Tools for Advanced Sound & Vibration Analysis - NIindia.ni.com/sites/default/files/Tools for...

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ni.com

Tools for Advanced Sound & Vibration Analysis

ni.com

Ravichandran Raghavan

Technical Marketing Engineer

Agenda

• NI Sound and Vibration Measurement Suite

• Advanced Signal Processing Algorithms

• Time-Frequency Analysis

• Quefrency and Cepstrum

• Wavelet Analysis

• AR Modeling

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AR Modeling

• Application Examples

• Bearing fault detection, dashboard motor testing, speaker

testing, …

• Prognostics

NI Sound and Vibration Measurement Suite

• Minimize development time with ready-to-run application examples

• Get started quickly with the Sound and Vibration Assistant (LabVIEW not required)

• Build custom data acquisition systems faster than ever with DAQ configuration

XControl

• Avoid the expense of verification with NI ANSI- and IEC-compliant octave and

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sound-quality analysis

• Decrease test time with parallel processing

Sound and Vibration Signals

• Can indicate the condition or quality of machines and

structures

• Cooling fans with faulty bearings produce louder noise

• You can analyze sound and vibration signals to

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• You can analyze sound and vibration signals to

• Optimize a design

• Ensure production quality

• Monitor machine or structure conditions

Signal Characteristics PlaneFre

quency

and Long time but narrow band

Short time

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quency

Short time

& narrow band

Signal Processing Algorithms Overview

• Time Domain

• Frequency Domain

• Time-Frequency Domain

• Quefrency Domain (Cepstrum)

Wavelet

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• Wavelet

• Model-Based

How to Select the Right AlgorithmsFrequencyFrequencyFrequencyFrequency

AnalysisAnalysisAnalysisAnalysis

Order Order Order Order

TimeTimeTimeTime----

Frequency Frequency Frequency Frequency

QuefrencyQuefrencyQuefrencyQuefrency

Wavelet Wavelet Wavelet Wavelet

ModelModelModelModel

BasedBasedBasedBased

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Select the Right AlgorithmsFrequencyFrequencyFrequencyFrequency

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Limitations of the FFT

• No information about how frequencies evolve over time

• Not suitable for analyzing impulsive signals

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Power Spectrum

• A power spectrum does not contain time

information

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Transients

• It is difficult to detect presence of transients in a

signal by its power spectrum

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Time-Frequency Analysis

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• The short-time Fourier transform (STFTSTFTSTFTSTFT) is the

most popular time-frequency analysis algorithm

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Advantages of Time-Frequency Analysis

• Time-frequency representation shows how frequency

components of a signal evolve over time

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Reversed in time domain

Application Example: Speaker Test

• Speakers play a log chirp for quality test

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Select the Right Algorithms

FrequencyFrequencyFrequencyFrequency

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Quefrency Analysis

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Cepstrum and Quefrency

Cepstrum is the spectrum of a decibel spectrum• Quefrency is the independent variable of cepstrum

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Cepstrum Property

• The cepstrum reveals the periodicity of a spectrum

• A peak in the cepstrum corresponds to harmonics in power

spectrum

RahmonicsRahmonicsRahmonicsRahmonics

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10Hz10Hz10Hz10Hz harmonicsharmonicsharmonicsharmonics A peak at A peak at A peak at A peak at 0.1s0.1s0.1s0.1s quefrencyquefrencyquefrencyquefrency

RahmonicsRahmonicsRahmonicsRahmonics

Cepstrum Property Cont.

13Hz harmonics13Hz harmonics13Hz harmonics13Hz harmonics 10Hz harmonics10Hz harmonics10Hz harmonics10Hz harmonics

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10Hz 10Hz 10Hz 10Hz andandandand 13Hz13Hz13Hz13Hz harmonicsharmonicsharmonicsharmonics

1/10 = 0.1s1/10 = 0.1s1/10 = 0.1s1/10 = 0.1s

1/13 = 0.078s1/13 = 0.078s1/13 = 0.078s1/13 = 0.078s

Application Example:

Bearing Fault Detection

• Use a cepstrum to detect a bearing faultbearing faultbearing faultbearing fault

−= )cos(1

outerD

Characteristic frequency for an outerouterouterouter ring fault of a

bearing

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−= )cos(1

outerD

+= )cos(1

BBinner

Characteristic frequency for an innerinnerinnerinner ring fault of a

bearing

BN : Number of balls

f : Rotation frequency

CD : Retainer diameter

: Ball diameter α : Ball contact

Bearing Fault Detection Example

7=BN Hzf 30= mmDB 10=mmDC 70= 0=α

• Geometry parameters of the bearings under test are:

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BBouter 900.3)cos(1

−= α

BBinner 1200.4)cos(1

• Characteristic frequencies of the bearings are:

OuterOuterOuterOuter ring fault –

Inner Inner Inner Inner ring fault –

Harmonics of Bearing Signals

• Use harmonics to detect bearing faults

• The outerouterouterouter ring fault signal has

harmonics of 90Hz90Hz90Hz90Hz

• The innerinnerinnerinner ring fault signal has

harmonics of 120Hz120Hz120Hz120Hz

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Cepstrum of Bearing Signals

• A peak in the cepstrum means harmonics exist in the

power spectrum

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Wavelet Analysis

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Wavelet vs Sine Wave

• Wavelet = WaveWaveWaveWave (Oscillatory ) + letletletlet (Compact)

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Wavelet Transform: Look at the FFT First

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Wavelet Transform

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Dashboard Motor Production Test

• A dashboard motor is a stepper motor that has an angle

constraint

• Oil pressure, tachometers, and speedometers use dashboard

motors

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Dead zone

Dashboard Motor Faults

• There are two kinds of faults

• Fault 1 – Knock at turning angles

• Fault 2 – Rub noise

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Good Motor Knocks Larger Knocks

and Rub

Why do Wavelets Work?

• Knocks generate spikes and resonance

• Spikes and high frequency resonance result

in larger wavelet coefficients

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TimeTimeTimeTime----Frequency Frequency Frequency Frequency

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Model-Based Analysis

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Auto-Regressive (AR) Modeling

• A sample in a time series can be considered as the linear

combination of past samples plus error

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+−=M

k neknxanx1

)( )()(

Deterministic part

(Model Coefficients)

Stochastic part

(Modeling error)

Power Spectrum Estimation

• The AR model spectrum has higher resolution than the

FFT based spectrum

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Hard Disk Drive Production Test

• AR modeling errors indicate different types of HDD faults.

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Engine Knock Detection• Optimized ignition timing results in a higher degree of

engine efficiency

• Earlier ignition results in a lower engine temperature and

reduced efficiency.

• Late ignition might result in auto-ignition and cause engine

knocks, which are shock waves on the cylinder.

• Engine knocks are transient events and can be detected

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• Engine knocks are transient events and can be detected

by the AR modeling error.

Engine Knock Detection - Sample 1

Constant Speed

You cannot see knocks in the

signal, even though you can hear

them clearly

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Peaks indicate the existence

of knocks

Engine Knock Detection - Sample 2

Run-up and Run-down

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Highlights of AR Modeling

• Good mathematical description of stationary signal.

• The AR modeling error indicates transients in the signal

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Select the Right AlgorithmsFrequencyFrequencyFrequencyFrequency

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Watchdog Agent® Prognostics Toolkit

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Prognostics

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Confidence ValueConfidence ValueConfidence ValueConfidence ValueConfidence ValueConfidence ValueConfidence ValueConfidence Valuefor performancefor performancefor performancefor performance

degradation degradation degradation degradation

assessmentassessmentassessmentassessment

(CV ~ 0(CV ~ 0(CV ~ 0(CV ~ 0----1)1)1)1)

Health Map Health Map Health Map Health Map Health Map Health Map Health Map Health Map for potential issues and for potential issues and for potential issues and for potential issues and

pattern pattern pattern pattern classificationclassificationclassificationclassification

Health Radar Chart Health Radar Chart Health Radar Chart Health Radar Chart Health Radar Chart Health Radar Chart Health Radar Chart Health Radar Chart for multiple components for multiple components for multiple components for multiple components

degradation monitoringdegradation monitoringdegradation monitoringdegradation monitoring

Risk Radar ChartRisk Radar ChartRisk Radar ChartRisk Radar ChartRisk Radar ChartRisk Radar ChartRisk Radar ChartRisk Radar Chartto prioritize maintenance to prioritize maintenance to prioritize maintenance to prioritize maintenance

decisiondecisiondecisiondecision

Watchdog Agent

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Prognostics & Forecasting Methods

Feature 2

Normal

BehaviorModel of

Failure

Predicted Predicted Predicted Predicted

Probability of Probability of Probability of Probability of

FailureFailureFailureFailure

Start of

Performance

Degradation

Current

Situation

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Feature 1

Prediction

Evolution of

ARMA Prediction

Prediction

Uncertainty

Predicted Predicted Predicted Predicted

Confidence Confidence Confidence Confidence

ValueValueValueValue

Signal Processing & Feature Extraction

Stationary Stationary Stationary Stationary

NonNonNonNon----Stationary Stationary Stationary Stationary

Signal Processing &

Feature Extraction

Time Domain Analysis

Frequency Domain Analysis

Wavelet/Wavelet Packet Analysis

Principal Component Analysis (PCA)

Raw Raw Raw Raw

VibrationsVibrationsVibrationsVibrationsRaw Raw Raw Raw

VibrationsVibrationsVibrationsVibrations

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CWT of time synchronous signal for gearbox with broken tooth (File 105)

Time (for 1 revolution)

scales a

1000 2000 3000 4000 5000 6000 7000 8000 1

synchronous synchronous synchronous synchronous

AverageAverageAverageAverage

FFT + FFT + FFT + FFT +

EnvelopeEnvelopeEnvelopeEnvelope

WaveletsWaveletsWaveletsWavelets

Health Assessment

Logistic Regression

Statistical Pattern Recognition

Feature Map Pattern Matching

(Self-organizing Map)

Neural Network

Gaussian Mixture Model (GMM)

Normal Normal Normal Normal Most Recent Most Recent Most Recent Most Recent

Normal Behavior in Operating Condition 1Normal Behavior in Operating Condition 1Normal Behavior in Operating Condition 1Normal Behavior in Operating Condition 1 Recent Behavior in Operating Condition 1Recent Behavior in Operating Condition 1Recent Behavior in Operating Condition 1Recent Behavior in Operating Condition 1

Raw Vibration in each Raw Vibration in each Raw Vibration in each Raw Vibration in each

Operating ConditionOperating ConditionOperating ConditionOperating Condition

Health Assessment

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Feature SpaceFeature SpaceFeature SpaceFeature Space

Normal Normal Normal Normal

BehaviorBehaviorBehaviorBehaviorMost Recent Most Recent Most Recent Most Recent

BehaviorBehaviorBehaviorBehavior

Confidence Value (CV)Confidence Value (CV)Confidence Value (CV)Confidence Value (CV)

Normal Behavior in Operating Condition 1Normal Behavior in Operating Condition 1Normal Behavior in Operating Condition 1Normal Behavior in Operating Condition 1 Recent Behavior in Operating Condition 1Recent Behavior in Operating Condition 1Recent Behavior in Operating Condition 1Recent Behavior in Operating Condition 1

FeaturesFeaturesFeaturesFeatures

Health DiagnosisSupport Vector Machine (SVM)

Feature Map Pattern Matching (SOM)

Bayesian Belief Network (BBN)

Hidden Markov Model (HMM)

Evidence-based Holo-Coefficients

Health Diagnosis

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Gearbox Health MapGearbox Health MapGearbox Health MapGearbox Health Map

Normal Normal Normal Normal

GearboxGearboxGearboxGearbox

Recent Behavior Recent Behavior Recent Behavior Recent Behavior

Gear 1 Broken ToothGear 1 Broken ToothGear 1 Broken ToothGear 1 Broken Tooth

FeaturesFeaturesFeaturesFeatures

Need to train 1 Need to train 1 Need to train 1 Need to train 1

health map for health map for health map for health map for

each operating each operating each operating each operating

condition!condition!condition!condition!

WIND FARM INFO:WIND FARM INFO:WIND FARM INFO:WIND FARM INFO:WIND FARM INFO:WIND FARM INFO:WIND FARM INFO:WIND FARM INFO: CURRENT CONDITIONS:CURRENT CONDITIONS:CURRENT CONDITIONS:CURRENT CONDITIONS:CURRENT CONDITIONS:CURRENT CONDITIONS:CURRENT CONDITIONS:CURRENT CONDITIONS:

Equipment HealthEquipment HealthEquipment HealthEquipment Health

PHM Analytics for a Wind Farm

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Equipment RiskEquipment RiskEquipment RiskEquipment Risk

WIND TURBINE INFO:WIND TURBINE INFO:WIND TURBINE INFO:WIND TURBINE INFO:WIND TURBINE INFO:WIND TURBINE INFO:WIND TURBINE INFO:WIND TURBINE INFO: CURRENT CONDITIONS:CURRENT CONDITIONS:CURRENT CONDITIONS:CURRENT CONDITIONS:CURRENT CONDITIONS:CURRENT CONDITIONS:CURRENT CONDITIONS:CURRENT CONDITIONS:

Wind Turbine EfficiencyWind Turbine EfficiencyWind Turbine EfficiencyWind Turbine Efficiency

Component HealthComponent HealthComponent HealthComponent Health

Wind Turbine Efficiency

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Wind Turbine CV HistoryWind Turbine CV HistoryWind Turbine CV HistoryWind Turbine CV History Component RiskComponent RiskComponent RiskComponent Risk2 4 6 8 10 12 14 16 18 20

Wind Turbine Efficiency

2 4 6 8 10 12 14 16 18 200

Health CV History

NI LabVIEW Watchdog Agent Toolkit

Signal Analysis

Health

Assessment

Features

Confidence Value

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Health

Prediction

Health

Diagnosis

Confidence Value

Future Health

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