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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
Short
tim
e b
ut
wid
e b
and Long time but narrow band
Short time
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Fre
quency
Time
Short
tim
e b
ut
wid
e b
and
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
AnalysisAnalysisAnalysisAnalysis
TimeTimeTimeTime----
Frequency Frequency Frequency Frequency
AnalysisAnalysisAnalysisAnalysis
QuefrencyQuefrencyQuefrencyQuefrency
AnalysisAnalysisAnalysisAnalysis
Wavelet Wavelet Wavelet Wavelet
AnalysisAnalysisAnalysisAnalysis
ModelModelModelModel
BasedBasedBasedBased
t
f
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Select the Right AlgorithmsFrequencyFrequencyFrequencyFrequency
AnalysisAnalysisAnalysisAnalysis
Order Order Order Order
AnalysisAnalysisAnalysisAnalysis
TimeTimeTimeTime----
Frequency Frequency Frequency Frequency
AnalysisAnalysisAnalysisAnalysis
QuefrencyQuefrencyQuefrencyQuefrency
AnalysisAnalysisAnalysisAnalysis
Wavelet Wavelet Wavelet Wavelet
AnalysisAnalysisAnalysisAnalysis
ModelModelModelModel
BasedBasedBasedBased
t
f
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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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Time-Frequency Analysis
Time-Frequency Analysis
• The short-time Fourier transform (STFTSTFTSTFTSTFT) is the
most popular time-frequency analysis algorithm
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STFT
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
AnalysisAnalysisAnalysisAnalysis
Order Order Order Order
AnalysisAnalysisAnalysisAnalysis
TimeTimeTimeTime----
Frequency Frequency Frequency Frequency
AnalysisAnalysisAnalysisAnalysis
QuefrencyQuefrencyQuefrencyQuefrency
AnalysisAnalysisAnalysisAnalysis
Wavelet Wavelet Wavelet Wavelet
AnalysisAnalysisAnalysisAnalysis
ModelModelModelModel
BasedBasedBasedBased
t
f
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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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IFFT
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
2αBB
outerD
DfNf
Characteristic frequency for an outerouterouterouter ring fault of a
bearing
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−= )cos(1
2α
C
outerD
f
+= )cos(1
2α
C
BBinner
D
DfNf
Characteristic frequency for an innerinnerinnerinner ring fault of a
bearing
BN : Number of balls
f : Rotation frequency
BD
CD : Retainer diameter
: Ball diameter α : Ball contact
angle
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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HzfD
DfNf
C
BBouter 900.3)cos(1
2==
−= α
HzfD
DfNf
C
BBinner 1200.4)cos(1
2==
+= α
• 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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Select the Right Algorithms
FrequencyFrequencyFrequencyFrequency
AnalysisAnalysisAnalysisAnalysis
Order Order Order Order
AnalysisAnalysisAnalysisAnalysis
TimeTimeTimeTime----
Frequency Frequency Frequency Frequency
AnalysisAnalysisAnalysisAnalysis
QuefrencyQuefrencyQuefrencyQuefrency
AnalysisAnalysisAnalysisAnalysis
Wavelet Wavelet Wavelet Wavelet
AnalysisAnalysisAnalysisAnalysis
ModelModelModelModel
BasedBasedBasedBased
t
f
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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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Application Example:
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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Select the Right Algorithms
FrequencyFrequencyFrequencyFrequency
AnalysisAnalysisAnalysisAnalysis
Order Order Order Order
AnalysisAnalysisAnalysisAnalysis
TimeTimeTimeTime----Frequency Frequency Frequency Frequency
AnalysisAnalysisAnalysisAnalysis
QuefrencyQuefrencyQuefrencyQuefrency
AnalysisAnalysisAnalysisAnalysis
Wavelet Wavelet Wavelet Wavelet
AnalysisAnalysisAnalysisAnalysis
ModelModelModelModel
BasedBasedBasedBased
t
f
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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
M
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∑=
+−=M
k
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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Application Example:
Hard Disk Drive Production Test
• AR modeling errors indicate different types of HDD faults.
Good
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Pitch
Crack
Zee
Application Example:
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
AnalysisAnalysisAnalysisAnalysis
Order Order Order Order
AnalysisAnalysisAnalysisAnalysis
TimeTimeTimeTime----
Frequency Frequency Frequency Frequency
AnalysisAnalysisAnalysisAnalysis
QuefrencyQuefrencyQuefrencyQuefrency
AnalysisAnalysisAnalysisAnalysis
Wavelet Wavelet Wavelet Wavelet
AnalysisAnalysisAnalysisAnalysis
ModelModelModelModel
BasedBasedBasedBased
t
f
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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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0
Feature 1
ARMA
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
Time-Frequency 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
5
9
13
17
21
25
29
33
37
41
45
49
53
57
61
TimeTimeTimeTime----
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!
C1
C1
C4
C1
C4
C3
C3
C1
C4
C3
C2
C2
C4
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:
N
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:
N
Wind Turbine EfficiencyWind Turbine EfficiencyWind Turbine EfficiencyWind Turbine Efficiency
Component HealthComponent HealthComponent HealthComponent Health
0.4
0.6
0.8
1
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
0
0.2
Time
Wind Turbine Efficiency
2 4 6 8 10 12 14 16 18 200
0.2
0.4
0.6
0.8
1
Time
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
