Estimating the Degree of Nonlinearity in Transient Responses with Zeroed Early Time Fast Fourier Transforms

Similar documents
Estimating the Degree of Nonlinearity in Transient Responses with Zeroed Early-Time Fast Fourier Transforms

Introduction to Iwan Models and Modal Testing for Structures with Joints

Input-Output Peak Picking Modal Identification & Output only Modal Identification and Damage Detection of Structures using

Substructuring of a nonlinear beam using a modal Iwan framework, Part II: Nonlinear Modal Substructuring

OSE801 Engineering System Identification. Lecture 09: Computing Impulse and Frequency Response Functions

The Measurement of a Nonlinear Resonant Decay using Continuous-scan Laser Doppler Vibrometry

Experimental Modal Substructuring to Extract Fixed-Base Modes from a Substructure Attached to a Flexible Fixture

Experimental Modal Substructuring with Nonlinear Modal Iwan Models to Capture Nonlinear Subcomponent Damping

SHOCK AND VIBRATION RESPONSE SPECTRA COURSE Unit 6B. Notes on the Fourier Transform Magnitude

Application of Viscous and Iwan Modal Damping Models to Experimental Measurements from Bolted Structures

Advanced Operational Modal Analysis Methods for Linear Time Periodic System Identification

Plate mode identification using modal analysis based on microphone array measurements

Vibration Testing. an excitation source a device to measure the response a digital signal processor to analyze the system response

Aeroelasticity. Lecture 4: Flight Flutter Testing. G. Dimitriadis. AERO0032-1, Aeroelasticity and Experimental Aerodynamics, Lecture 4

ARTICLE IN PRESS. Mechanical Systems and Signal Processing

Aspects of Continuous- and Discrete-Time Signals and Systems

In-Structure Response Spectra Development Using Complex Frequency Analysis Method

Delayed, Multi-Step Inverse Structural Filter for Robust Force Identification

Force, displacement and strain nonlinear transfer function estimation.

SENSITIVITY ANALYSIS OF ADAPTIVE MAGNITUDE SPECTRUM ALGORITHM IDENTIFIED MODAL FREQUENCIES OF REINFORCED CONCRETE FRAME STRUCTURES

Identification Methods for Structural Systems. Prof. Dr. Eleni Chatzi Lecture March, 2016

ME 563 HOMEWORK # 7 SOLUTIONS Fall 2010

ABSTRACT Modal parameters obtained from modal testing (such as modal vectors, natural frequencies, and damping ratios) have been used extensively in s

Malaysia. Lumpur, Malaysia. Malaysia

SHOCK AND VIBRATION RESPONSE SPECTRA COURSE Unit 1B. Damping

Localization of vibration-based damage detection method in structural applications

New Developments in Tail-Equivalent Linearization method for Nonlinear Stochastic Dynamics

Simple Identification of Nonlinear Modal Parameters Using Wavelet Transform

Transient Analysis of Interconnects by Means of Time-Domain Scattering Parameters

Nonlinearity of Joints in Structural Dynamics of Weapons Systems

An algorithm for detecting oscillatory behavior in discretized data: the damped-oscillator oscillator detector

Simple Experiments to Validate Modal Substructure Models

Displacement at very low frequencies produces very low accelerations since:

2330. A study on Gabor frame for estimating instantaneous dynamic characteristics of structures Wei-Chih Su 1, Chiung-Shiann Huang 2 1

Integration of measured receptance into a time domain simulation of a Multi Body Model using SIMPACK

Nonlinear system identification with the use of describing functions a case study

D && 9.0 DYNAMIC ANALYSIS

Stochastic Dynamics of SDOF Systems (cont.).

Dynamic Substructuring of an A600 Wind Turbine

Fourier Transform for Continuous Functions

Improving the Accuracy of Dynamic Vibration Fatigue Simulation

Dynamic Characteristics for Traditional Wooden Structure in Korea by Using Impact Hammer Test

STRUCTURAL HEALTH MONITORING OF A FRAME USING TRANSIENT VIBRATION ANALYSIS

Math Fall Linear Filters

Alfa-Tranzit Co., Ltd offers the new DYNAMICS R4.0 program system for analysis and design of rotor systems of high complexity

Time-Series Analysis for Ear-Related and Psychoacoustic Metrics

SHOCK RESPONSE SPECTRUM ANALYSIS VIA THE FINITE ELEMENT METHOD Revision C

OSE801 Engineering System Identification. Lecture 05: Fourier Analysis

Non-parametric identification

The use of transmissibility properties to estimate FRFs on modified structures

Dynamic measurement: application of system identification in metrology

MASS LOADING EFFECTS FOR HEAVY EQUIPMENT AND PAYLOADS Revision F

Lecture 27 Frequency Response 2

Accounting for non-stationary frequency content in Earthquake Engineering: Can wavelet analysis be useful after all?

742. Time-varying systems identification using continuous wavelet analysis of free decay response signals

COMPARISON OF MODE SHAPE VECTORS IN OPERATIONAL MODAL ANALYSIS DEALING WITH CLOSELY SPACED MODES.

Acoustics-An An Overview. Lecture 1. Vibro-Acoustics. What? Why? How? Lecture 1

EXPERIMENTAL EVALUATION OF NONLINEAR INDICES FOR ULTRASOUND TRANSDUCER CHARACTERIZATIONS TIMOTHY ALLEN BIGELOW. B.S., Colorado State University, 1998

Vibro-Impact Dynamics of a Piezoelectric Energy Harvester

Structural Damage Detection Using Time Windowing Technique from Measured Acceleration during Earthquake

Non-linear Modal Behaviour in Cantilever Beam Structures

Lecture Hilbert-Huang Transform. An examination of Fourier Analysis. Existing non-stationary data handling method

PART 1. Review of DSP. f (t)e iωt dt. F(ω) = f (t) = 1 2π. F(ω)e iωt dω. f (t) F (ω) The Fourier Transform. Fourier Transform.

Experimental and Numerical Modal Analysis of a Compressor Mounting Bracket

Experimental Aerodynamics. Experimental Aerodynamics

Identification of Nonlinear Mechanical Systems: State of the Art and Recent Trends

Iterative Learning Control (ILC)

e jωt = cos(ωt) + jsin(ωt),

Introduction to Biomedical Engineering

Calculating Mechanical Transfer Functions with COMSOL Multiphysics. Yoichi Aso Department of Physics, University of Tokyo

Bearing fault diagnosis based on TEO and SVM

Estimation of Unsteady Loading for Sting Mounted Wind Tunnel Models

Cepstral Deconvolution Method for Measurement of Absorption and Scattering Coefficients of Materials

Grandstand Terraces. Experimental and Computational Modal Analysis. John N Karadelis

CHAPTER 8 FATIGUE LIFE ESTIMATION OF ELECTRONIC PACKAGES SUBJECTED TO DYNAMIC LOADS

Review of Analog Signal Analysis

A vector to a system output vector is required for many

SPERIMENTAZIONE DI STRUTTURE AEROSPAZIALI TESTING OF AEROSPACE STRUCTURES

S. Marmorat (POEMS - INRIA Rocquencourt) F. Collino (CERFACS) J.-D. Benamou (POEMS - INRIA Rocquencourt)

Analysis of shock force measurements for the model based dynamic calibration

Finite Element Analysis Lecture 1. Dr./ Ahmed Nagib

Professor T.S. Jang. Naval Architecture and Ocean Engineering Naval Architecture and Ocean Engineering

Nonparametric identification of added masses in frequency domain: a numerical study

Topic 7. Convolution, Filters, Correlation, Representation. Bryan Pardo, 2008, Northwestern University EECS 352: Machine Perception of Music and Audio

Introduction to time-frequency analysis. From linear to energy-based representations

An example of correlation matrix based mode shape expansion in OMA

Structural changes detection with use of operational spatial filter

IMPROVEMENTS IN MODAL PARAMETER EXTRACTION THROUGH POST-PROCESSING FREQUENCY RESPONSE FUNCTION ESTIMATES

Process Control & Instrumentation (CH 3040)

Structural System Identification (KAIST, Summer 2017) Lecture Coverage:

(i) Represent discrete-time signals using transform. (ii) Understand the relationship between transform and discrete-time Fourier transform

Convolution. Define a mathematical operation on discrete-time signals called convolution, represented by *. Given two discrete-time signals x 1, x 2,

EE538 Final Exam Fall 2007 Mon, Dec 10, 8-10 am RHPH 127 Dec. 10, Cover Sheet

Vibration Testing. Typically either instrumented hammers or shakers are used.

Outline of parts 1 and 2

Nonlinear Buckling Prediction in ANSYS. August 2009

Evaluation of a Simple Method of Identification of Dynamic Parameters of a Single-Degree-of-Freedom System

Direct Fatigue Damage Spectrum Calculation for a Shock Response Spectrum

Linear Filters. L[e iωt ] = 2π ĥ(ω)eiωt. Proof: Let L[e iωt ] = ẽ ω (t). Because L is time-invariant, we have that. L[e iω(t a) ] = ẽ ω (t a).

Nonlinear Rolling Element Bearings in MADYN 2000 Version 4.3

Transcription:

Estimating the Degree of Nonlinearity in Transient Responses with Zeroed Early Time Fast Fourier Transforms Matthew S. Allen Department of Engineering Physics University of Wisconsin-Madison Randall L. Mayes Sandia National Laboratories IMAC XXVI, Orlando, Florida, Feb. 29

Introduction Challenge Detect and characterize short duration nonlinearity in transient response data from relatively high order systems. Proposed Tools and Theory Zeroed Early-time FFT (ZEFFT) Backwards Extrapolation for Nonlinearity Detection (BEND)

Challenge Some systems with bolted joints respond to impulses nonlinearly in the first few cycles followed by a linear decay. x 2 8 6 Response Zero Pts m 2 4 2 f slip Response -2-4 m 6-8 -6 k 5-1 x 6 2 4 6 8 1 Time (ms)

Challenge (2) Full nonlinear system identification of structures with joints is extremely difficult. Moderate to High order systems are currently beyond the reach of state of the art nonlinear system identification algorithms Time-frequency methods may not have sufficient resolution for the responses of interest. (i.e. Spectrogram, Wavelet, Choi-Williams, Hilbert- Huang ) What can we do then?

Zeroed Early time FFT Magnitude Response Excitation 8 6 4 2 1 6-2 -4-6 -8-1 -12 1 5 NLDetect: FFT of Time Response - Truncated at zero points) Response Zero Pts 13.9 27.4 5 1 15 2 25 3 35 4 45 5 Time 12 (ms) in. F High Amplitude Nonlinear Bolted Joint 42.18 58.35 82.28 17 129.6 height =.75 in, width = 1. in. Low Amplitude Linear 134 136 138 14 142 144 Frequency (Hz) Nonlinearity is assumed to be active at high amplitudes and inactive at lower amplitudes The response then becomes more linear as more of the initial nonlinear response is nullified. Impulse responses with initial segments of varying length set to zero are compared in the frequency domain. The nonzero portion of each impulse response begins at a point in which the response is near zero.

Response 5 4 3 2 1-1 -2 Experimental Shock Data: Time Response Response Zero Pts Response of a complex structure with a bolted joint to an impulsive force. -3-4 1 2 3 4 5 6 Time (ms)

Shock Data: ZEFFTs Magnitude 1 5 1 4 1 3.65 1.1 1.875 2.635 3.595 5.1 5.78 Response contains a large, broad peak between 375 and 6 Hz at early times. Response appears to be very noisy above 8 Hz. This could be due in part to harmonics of the lower frequency modes. 2 4 6 8 1 12 14 16 18 2 Frequency (Hz)

Analytical Example x 1 x 2 x 2 x 4 x 5 m 1 m 2 m 2 m 4 m 5 k 1 k 2 k 2 k 4 Y X k 5 (Nonlinear) m 6 k 6 m 7 x 6 x 7 7-DOF system above used to test the methods.

Example #1 Slip: Time Response Response 8 6 4 2-2 -4-6 Response Zero Pts Zero crossings in time response identified. It is difficult to discern if the system is nonlinear by simply inspecting the acceleration time history. -8-1 2 4 6 8 1 Time (ms)

Example #1 Slip: ZEFFTs Magnitude 1 7 1 6 1 5 12.81 17.97 29.25 48.15 62.89 81.11 96.18 1 4 5 1 15 2 25 3 35 4 Frequency (Hz) ZEFFTs show extra frequency content between 18 and 35 Hz for zero times less than 15 ms.

Truncated Analytical Impulse Response Free response of an LTI system in Frequency Domain: H H ( ω) t z ( ω) 2N N * N A r Ar A r iωb1+ B = = + * = 2 2 r= 1 iω λr r= 1 iω λr iω λr r= 1 ωr ω + 2iζrωrω = 2N r= 1 λrtz Ae r e iω λ r iωt z Free response truncated at time t z has the same form as the impulse response except for the eiωt factor.

Example #1 Slip: BEND & IBEND 1 7.45.4.35 Normalized Integral of Difference FRF over Frequency Integral fit time.4.35 Magnitude 1 6 Normalized Integral.3.25.2.15.1.3.25.2.15.1 1 5 21.9 Fit to 21.9 Extrap from 21.9 to.5 2 4 6 8 1 time (ms).5 5 1 15 2 25 3 35 4 Frequency (Hz) Response at 21.9 ms was curve fit using AMI algorithm. Agreement is excellent at 21.9 ms and at all later times. Backwards extrapolation of 21.9 ms response to time does not agree well suggesting that the system behaves nonlinearly some time before 21.9 ms. IBEND suggests that the system is linear for t > 1-15ms.

Shock Data: BEND Magnitude 1 5 1 4 NLDetect: FFT of Time Response - Truncated at zero points) 3.84 Fit to 3.84.19 Extrap from 3.84 to.19 1 3 1 2 3 4 5 6 7 8 Frequency (Hz) 5.87 Extrap from 3.84 to 5.87 Fit the response at 3.84ms and extrapolated backwards to.19ms and forwards to 5.87ms. Forward extrapolation agrees with the data about as well as the fit suggesting that the curve fit is accurate. Backward extrapolation does not show the broad peak between 375 and 6 Hz, suggesting that this is indeed due to nonlinearity.

Shock Data: IBEND Normalized Integral.9.8.7.6.5.4.3.2.1 Normalized Integral of Difference FRF over Frequency Integral fit time 1 2 3 4 5 6 time (ms).8.7.6.5.4.3.2.1 IBEND suggests that the system behaves linearly after 2ms. The agreement is not perfect from 4-6ms. This may be due to the increasing relative importance of measurement noise.

Conclusions Zeroed Early-time FFTs (ZEFFTs) and Backwards Extrapolation (BEND) provide insight into the response of a nonlinear system to shock loading. BEND and IBEND can be used to provide quantitative information and to develop insight. Care must be taken when interpreting the results of linear system identification. Even if SYSID fails, direct inspection of the ZEFFTs may still yield useful information.