VIBRATION BASED CONDITION MONITORING UNDER NON-STATIONARY CONDITIONS

• Stephan Heyns

• Professor and Director: C-AIM

• C-AIM University of Pretoria

• South Africa

Introduction

• Existing physical assets aging - operational life

pushed to new limits - financial & environmental.

• Increasingly complex new assets commissioned.

• Reliability Availability Maintenance Safety (RAMS)

requirement increasing.

• Need cost-effective, robust condition based

maintenance (CBM) strategies.

Wind turbine gearboxes

In recent years

wind turbines

have sparked

great interest

in monitoring

of complex

machinery

during non-

stationary

operations

Mining gearboxes

A B

E F G

C

H

D

Tooth damage

Dragline Bucket

Drag Cables

Hoist Cables

Mining gearboxes

• Expensive gearboxes

• Highly fluctuating load and

speed conditions

• Reversals in direction of

rotation

• Load variations tend to

cause amplitude modulation,

rotational speed frequency

modulation

Why CM under fluctuating conditions?

• Difficult to create comparable operational

conditions unless equipment is unloaded.

• Monitoring under actual loaded conditions more

likely to show defects.

• Conventional CM methodologies often violate

underlying mathematical assumptions and new

approaches are necessary.

Agenda

1. Fundamental concepts in non-stationary

vibration based condition monitoring.

2. Applications.

3. New developments in the Centre for Asset

Integrity Management.

4. Conclusions.

1. Fundamental concepts

Fundamental concepts in non-stationary vibration

based condition monitoring:

• Synchronous averaging

• Order tracking

• Load demodulation normalization

• Instantaneous angular speed

• Phase domain averaging

• Discrepancy signals

• Sensorless signal resampling

Synchronous averaging

• Extracting periodic signals from a composite signal is based on averaging signal sections of the period sought.

• This does require a priori knowledge of the frequency sought.

Encyclopedia of Vibration p 102

Order tracking

Sampling at constant sampling

frequency (normal frequency analysis)

8 samples per rev

Sampling at fixed number of

times per shaft revolution

Shock and Vibration Handbook by C.M. Harris

Spectral peak

smeared over

various lines

Spectrum one line

Load demodulation normalization

• Load demodulation normalization procedure account for modulation caused by fluctuating loads.

• Divide by load modulating envelope existing assumptions

Load demodulation normalization

• Envelope estimated as low pass filtered signal

maxima.

• Filter frequency optimized to ensure best

conformance between statistical properties of

signals measured under different load

conditions.

• Rotation domain averaging technique combines

ability of COT and time domain averaging to

suppress spectral smearing caused by speed

fluctuations and suppress amplitude of non-

synchronous vibrations.

Instantaneous angular speed

• IAS sensitive indicator of gear

condition

• IAS less susceptible to phase

distortions introduced by

transmission path compared to

gearbox casing vibration

measurements.

Phase domain averaging

• Reduce transmission path phase

distortion effects. Employ phase

domain averaging.

• Synchronous averaging with

regard to phase of reference

frequency.

Discrepancy analysis

Recently focus on low-cost condition monitoring

using empirical models such as:

• probability density functions and regression

functions

• to model complex machine response signals.

Special attention was given to discrepancy analysis

Discrepancy analysis

• Slide Models not data samples

Autoregressive filters

(linear steady state)

• Parallel adaptive AR filters

• SANC technique based on

LMS adaptive filter

• Schur filter

• Non-linear principal

components

• Likelihood measures based

on Gaussian mixture models

Neural network residual

envelopeTheo Heyns

Discrepancy transform

• Compare novel signal to reference signal(s) in piecewise manner

• Generate discrepancy transform which indicates where novel signal deviates from reference signal(s)

• Reference signal(s) representative of vibration response from healthy machine as subjected to different/fluctuating operating conditions

• Discrepancy signal expected to be smoother and simpler and subsequently less sensitive to frequency, amplitude and phase modulation.

Sensorless signal resampling

• Order tracking popular method to account for frequency modulation

• Due to physical and financial constraints not always possible to install an angular position reference sensor (e.g. tachometer)

• Different methodologies have been investigated where shaft speed/ angular position is directly estimated from signal itself

• Due to signal noise/ stochastic behaviour and cross frequency interference the angular position/speed estimates offer limited accuracy

2. Applications

Accelerated gearbox failure

ACC Fan gearbox

Accelerated gearbox failure

Accelerated test 17 h

Local damage shows after 8 h

Pinion fails not gear

Much earlier than frequency domain

Gear Damage: Gear machined down.

Regression model weighted average of

ensemble of AR models (range of

operating conditions – healthy state)

updated on statistical model selection

framework.

Discrepancy: Difference between one step

ahead prediction by non-linear AR filter

and observed response waveform

Structure (magnitude and periodicity) of

discrepancy signal contains diagnostic

information.

Kroch & Heyns

Methodology

• Transform complex (containing lost of information) vibration signal into smoothed discrepancy signal

• Estimate approximate shaft speed based on analysis of original vibration signal

• Resample discrepancy signal to account for most significant frequency modulation

• Analyse resampled discrepancy signal by means of synchronous averaging or spectrum.

Application: ACC fan gearbox

• ACC fans widely used to

facilitate indirect dry cooling

– 48 fans required per unit

– 288 fans for 6 unit power station

• Recurrent failures of multi-

stage gearboxes at specific

power station

– Same gearboxes intended for

use in 2 new power stations

– 2-year vibration monitoring

programme

Methodology

• Current power station– 9m diameter fan – Hot air recirculation– Gearbox with helical gears– Pinion gear failures– Gear pump failures

• Vibration monitoring of gearbox over lifespan– Started a couple of months

after installation– November 2011 – November

2013

24t68t

18t

Dri

ve

moto

r

Brg 5/6

79tBrg 5/6

Gear pump

Brg 3/4

Brg 3/4

Brg 1

Brg 2

To fan

Shaft 1

Shaft 2

Shaft 3

1500 RPM

120.6 RPM

Methodology

• Three adjacent gearboxes instrumented– Single axis accelerometer (radial)– Tachometer– Thermocouple

• Data acquisition with eDAQ lite data logger– 2500 Hz sample frequency

• Recurrent pinion gear failures

• Initially bearing diagnostics were ignored

Boil

er s

ide

HV

yar

d s

ide

Gea

rbox

#1

Gea

rbox

#2

Gea

rbox

#3

Accelerometer position &

orientation

Tachometer

Thermocouple

Inner shaft lower taper roller bearing

• Two defects on outer race

• Locations correspond to NLL peak positions

• Movement of bearing outer ring in gearbox

3. New developments in C-AIM

Fundamental concepts in non-stationary vibration

based condition monitoring:

• Synchronous averaging

• Order tracking

• Load demodulation normalization

• Instantaneous angular speed

• Phase domain averaging

• Discrepancy signals

• Sensorless signal resampling

Optical flow measurement

Localised phase information of the

image convolved with a complex filter.

Very small vibrations.

450 x smaller than single pixel from

video sequence.

20 Hz

225 FPS

450 mm standoff distance

167 micron/pixel image

resolution

Optical flow measurement

• Up to a resolution/amplitude ratio of 50, the optical flow method is better than 1% accurate

• Optical flow worst performance of 5% error

• Much more accurate than conventional DIC

• Viable for cost-effective structural vibration measurement with the advantage of full field vision!

Incremental shaft encoders

• Measure shaft angular velocity.

• Normally has several pulses per revolution, as

opposed to one pulse.

• Has potential to measure torsional vibration

(e.g. gearboxes) for diagnostics etc.

• Instantaneous Angular Speed can potentially

be more sensitive to gear faults than

acceleration measurements.

• But IAS usually difficult to measure. Good

quality encoders difficult to install and

expensive.

Low cost zebra tape encoder

Stationary Piezoelectric

probe

Shaft

Zebra tape

Geometric effect illustration

Stationary probe

Butt joint

Method assumptions

• For a shaft encoder with N encoder increments and M recorded revolutions.

• Assume that the angular velocity is linearly varying throughout each increment.

• Assume that the shaft velocity is continuous throughout all shaft increments.

• There are 2MN unknowns and MN equations in system. System is underdetermined and has infinitelymany solutions.

• Bayesian linear regression can be used to addprior information about the system parameters.

• Very promising results for varying speed conditions.

Blade tip timing for detecting

turbomachine blade problems

Bayesian statistics

Pressure measurementssensitive microphone

Blade tip timing for detecting

turbomachine blade problems

Blade tip timing

• The mill is the

Strain gauge – TelemetryBlade Tip TimingScanning LaserHigh Speed photography

Gwashavanhu & Diamond

Mill monitoring

Poorly performing milling can cause a range of secondary issues:

• Unburnt carbon in ash

• High NOx emissions

• Uneven steam temperatures

• High exit gas temperatures

Salzwedel

Fault detection and diagnosis

• Use machine learning to extract features from

data to predict and diagnose coal mill failures.

• Three main sources of data:

• Analogue process signals

• Binary event signals

• Mill notifications

Mill notifications

• Natural language descriptions of faults, events

logged by operators, maintenance personnel

and engineers.

• Dirty, but informative. There are many different

ways to describe the same fault.

• Often include spelling errors, abbreviations:

Semantic labelling of operator logs

• Refine mill

notification clustering

• Correlate notification

clusters to process

data

• Train a deep neural

network to classify

failures based on

process signals.

4. Conclusions

• Explained need for CM under non-stationary

conditions

• Introduced a number of very important concepts

in condition monitoring under non-stationary

conditions

• Shown a number of applications

• Introduced proposing new developments in the

C-AIM

Acknowledgements

I am pleased to acknowledge the critical research

contributions of the following C-AIM post-graduate students

and researchers:

• Dr Corné Stander

• Berndt Eggers

• Dr Abrie Oberholster

• Dawie Diamond

• Benji Gwashavanhu

• Robert Salzwedel

and the Eskom Power Plant Industry Programme (EPPEI)