DOCSLIB.ORG

Frequency Based Fatigue

Total Page：16

File Type：pdf, Size：1020Kb

Download full-text PDF Read full-text
Frequency Based Fatigue Frequency Based Fatigue Professor Darrell F. Socie Mechanical Engineering University of Illinois at Urbana-Champaign © 2001 Darrell Socie, All Rights Reserved Deterministic versus Random Deterministic – from past measurements the future position of a satellite can be predicted with reasonable accuracy Random – from past measurements the future position of a car can only be described in terms of probability and statistical averages AAR Seminar © 2001 Darrell Socie, All Rights Reserved 1 of 57 Time Domain Bracket.sif-Strain_c56 750 Strain (ustrain) -750 Time (Secs) AAR Seminar © 2001 Darrell Socie, All Rights Reserved 2 of 57 Frequency Domain ap_000.sif-Strain_b43 102 101 100 Strain (ustrain) 10-1 0 20 40 60 80 100 120 140 Frequency (Hz) AAR Seminar © 2001 Darrell Socie, All Rights Reserved 3 of 57 Statistics of Time Histories ! Mean or Expected Value ! Variance / Standard Deviation ! Coefficient of Variation ! Root Mean Square ! Kurtosis ! Skewness ! Crest Factor ! Irregularity Factor AAR Seminar © 2001 Darrell Socie, All Rights Reserved 4 of 57 Mean or Expected Value Central tendency of the data N ∑ x i = Mean = µ = x = E()X = i 1 x N AAR Seminar © 2001 Darrell Socie, All Rights Reserved 5 of 57 Variance / Standard Deviation Dispersion of the data N − 2 ∑( xi x ) Var()X = i =1 N Standard deviation σ = x Var( X ) AAR Seminar © 2001 Darrell Socie, All Rights Reserved 6 of 57 Coefficient of Variation σ COV = x µ x Useful to compare different dispersions µ =10 µ = 100 σ =1 σ =10 COV = 0.1 COV = 0.1 AAR Seminar © 2001 Darrell Socie, All Rights Reserved 7 of 57 Root Mean Square N 2 ∑ x i = RMS = i 1 N The rms is equal to the standard deviation when the mean is 0 AAR Seminar © 2001 Darrell Socie, All Rights Reserved 8 of 57 Skewness Skewness is a measure of the asymmetry of the data around the sample mean. If skewness is negative, the data are spread out more to the left of the mean than to the right. If skewness is positive, the data are spread out more to the right. The skewness of the normal distribution (or any perfectly symmetric distribution) is zero. N − 3 ∑( xi x ) = Skewness()X = i 1 Nσ3 AAR Seminar © 2001 Darrell Socie, All Rights Reserved 9 of 57 Kurtosis Kurtosis is a measure of how outlier-prone a distribution is. The kurtosis of the normal distribution is 3. Distributions that are more outlier-prone than the normal distribution have kurtosis greater than 3; distributions that are less outlier-prone have kurtosis less than 3. N − 4 ∑( xi x ) = Kurtosis()X = i 1 Nσ4 AAR Seminar © 2001 Darrell Socie, All Rights Reserved 10 of 57 Crest Factor The crest factor is the ration of the peak (maximum) value to the root-mean-square (RMS) value. A sine wave has a crest factor of 1.414. AAR Seminar © 2001 Darrell Socie, All Rights Reserved 11 of 57 Irregularity Factor Positive zero crossing E(0+ ) 4 IF= = Peak E(P ) 7 IF → 1 is narrow band signal IF → 0 is wide band signal AAR Seminar © 2001 Darrell Socie, All Rights Reserved 12 of 57 Time Domain Nomenclature ! Random ! Stochastic ! Stationary ! Non-stationary ! Gaussian ! Narrow-band ! Wide-band AAR Seminar © 2001 Darrell Socie, All Rights Reserved 13 of 57 Random The instantaneous value can not be predicted at any future time. AAR Seminar © 2001 Darrell Socie, All Rights Reserved 14 of 57 Stochastic Stochastic processes provide suitable models for physical systems where the phenomena is governed by probabilities. AAR Seminar © 2001 Darrell Socie, All Rights Reserved 15 of 57 Stationary ∆t The properties computed over short time intervals, t + ∆t, do not significantly vary from each other AAR Seminar © 2001 Darrell Socie, All Rights Reserved 16 of 57 Non-stationary AAR Seminar © 2001 Darrell Socie, All Rights Reserved 17 of 57 Gaussian Normally distributed around the mean AAR Seminar © 2001 Darrell Socie, All Rights Reserved 18 of 57 Narrow-band AAR Seminar © 2001 Darrell Socie, All Rights Reserved 19 of 57 Wide-band AAR Seminar © 2001 Darrell Socie, All Rights Reserved 20 of 57 Frequency Domain Nomenclature Fourier Nyquist frequency Aliasing Anit-aliasing filter FFT Inverse FFT Autospectral density Transfer Function AAR Seminar © 2001 Darrell Socie, All Rights Reserved 21 of 57 Fourier 1768 - 1830 Fourier studied the mathematical theory of heat conduction. He established the partial differential equation governing heat diffusion and solved it by using infinite series of trigonometric functions. AAR Seminar © 2001 Darrell Socie, All Rights Reserved 22 of 57 Fourier Series ∞ = + ω + ω X(t) ao ∑ak sin(k ot) bk cos(k ot) k =1 ∞ = + ω + θ X(t) co ∑ck cos(k ot k ) k =1 ∞ ω = jk ot X(t) ∑ XS [k]e k = −∞ ak,bk,ck,XS[k] are Fourier coefficients frequency, magnitude, and phase are all described by the coefficients AAR Seminar © 2001 Darrell Socie, All Rights Reserved 23 of 57 Digital Sampling ∆t What ∆t should be used for sampling the data? AAR Seminar © 2001 Darrell Socie, All Rights Reserved 24 of 57 Aliasing 2 signals that differ in frequency by a factor of 2 f 2f Both signals have the same digital samples. AAR Seminar © 2001 Darrell Socie, All Rights Reserved 25 of 57 Nyquist frequency Real spectrum Aliased spectrum 1magnitude magnitude f = c 2∆t frequency frequency fc 1 f = c 2∆t Sample at 2 times the frequency of interest AAR Seminar © 2001 Darrell Socie, All Rights Reserved 26 of 57 Anti-aliasing Filter 180 0 Phase -180 0 50 100 150 0 frequency -20 -40 -60 -80 Magnitude, dB -100 AAR Seminar © 2001 Darrell Socie, All Rights Reserved 27 of 57 Fourier Transform Magnitude FFT Magnitude ∆f Time Frequency The area under each spike represents the magnitude of the sine wave at that frequency. The magnitude of the FFT depends on the frequency window ∆f. AAR Seminar © 2001 Darrell Socie, All Rights Reserved 28 of 57 Fast Fourier Transform - FFT 16 14 12 10 Strain (ustrain) 8 6 FFT 4 2 0 0 20 40 60 80 100 120 140 Frequency (Hz) N/ 2 = + ω + θ X(t) co ∑ck cos(k ot k ) k =1 AAR Seminar © 2001 Darrell Socie, All Rights Reserved 29 of 57 Fast Fourier Transform - FFT 150 100 50 0 -50 Phase (Degrees) -100 -150 0 20 40 60 80 100 120 140 Frequency (Hz) N/ 2 = + ω + θ X(t) co ∑ck cos(k ot k ) k =1 AAR Seminar © 2001 Darrell Socie, All Rights Reserved 30 of 57 Inverse FFT N/ 2 = + ω + θ X(t) co ∑ck cos(k ot k ) k =1 θ ω ck, k, and k o are all known 750 Strain (ustrain) -750 Time (Secs) AAR Seminar © 2001 Darrell Socie, All Rights Reserved 31 of 57 Autospectral Density Function Units Time History EU X(n) Linear Spectrum EU S(n) = DFT(X(n)) AutoPower EU^2 AP(n) = S(n) · S(n) PSD (EU^2)/Hz ∆ PSD(n) = AP(n) / ( Wf · f) · ∆ ESD (EU^2*sec)/Hz ESD(n) = AP(n) T/(Wf · f) AAR Seminar © 2001 Darrell Socie, All Rights Reserved 32 of 57 Calculating Autospectral Density ND T2T3T nDT ND Block size n 1 D 2 S()f = X()f k ∆ ∑ k nD ND t i=1 Magnitude only no phase information AAR Seminar © 2001 Darrell Socie, All Rights Reserved 33 of 57 Power Spectral Density - PSD Log Magnitude-Power-Strain_c56 105 104 103 102 101 Strain (ustrain^2)/Hz 100 100 101 102 Frequency (Hz) Average power associated with a 1 Hz frequency window centered at each frequency, f . Phase information is lost. AAR Seminar © 2001 Darrell Socie, All Rights Reserved 34 of 57 Linear Spectrum Log Magnitude-Linear-Strain_c56 102 101 Strain (ustrain) 100 100 101 102 Frequency (Hz) Sometimes called Amplitude Spectral Density AAR Seminar © 2001 Darrell Socie, All Rights Reserved 35 of 57 Comparison 105 104 103 PSD 102 101 Strain (ustrain^2) 0 10 Linear Spectrum 10-1 100 101 102 Frequency (Hz) AAR Seminar © 2001 Darrell Socie, All Rights Reserved 36 of 57 Frequency Domain Limitations Non-stationary signals EASE1.EDT-Strain_c56 800 600 400 200 0 Strain (ustrain) -200 -400 Time (Secs) AAR Seminar © 2001 Darrell Socie, All Rights Reserved 37 of 57 Linear Spectrum 103 102 101 Strain (ustrain) 100 10-1 0 20 40 60 80 100 120 140 Frequency (Hz) AAR Seminar © 2001 Darrell Socie, All Rights Reserved 38 of 57 Transfer Function output 104 m output 3 input 10 input 102 100 101 102 Frequency (Hz) AAR Seminar © 2001 Darrell Socie, All Rights Reserved 39 of 57 Frequency Based Fatigue ! Stationary Loading ! Wind ! Sea State ! Vibration AAR Seminar © 2001 Darrell Socie, All Rights Reserved 40 of 57 Assumptions Random Gaussian Stationary AAR Seminar © 2001 Darrell Socie, All Rights Reserved 41 of 57 Fatigue Analysis Load Stress Structural time time model history history Time Domain Miner Rainflow linear Durability damage Frequency Domain ? Acceleration Transfer Stress PSD function PSD AAR Seminar © 2001 Darrell Socie, All Rights Reserved 42 of 57 Dynamics Model F(t) y(t) d2y m = F(t) m dt2 F(t) z(t) = y(t) – x(t) y(t) d2z dz d2x m m + c + k z = F(t) − m dt2 dt dt2 x(t) c k d2z dz d2x + 2ζω + ω 2 z = − dt2 n dt n dt2 AAR Seminar © 2001 Darrell Socie, All Rights Reserved 43 of 57 PSD Moments fk /Hz 2 Gk(f) Magnitude δf frequency N = n δ mn ∑ fk Gk ( fk ) f k =1 AAR Seminar © 2001 Darrell Socie, All Rights Reserved 44 of 57 Expected Values m zero crossings E(0)= 2 m0 m peaks E(P )= 4 m2 E(0 ) m 2 Irregularity factor IF= = 2 E(P ) m0 m4 AAR Seminar © 2001 Darrell Socie, All Rights Reserved 45 of 57 Probability Density Function ∆ Si ∆ p( Si ) Probability density δS Stress range ∆ The probability P( Si ) of a stress range occurring between δS δS ∆S − and ∆S + is P( ∆S ) = p( ∆S )δS i 2 i 2 i i AAR Seminar © 2001 Darrell Socie, All Rights Reserved 46 of 57 Fatigue Damage ∆ δ Cycles at level i ni =p( Si ) SNT Total cycles NT =E(P)T Total time 1 ∆S b Fatigue life N ( ∆S )= i f i ' 2Sf N n 1 N p( ∆S ) Fatigue Damage D= i = E(P)T δS(2S' )b i ∑ f ∑ 1 = ∆ = i 1 Nf ( Si ) i 1 ()∆
Load more

Recommended publications
  • WILL the REAL MAXIMUM SPL PLEASE STAND UP? Measured Maximum SPL Vs Calculated Maximum SPL and How Not to Be Fooled
    WILL THE REAL MAXIMUM SPL PLEASE STAND UP? Measured Maximum SPL vs Calculated Maximum SPL and how not to be fooled Introduction When purchasing powered loudspeakers, most customers compare three key specifications: price, power and maximum SPL. Unfortunately, this can be like comparing apples and oranges. For the Maximum SPL number in particular, each manufacturer often has its own way of reporting the specification. There are various methods a manufacturer can employ and each tends to choose the one that makes a particular product look the best. Unfortunately for the customer, this makes comparing two products on paper very difficult. In this document, Mackie will do the following to shed some light on this issue: • Compare and contrast two common ways of reporting the maximum SPL of a powered loudspeaker. • Conduct a case study comparing the Mackie HD series loudspeakers with similar models from a key competitor to illustrate these differences. • Provide common practices for the customer to follow, allowing them to see through the hype and help them choose the best loudspeaker for their specific needs. The two most common ways of reporting maximum SPL for a loudspeaker are a Calculated Maximum SPL and a Measured Maximum SPL. These names are given here to simplify comparison; other manufacturers may use different terminology. Calculated Maximum SPL The Calculated Maximum SPL is a purely theoretical specification. The manufacturer uses the known power of their amplifier and the known sensitivity of the transducer to mathematically calculate the maximum SPL they can produce in a particular loudspeaker. For example, a compression driver’s peak sensitivity may be 110 dB at 1W at 1 meter.
    [Show full text]
  • Sound Quality of Audio Systems
    Acoustical Measurement of Sound System Equipment according IEC 60268-21 符合IEC 60268-21的音響系統聲學測量設備 KLIPPEL- live a series of webinars presented by Wolfgang Klippel KLIPPEL-live #5: Maximum SPL – a number becomes important , 1 Previous Sessions 1. Modern audio equipment needs output based testing 2. Standard acoustical tests performed in normal rooms 3. Drawing meaningful conclusions from 3D output measurement 4. Simulated standard condition at an evaluation point 5. Maximum SPL – giving this value meaning 6. Selecting measurements with high diagnostic value 7. Amplitude Compression – less output at higher amplitudes 8. Harmonic Distortion Measurements – best practice 9. Intermodulation Distortion – music is more than a single tone 10.Impulsive distortion - rub&buzz, abnormal behavior, defects 11.Benchmarking of audio products under standard conditions 12.Auralization of signal distortion – perceptual evaluation 13.Setting meaningful tolerances for signal distortion Acoustical14.testingRatingof a modernthe maximum active audio deviceSPL value for a product 1st Session15.2nd SessionSmart speaker testing with wireless audio input 3rd Session 4th Session KLIPPEL-live #5: Maximum SPL – a number becomes important , 2 Ask Klippel First Question: 在沒有復雜的測試箱或基於近場/遠場測量設置的測試箱的情況下，品檢上是否有一個好的解決方案。 Is there a good solution for on-line QC without complex testing box or testing chamber based on near field / far field measurement setting. Response WK: 否，對於傳感器和小型系統（例如智能手機），因為屏蔽環境噪聲非常有用。 NO, for transducers and small system (e.g. smart phones) because the shielding against ambient noise is very useful. 是的，較大的系統（條形音箱，專業設備，電視……）需要不同的解決方式 Yes, larger systems (sound bars, professional equipment, TVs …) need a different solution considering the following points: • 進行近場測量（與環境噪聲相比，SNR更高，SPL更高） Performing a near-field measurement (better SNR, higher SPL compared to ambient noise) • 在終端測試處使用圍繞測試站的吸收牆。 圍繞組裝線建造測試環境的最佳解決方案。 Using absorbing walls around the test station at the end of line.
    [Show full text]
  • Bachelor Thesis
    BACHELOR THESIS Perceived Sound Quality of Dynamic Range Reduced and Loudness Normalized Popular Music Jakob Lalér Bachelor of Arts Audio Engineering Luleå University of Technology Department of Business, Administration, Technology and Social Sciences Perceived sound quality of dynamic range reduced and loudness normalized popular music Lalér Jakob Lalér Jakob 1 S0038F ABSTRACT The lack of a standardized method for controlling perceived loudness within the music industry has been a contributory cause to the level increases that emerged in popular music at the beginning of the 1990s. As a consequence, discussions about what constitutes sound quality have been raised. This paper investigates to what extent dynamic range reduction affects perceived sound quality of popular music when loudness normalized in accordance with ITU-R BS. 1770-2. The results show that perceived sound quality was not affected by as much as -9 dB of average gain reduction. Lalér Jakob 2 S0038F TABLE OF CONTENTS ABSTRACT...........................................................................................................................................2 INTRODUCTION...................................................................................................................................4 Aim, Objectives and Limitations............................................................................................................4 Background..........................................................................................................................................4
    [Show full text]
  • A Review of Feature Extraction Methods in Vibration-Based Condition Monitoring and Its Application for Degradation Trend Estimation of Low-Speed Slew Bearing
    machines Review A Review of Feature Extraction Methods in Vibration-Based Condition Monitoring and Its Application for Degradation Trend Estimation of Low-Speed Slew Bearing Wahyu Caesarendra 1,2 ID and Tegoeh Tjahjowidodo 3,* ID 1 Mechanical Engineering Department, Diponegoro University, Semarang 50275, Indonesia; [email protected] 2 School of Mechanical, Materials and Mechatronic Engineering, University of Wollongong, Wollongong, NSW 2522, Australia 3 School of Mechanical and Aerospace Engineering, Nanyang Technological University, Singapore 639798, Singapore * Correspondence: [email protected] Received: 9 August 2017; Accepted: 19 September 2017; Published: 27 September 2017 Abstract: This paper presents an empirical study of feature extraction methods for the application of low-speed slew bearing condition monitoring. The aim of the study is to find the proper features that represent the degradation condition of slew bearing rotating at very low speed (≈1 r/min) with naturally defect. The literature study of existing research, related to feature extraction methods or algorithms in a wide range of applications such as vibration analysis, time series analysis and bio-medical signal processing, is discussed. Some features are applied in vibration slew bearing data acquired from laboratory tests. The selected features such as impulse factor, margin factor, approximate entropy and largest Lyapunov exponent (LLE) show obvious changes in bearing condition from normal condition to final failure. Keywords: vibration-based condition monitoring; feature extraction; low-speed slew bearing 1. Introduction Slew bearings are large roller element bearings that are used in heavy industries such as mining and steel milling. They are particularly used in turntables, cranes, cranes, rotatable trolleys, excavators, reclaimers, stackers, swing shovels and ladle cars [1].
    [Show full text]
  • Crest Factors of Complementary-Sequence-Based Multicode MC-CDMA Signals Byoung-Jo Choi and Lajos Hanzo
    1114 IEEE TRANSACTIONS ON WIRELESS COMMUNICATIONS, VOL. 2, NO. 6, NOVEMBER 2003 Crest Factors of Complementary-Sequence-Based Multicode MC-CDMA Signals Byoung-Jo Choi and Lajos Hanzo Abstract—The crest factor properties of binary phase-shift be bounded by 4 dB upon carefully selecting the code sets keying modulated two- and four-code assisted multicarrier and upon employing high-rate crest-factor reduction coding, code-division multiple-access (MC-CDMA) employing comple- provided that the number of simultaneously used codes is mentary-sequence-based spreading sequences are characterized. More specifically, a complementary-sequence pair, a comple- higher than or equal to four. When is less than four, the mentary-sequence-based subcomplementary code pair, and a MC-CDMA signals employing orthogonal Gold (OGold) Sivaswamy’s complementary code set are studied. It was found codes, Frank codes [8], and Zadoff–Chu codes [10], [11] that the corresponding crest factors are bounded by 3 dB, which resulted in considerably lower crest factors. Even though corresponds to the crest factor of a single sine wave. This low crest Zadoff–Chu codes indeed do produce an ideal crest factor of factor resulted in a lower power loss and a lower out-of-band power spectrum due to clipping when the time-domain signal was less than 3 dB in the context of single-code MC-CDMA, the subjected to a typical nonlinear power amplifier, in comparison to crest factor values of neither Zadoff–Chu codes, Frank codes, those of Walsh code and orthogonal Gold code-spread MC-CDMA nor those of OGold codes are bounded by 3 dB in the context of signals.
    [Show full text]
  • An Efficient Hardware Implementation of the Peak Cancellation Crest Factor Reduction Algorithm
    DEGREE PROJECT IN INFORMATION AND COMMUNICATION TECHNOLOGY, SECOND CYCLE, 30 CREDITS STOCKHOLM, SWEDEN 2016 An efficient Hardware implementation of the Peak Cancellation Crest Factor Reduction Algorithm MATTEO BERNINI KTH ROYAL INSTITUTE OF TECHNOLOGY SCHOOL OF INFORMATION AND COMMUNICATION TECHNOLOGY An efficient Hardware implementation of the Peak Cancellation Crest Factor Reduction Algorithm MATTEO BERNINI Master’s Thesis at KTH Information and Communication Technology Supervisor: Shafqat Ullah Examiner: Johnny Öberg TRITA-ICT-EX-2016:187 Abstract An important component of the cost of a radio base station comes from to the Power Am- plifier driving the array of antennas. The cost can be split in Capital and Operational expenditure, due to the high design and realization costs and low energy efficiency of the Power Amplifier respectively. Both these cost components are related to the Crest Factor of the input signal. In order to reduce both costs, it would be possible to lower the average power level of the transmitting signal, whereas in order to obtain a more efficient transmis- sion, a more energized signal would allow the receiver to better distinguish the message from the noise and interferences. These opposed needs motivate the research and development of solutions aiming at reducing the excursion of the signal without the need of sacrificing its average power level. One of the algorithms addressing this problem is the Peak Cancellation Crest Factor Reduction. This work documents the design of a hardware implementation of such method, targeting a possible future ASIC for Ericsson AB. SystemVerilog is the Hardware Description Language used for both the design and the verification of the project, together with a MATLAB model used for both exploring some design choices and to val- idate the design against the output of the simulation.
    [Show full text]
  • Efficiency Investigation of Switch Mode Power Amplifier Drving Low Impedance Transducers
    Downloaded from orbit.dtu.dk on: Sep 26, 2021 Efficiency Investigation of Switch Mode Power Amplifier Drving Low Impedance Transducers Iversen, Niels Elkjær; Schneider, Henrik; Knott, Arnold; Andersen, Michael A. E. Published in: Proceedings of the139th Audio Engineering Society Convention. Publication date: 2015 Document Version Peer reviewed version Link back to DTU Orbit Citation (APA): Iversen, N. E., Schneider, H., Knott, A., & Andersen, M. A. E. (2015). Efficiency Investigation of Switch Mode Power Amplifier Drving Low Impedance Transducers. In Proceedings of the139th Audio Engineering Society Convention. Audio Engineering Society. General rights Copyright and moral rights for the publications made accessible in the public portal are retained by the authors and/or other copyright owners and it is a condition of accessing publications that users recognise and abide by the legal requirements associated with these rights. Users may download and print one copy of any publication from the public portal for the purpose of private study or research. You may not further distribute the material or use it for any profit-making activity or commercial gain You may freely distribute the URL identifying the publication in the public portal If you believe that this document breaches copyright please contact us providing details, and we will remove access to the work immediately and investigate your claim. Audio Engineering Society Convention Paper Presented at the 139th Convention 2015 October 29{November 1 New York, USA This paper was peer-reviewed as a complete manuscript for presentation at this Convention. Additional papers may be obtained by sending request and remittance to Audio Engineering Society, 60 East 42nd Street, New York, New York 10165-2520, USA; also see www.aes.org.
    [Show full text]
  • Crest Factor and Amplifier Power
    TechnAmplifier Specificationical Note Proposals Crest Factor and Amplifier Power Introduction In order to achieve maximum fidelity/performance in an audio system, the loudspeakers and the amplifiers must be able to accurately reproduce peaks in the order of 12dB above the expected RMS levels. Over the last few years, loudspeaker manufacturers have been developing products that meet these criteria. It is especially important in high power PA systems, where the number of boxes needs to be kept to a minimum. Defining Crest Factor The Crest Factor of a waveform is defined as the ratio of the peak amplitude level divided by the RMS signal level. As such, it is a dimensionless value, but can be expressed in audio applications in decibels (dB). The minimum possible Crest Factor has a value of 1, where the peak amplitude is the same as the RMS amplitude. This is the CF of a square wave (assuming this swings symmetrically about zero Volts). A pure sine wave, also swinging symmetrically about zero of the same peak amplitude will have a CF of 1.414 as the RMS value is now ( √2)/2 so we have CF = 1/(( √2)/2) = 1.414. Determining Power Requirements MC 2 have developed the E-series of amplifiers to match the power requirements of these loudspeakers. They will deliver huge amounts of peak power, but automatically reduce (limit) the power if the CF of the signal goes too low. This reduces the heat generated within the amplifier and keeps the mains power draw to an acceptable level. It also helps to protect the loudspeakers which could suffer serious damage for the same reason.
    [Show full text]
  • The Crest Factor in DVB-T (OFDM) Transmitter Systems and Its Influence on the Dimensioning of Power Components
    ® ® ® ® ® ® Products: R&S NV7000, R&S NV7001, R&S NV8200, R&S SV7000, R&S SV7002, R&S SV8000 ® ® ® DTV transmitters, R&S EFA, R&S FSP, R&S FSU test instruments The Crest Factor in DVB-T (OFDM) Transmitter Systems and its Influence on the Dimensioning of Power Components Application Note 7TS02 Are there really power peaks that are 20 dB above the average value? Yes, there are. Ever since the intro- duction of digital transmission technology, it has been necessary to deal with such orders of magnitude. RF power components must be suitably dimensioned to handle the expected voltage peaks and avoid break- ing down. If we can determine the crest factor, i.e. the ratio of the peak value to the average or RMS value, with sufficient accuracy (even though it can only be assessed statistically), then we will have solved the question of dimensioning power components. This Application Note is intended to help with this problem by providing some useful insights including basic formulas, some statistical background and a practical look at the limiting factors when it comes to dealing with real transmitter systems. Subject to change – Bernhard Kaehs, January 2007 – 7TS02_2E Introduction Contents 1 Introduction ............................................................................................. 2 2 Determination and Usage of the Crest Factor ........................................ 3 The crest factor of a modulated RF signal......................................... 3 Crest factor for multiple superimposed signals.................................. 5 Crest factor measurement ................................................................. 6 Output signal from a DVB-T transmitter............................................. 9 Output bandpass.............................................................................. 11 Interconnection of multiple transmitters on an antenna................... 12 3 The Crest Factor During Generation of a DVB-T Signal .....................
    [Show full text]
  • New Methods for HD Radio™ Crest Factor Reduction and Pre-Correction
    New Methods for HD Radio Crest Factor Reduction and Pre-correction April 12, 2015 NAB Show 2015 Featuring GatesAir’s Tim Anderson Kevin Berndsen Radio Product & Business Senior Signal Development Manager Processing Engineer Copyright © 2015 GatesAir, Inc. All rights reserved. New Methods for HD Radio Crest Factor Reduction and Pre-correction Timothy Anderson, CPBE Radio Product Development Manager, GatesAir Kevin Berndsen, MSEE Senior Signal Processing Engineer, GatesAir NAB 2015 Broadcast Engineering Conference Crest Factor and Intermodulation Distortion The biggest challenge in amplifying orthogonal frequency-division multiplexed (OFDM) waveforms used for HD Radio and all other digital radio formats is: a) High Crest Factor of multiple carriers. b) Intermodulation products of multiple carriers Crest Factor vs. Peak-Average Power Ratio . Crest factor is a measure of a waveform, such as alternating current, sound or complex RF waveform, . As a power ratio, PAPR is showing the ratio of peak values to the normally expressed in average value. decibels (dB). Crest Factor is defined as the peak . When expressed in decibels, amplitude of the waveform divided by Crest Factor and PAPR are the RMS value of the waveform equivalent . The peak-to-average power ratio (PAPR) is the peak amplitude squared (giving the peak power) divided by the RMS value squared (giving the average power). It is the square of the crest factor Peak Distribution . The Hybrid HD Radio™ system uses up to 534 Orthogonal Frequency Division Multiplexed (OFDM) subcarriers modulated at equally spaced frequencies and 90 degrees to one another. Statistically, with this number of orthogonally opposed subcarriers, there can and will occasionally be very high amplitude peaks due to vector summation of the carriers.
    [Show full text]
  • AN-1434 Crest Factor Invariant RF Power Detector (Rev. A)
    Application Report SNWA004A–January 2006–Revised April 2013 AN-1434 Crest Factor Invariant RF Power Detector ..................................................................................................................................................... ABSTRACT The objective of this application report is to demonstrate and briefly explain how the LMV232 Mean Square Power Detector from Texas Instruments can be used as an accurate RF power detector for bandwidth efficiency modulated RF transmission in a handset or mobile unit. Contents 1 Introduction .................................................................................................................. 2 2 Overview of Digital Modulation in Cellular Phones ..................................................................... 2 3 GMSK Modulation in GSM/GPRS ........................................................................................ 4 4 QPSK and 16QAM Modulation in 3G CDMA ............................................................................ 4 5 Crest Factor (CF) or Peak-to-Average Ratio (PAR) in Digitally-Modulated RF ..................................... 4 6 Power Definition and Measurement in Cellular Phone Systems ...................................................... 5 7 Peak Power Detector ....................................................................................................... 5 8 Average Power Detector ................................................................................................... 5 9 Mean Square Detection ...................................................................................................
    [Show full text]
  • Crest Factor Reduction Based on Artificial Neural Networks
    Crest Factor Reduction Based On Artificial Neural Networks Master’s thesis in Complex Adaptive Systems EDUARDO SESMA CASELLES Department of Electrical Engineering CHALMERS UNIVERSITY OF TECHNOLOGY Gothenburg, Sweden 2017 Master’s thesis 2017:108 Crest Factor Reduction Based On Artificial Neural Networks EDUARDO SESMA CASELLES Department of Electrical Engineering Chalmers University of Technology Gothenburg, Sweden 2017 Crest Factor Reduction Based On Artificial Neural Networks EDUARDO SESMA CASELLES © EDUARDO SESMA CASELLES, 2017. Supervisor: Amir Eghbali, Lab.gruppen AB Examiner: Henk Wymeersch, Department of Electrical Engineering Master’s Thesis 2017:108 Department of Electrical Engineering Chalmers University of Technology SE-412 96 Gothenburg Telephone +46 31 772 1000 Cover: Decibel level meter along with a symbolic representation of an Artificial Neural Network at the centre. Typeset in LATEX Gothenburg, Sweden 2017 iv Crest Factor Reduction based on Artificial Neural Networks EDUARDO SESMA CASELLES Department of Electrical Engineering Chalmers University of Technology Abstract Nowadays most audio amplifiers for professional use include advanced signal pro- cessing techniques. While most of these signal processing relate to adjusting the spectral characteristics of the sound (like equalization), many more hidden features aim to guarantee proper function of the amplifier in conditions close to its oper- ational limits. For example, how to handle situations like the main supply being insufficient or the input signal amplitude is too high for the amplifier to generate a proportional output. Notice that in both aforementioned problems, the output sig- nal ends up being clipped with obvious consequence on the perceived audio quality. To counteract this effect, specific signal processing is introduced: a so called "limiter" has the purpose of re-shaping the signal in order to produce a sort of instantaneous compression of the signal itself, aiming to minimize the perception of distortion.
    [Show full text]