in the US

Market Share of

Wind Turbine Manufacturers in 2009


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Wind Turbine Criticality Analysis4-Quadrant Chart

Probability

of

Failure

Downtime Distribution

Rotor

Blades

Generator

Gearbox


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SURVEY OF

CRITICAL

WIND TURBINECOMPONENTS

GEARBOX

GENERATOR

ROTOR

ELECTRONICS

Bearing Faults

Gear Abrasion

Gear Eccentricity

Axle Misalignment

Stator Faults

Rotor Misalignment

Bearing Faults

Shorted Winding Coil

Short Circuit

Rotor Unbalance

Bearing Faults

Mass Imbalance

Aerodynamic Asymmetries

Surface Roughness

Overload

Thermo-mechanical Fatigue

ANN, BPNN

STFT / FFT / Envelope

Fuzzy Logic + PMP

Wavelet Analysis

Time Domain Analysis

Wavelet + FFT

Radial Basis NN

Time Series Tech.

Time Domain Analysis

Fuzzy Logic

Spectral Analysis

Order Analysis

Time-Domain Analysis

Thermal Analysis

COMPONENT FAULT TYPE ANALYSIS


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ISSUE 1: Lack of a performance metrics that compels maintenance activities.

Traditional CBM can track feature progression, but does not offer definitive informationwhen to initiate repair/replacement.

ISSUE 2: Dynamic wind turbine operating conditions and behavior.

Various operating states of wind turbine components, their environmental conditions,and their effect on system reliability need to be well studied.

Dynamic prognostics is needed for rotating wind turbine components working under

dynamic loads.

NEEDS - Reconfigurable and systematic PHM platforms are required toimprove overall wind turbine reliability.

Need for system-level (global) and component-level (local) prognostics.

Issues & Unmet Needs


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Smart Wind Turbine PHM Platform

Operating

Conditions:Wind Speed

Wind

Direction

Pitch Angle

Load

etc.

R2

R1

Condition

Data:Acceleration

Acoustic

Emission

Temperature

Oil Analysis

etc.

Signal Processing & Feature Extraction Fault Localization Fault Type Identification

Regime Density Estimation

Operating Regime IdentificationPerformance Prediction

Turbine Revenue Prediction

PerformanceAssessment

Output

Variable:Actual Output

Power

GLOBAL HEALTH ESTIMATOR

LOCAL DAMAGE ESTIMATOR

R2R1

InitiateMaintenanceW

orkOrder

SCADA

CMS

R2 R1


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It is based on the wind generation performance of a wind turbine.

The health information can be directly correlated with the wind production revenue.

The IMS approach is based on a multi-regime modeling health assessment that

considers the wind turbines dynamic operating conditions.

Global Health Estimator (GHE)


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Local Damage Estimator (LDE)CRITICAL COMPONENTS

FILTERING &

SIGNAL PROCESSING

3 4 5 6 7 8 92

3

4

5

6

7

8

9

Feature 1

Feature

2

Baseline

Current Data

PRINCIPAL COMPONENTS/

FEATURES

HEALTH ASSESSMENTFAULT LOCALIZATIONFAULT DIAGNOSIS


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PHM Data Challenge 2009 Gearbox Monitoring

Method 1A Systematic Approach for

Gearbox Health Assessment andFault Classification

Student Division

Winning Technique

Challenge: to determine for each

mechanical component, whether it is in

the healthy or fault state, and if it is inthe fault state, what problem is it

experiencing.

IMS Researchers won first and second

place. First place in both student and

professional division.

Method 2Information Reconstruction

Method

Professional Division

Winning Technique


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Gearbox Schematic and Description

Gear Components (4 Gears):

4 states (healthy, chipped tooth, manufacturing error, or broken tooth)

Bearing Components (6 Bearings): 4 states (healthy, inner race, outer race, ball defect)

Input Shaft:

3 states (healthy, imbalance, bent shaft)

Output Shaft:

2 States (healthy or bad key)


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Method 1 - Introduction

Signal Processing &

Feature Extraction

Regime Segmentation

Health Assessment

Fault Diagnosis

1. Features related to Overall Health: Mean and sum of spectral kurtosis features

RMS value from raw time signal (input and output

accelerometer).

Peak-to-peak level from TSA signal (input and

output accelerometer).

Energy operator from TSA signal (input and

output accelerometer).

Peaks for shaft related problems (10X, 15X, 20X).2. Features related to Shaft Problems:

Peak at 5X from input and output accelerometer

from TSA FFT.

3. Features related to Gear Problems: Mean, max and sum of a set of features related to

peaks corresponding to sidebands around gear

mesh frequency. Mean and sum of the set of features related to

sideband index and sideband level.

4. Features related to Bearing Problems: Mean of a set of features related to bearing fault

frequency peaks.

PHM Approach

Feature List


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Sample Diagnosis with Method 1

0 50 100 150 200 250 300 350 400-0.04

-0.03

-0.02

-0.01

0

0.01

0.02

0.03

0.04

0.05

0.06

Ouput Shaft Rotation (degree)

InputAccelerometer(g)

Vibration Time Synchronous Signal Input Accelerometer

File # 155 (Bent Shaft)

Signature of Broken Gear ToothSignature of Bent Shaft


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Method 2 - Introduction

Information Reconstruction Method


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Scheme of Reconstructing FFT spectrum

FFT spectrum of File-29 Reconstructed FFT spectrum of File-29

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Definition and Selection of Filter Functions

1717


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A holo-coefficients radar chart consists of all the energy coefficients.

In the map, the contribution rate of each coefficient can be revealed very

clearly along with its variation with operating conditions.

Holo-coefficients Radar Chart


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Patent filed on May 13, 2011 (International Application # PCT/US11/36402)

Inventors: Edzel Lapira, Hassan Al-Atat & Jay Lee

Turbine-to-Turbine Prognostics for Wind Farms (1)

Partitioning of

Operating

Regimes

Selection of

Best Unit

Model

Identification

Feature

SetWorking

Conditions

Similarity of

Operating

Regimes

Peer

Aggregation

Output

Variable

Model Prep

for Peers

Distance

Measurement

& Health

EstimationTesting

Training

Similarity to

Best Unit

Clustering

Best Unit(s) Selection

Peer-to-Peer

Comparison:

Degradation

Assessment

Modeling

1

2

3

Clustering Selection of Best

& Suspect Units

Peer-to-Peer

Comparison

1 2 3


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Turbine-to-Turbine Prognostics for Wind Farms (2)

Clustering1

Global

HealthEstimator

Selection of Best

& Suspect Units

2

Suspect Unit

Baseline Units

Local

Damage

EstimatorSuspect Unit

Peer-to-Peer

Comparison

3

Baseline Units

Fault

Localization

& Diagnosis


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Data was sampled every 10 minutes from a large scale, On-shore Wind

Turbine. The collected data spanned approximately 26 months (Jan. 1,

2008 to March 2, 2010). Within the duration of the data collection, there arefour downtime events (gray-shaded regions).

Data Description: Temperature (gearbox oil, gearbox bearing, gen slip ring, etc.)

Environmental Conditions (wind speed, wind direction, ambient temp, etc.)

Turbine Output Power (active power, reactive power, etc.)

Case Study - Power Performance Assessment


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Instance Filtering Data instances were removed when

active power is below 0 W

Sample points (blue dots), when

wind turbines pitch control

mechanism is engaged, areremoved (red asterisks).

Data Segmentation

Test data was divided into week long

intervals.

Data Pre-processing


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Health Assessment - SOM vs. GMM (1)

Method No. Multi-regime Modeling Baseline Comparison

1 Self-organizing Map (SOM) Minimum Quantization Error (MQE)2 Gaussian Mixture Model (GMM) 2 Distance3 Neural Network Analysis of Residues

Status 2

NormalStatus 1

Faulty

Status 3

Critical

Classification Health Assessment

Self-

Organizing

Maps


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Health Assessment - SOM vs. GMM (2)

Method No. Multi-regime Modeling Baseline Comparison

1 Self-organizing Map (SOM) Minimum Quantization Error (MQE)2 Gaussian Mixture Model (GMM) 2 Distance3 Neural Network Analysis of Residues

Gaussian

Mixture

Models

In feature space

Normal

Behavior

Most RecentBehavior

Calculate Health CV based on the

overlap of two distributions22

2

)()(

)()(

LL

L

xGxH

xGxHCV

)(xH )(xG

0 100 200 300 400 500 600-0.2

0

0.2

0.4

0.6

0.8

1

Gaussian 3

Gaussian 2Gaussian 1


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Results

SOM-MQE (lower) shows an abrupt increase of MQE distance.

More suitable for anomaly detection.

Self-

Organizing

Map (SOM)

Gaussian

MixtureModel (GMM)

GMM-L2 (upper) shows a more gradual degradation trend.

More suitable for degradation modeling.


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Wind Turbine PHM Platform - Simulation


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IMS Wind Turbine PHM - Factsheet1. Wind Turbine PHM Simulator

IMS has developed an NI LabVIEW-based wind turbine health monitoring simulator , based

on a real 2-MW onshore turbinehttp://www.imscenter.net/windturbinephm/WTPHMDemo

2. PHM Platform Validation on Off-shore Wind Farm

Global Health Estimator (GHE) and Local Damage Estimator (LDE) approach is going to be

validated using a fleet of off-shore wind turbines from a new IMS member company.

3. PHM Data Challenge 2011

IMS researchers places 1st, 3rd and 4th in the 2011 PHM Data Challenge Competition. The

task is anemometer health assessment for wind resource evaluation.

4. NREL Gearbox Reliability Collaborative (GRC)

IMS Center is one of only 19 partners (NI included) worldwide that participated in the NREL

Gearbox Reliability Collaborative (GRC) round robin with high ranking.

5. Turbine-to-Turbine (T2T) Intellectual Property

IMS has filed a non-provisional patent on Turbine-to-Turbine Prognostics for Wind Farms.
http://www.imscenter.net/windturbinephm/WTPHMDemohttp://www.imscenter.net/windturbinephm/WTPHMDemohttp://www.imscenter.net/windturbinephm/WTPHMDemo


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ts1860 developing a wind turbine condition monitoring system.pdf

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