EN 15433-4:2007
(Main)Transportation loads - Measurement and evaluation of dynamic mechanical loads - Part 4: Data evaluation
Transportation loads - Measurement and evaluation of dynamic mechanical loads - Part 4: Data evaluation
This standard presents guidelines for the instruments, procedures and parameters, used to analyse dynamic data. It is assumed that the person performing the analyses has the use of appropriate digital FFT signal processors or FFT computers.
These guidelines are also applicable for other types of signal processing procedures, as long as the analysing parameters are equivalent. Such other procedures contain correlation algorithms e.g. Blackman-Tuckey), digital band pass filter algorithms or heterodyne techniques.
An outline of the data analysis procedures covered in this section is presented in Figure 1.
Transportbelastungen - Messen und Auswerten von mechanisch-dynamischen Belastungen - Teil 4: Datenauswertung
Diese Norm enthält Leitlinien für die Instrumente, Verfahren und Parameter, die bei der Auswertung dynamischer Daten eingesetzt werden. Es wird vorausgesetzt, dass die Person, die die Auswertungen durchführt, über geeignete FFT-Signalverarbeitungsprozessoren oder FFT-Rechner verfügt.
Diese Leitlinien gelten auch für andere Arten von Signalverarbeitungsverfahren, solange die Auswertungs¬parameter gleichwertig sind. Derartige andere Verfahren enthalten Korrelationsalgorithmen (z. B. nach Blackman-Tuckey), digitale Bandpassfilter-Algorithmen oder Überlagerungstechniken.
Ein Überblick über die in diesem Teil beschriebenen Datenauswertungsverfahren ist in Bild 1 dargestellt.
Charges de transport - Mesurage et analyse des charges mécaniques dynamiques - Partie 4: Evaluation des données
La présente norme donne des directives relatives aux instruments, aux procédures et aux paramètres utilisés pour analyser les données dynamiques. Il est supposé que la personne qui effectue les analyses dispose de processeurs de signaux TFR numériques ou d’ordinateurs TFR appropriés.
Ces directives sont également applicables à d’autres types de procédures de traitement des signaux, tant que les paramètres d’analyse sont équivalents. Ces autres procédures contiennent des algorithmes de corrélation, par exemple de Blackman-Tuckey, des algorithmes de filtre de passe-bande numériques ou des techniques hétérodynes.
Un plan des procédures d’analyse de données prises en compte dans la présente section est illustré à la Figure 1.
Obremenitve pri transportu - Merjenje in analiza dinamično mehanskih obremenitev - 4. del: Vrednotenje podatkov
General Information
Standards Content (Sample)
2003-01.Slovenski inštitut za standardizacijo. Razmnoževanje celote ali delov tega standarda ni dovoljeno.Transportation loads - Measurement and analysis of dynamic mechanical loads - Part 4: Data evaluationNLKCharges de transport - Mesurage et analyse des charges mécaniques dynamiques - Partie 4 : Evaluation des donnéesTransportbelastungen - Messen und Auswerten von mechanisch-dynamischen Belastungen - Teil 4: DatenauswertungTa slovenski standard je istoveten z:EN 15433-4:2007SIST EN 15433-4:2008en,de55.180.01ICS:SLOVENSKI
STANDARDSIST EN 15433-4:200801-februar-2008
EUROPEAN STANDARDNORME EUROPÉENNEEUROPÄISCHE NORMEN 15433-4December 2007ICS 55.180.01 English VersionTransportation loads - Measurement and evaluation of dynamicmechanical loads - Part 4: Data evaluationCharges de transport - Mesurage et analyse des chargesmécaniques dynamiques - Partie 4: Evaluation desdonnéesTransportbelastungen - Messen und Auswerten vonmechanisch-dynamischen Belastungen - Teil 4:DatenauswertungThis European Standard was approved by CEN on 28 October 2007.CEN members are bound to comply with the CEN/CENELEC Internal Regulations which stipulate the conditions for giving this EuropeanStandard the status of a national standard without any alteration. Up-to-date lists and bibliographical references concerning such nationalstandards may be obtained on application to the CEN Management Centre or to any CEN member.This European Standard exists in three official versions (English, French, German). A version in any other language made by translationunder the responsibility of a CEN member into its own language and notified to the CEN Management Centre has the same status as theofficial versions.CEN members are the national standards bodies of Austria, Belgium, Bulgaria, Cyprus, Czech Republic, Denmark, Estonia, Finland,France, Germany, Greece, Hungary, Iceland, Ireland, Italy, Latvia, Lithuania, Luxembourg, Malta, Netherlands, Norway, Poland, Portugal,Romania, Slovakia, Slovenia, Spain, Sweden, Switzerland and United Kingdom.EUROPEAN COMMITTEE FOR STANDARDIZATIONCOMITÉ EUROPÉEN DE NORMALISATIONEUROPÄISCHES KOMITEE FÜR NORMUNGManagement Centre: rue de Stassart, 36
B-1050 Brussels© 2007 CENAll rights of exploitation in any form and by any means reservedworldwide for CEN national Members.Ref. No. EN 15433-4:2007: E
It is part of a complete normative concept to acquire and describe the loads acting on goods and influencing them during transport, handling and storage. This standard becomes significant when related to the realisation of the European Directive on Packaging and Packaging Waste (Directive 94/62 EC, 20 December 1994). This directive specifies requirements on the avoidance or reduction of packaging waste, and requires that the amount of packaging material is adjusted to the expected transportation load, in order to protect the transportation item adequately. However, this presumes some knowledge of the transportation loads occurring during shipment. At present, basic standards, based on scientifically confirmed values, which can adequately describe and characterize the magnitudes of transportation loads, especially in the domain of dynamic mechanical loads do not exist nationally or internationally.
Reasons for this are mainly the absence of published data, insufficient description of the measurements or restrictions on the dissemination of this information. This standard will enable the measurement and evaluation of dynamic mechanical transportation loads, thus enabling the achievement of standardized and adequately documented load values. This series of standards consists of the following parts: Part 1: General requirements,
Part 2: Data acquisition and general requirements for measuring equipment; Part 3: Data validity check and data editing for evaluation; Part 4: Data evaluation;
Part 5: Derivation of Test Specifications; Part 6: Automatic recording systems for measuring randomly occurring shock during monitoring of transports.
It is assumed that the person performing the analyses has the use of appropriate digital FFT signal processors or FFT computers.
These guidelines are also applicable for other types of signal processing procedures, as long as the analysing parameters are equivalent. Such other procedures contain correlation algorithms e.g. Blackman-Tuckey), digital band pass filter algorithms or heterodyne techniques. An outline of the data analysis procedures covered in this section is presented in Figure 1.
Measured signals3.1)
Instantaneous values3.2)
Average values3.3)
Synchronous averages3.4)
Filtered signals4)
Data classification4.2)
Time dependence4.3)
Randomness4.4)
Normality7)
Dual channel analysis7.2)
Cross-spectra7.3)
Coherence7.4)
Frequency response7.5)
Cross-correlation7.6)
Correlation coefficient7.7)
Unit impulse response5)
Single channel spectral analysis5)
Periodic and random data5.1)
FFT algorithms5.2)
Periodic data (inner spectra)5.3)
Stationary random data
(Auto or power spectra)5.4)
Non-stationary data6)
Transient data6.2)
Fourier spectra6.3)
Energy spectra6.4)
Shock response spectra8)
Other analyses8.2)
Probability density5.5)
Proportional bandwidth
(1/3 octave bandwidth) Figure 1 — Outline of data analysis
ˆ µγx=1Tx(t)dt=1N0T∫x(n∆t)n=1N∑
(1) b) RMS value:
ˆ ψγx=1Tx2(t)dt0T∫γγγγγγ1/2=1Nx2(n∆t)n=1N∑γγγγγγ1/2
(2)
Without the DC component, the mean value of the signal is zero and the RMS value computation of equation (2) will yield the standard deviation. 3.2.2 Instruments and software The averaging time constant is a key parameter in establishing the accuracy of average value estimates for random signals. The operations in equations (1) to (3) are easily accomplished on a digital computer with simple software programs. NOTE Both analogue and digital DC voltmeters essentially compute the mean value of a signal, while true RMS voltmeters (not to be confused with AC voltmeters) compute an approximation of the RMS value of a signal. Most analogue and digital voltmeters compute a continuous exponential weighted (RC) average, rather than a single linear average. 3.2.3 Types of averaging Various methods are common for computing average values. The procedures and parameters used to perform the computation should be detailed.
In the case of stationary or steady state signals (where the average value of interest does not change much over the duration of the measurement), a linear average over the entire measurement is recommended. In the case of non-stationary signals (where the average value of interest is changing considerably over the measurement duration), an exponential weighted average is recommended for analogue signals, and a step-wise linear average is recommended for digital signals. NOTE A step-wise linear average (sometimes called a running average) can be produced by computing a series of average value estimates using N data values, where n new values are added to the end and n old values are discarded from the beginning of the N data values for each average. This will produce correlated average value estimates every ∆t seconds (n = 1, N). 3.2.4 Averaging time and sampling errors For periodic signals, the only error in an average value estimate (beyond calibration errors) is the truncation error caused by the fact that the averaging operation may not cover an exact integer number of cycles of the signal. This truncation error becomes negligible as the linear averaging time becomes long relative to the period of the signal. For random signals, however, there will be a random sampling error in the average value estimate that is dependent on both the averaging time and the frequency bandwidth of the signal. The normalized random error εr in the estimate of a parameter Φ is defined as:
εrˆ Φγ[]=σˆ Φγ[]Φ
(4) where
[]Φσ is the standard deviation of the estimate$Φ. NOTE The random errors for the estimates of the mean and RMS values of random signals are summarized in Figure 2 in terms of a normalized random error (coefficient of variation). The quantity B in Figure 2 is the frequency bandwidth of the signal, assumed to have a uniform spectrum, and the quantity T is the linear averaging time used to make the estimate. In the case of exponentially weighted averages with a time constant K, it can be assumed from Figure 2 that T = 2K. XY1,000,100,0110100100012 Key X-axis
For mean values: BT/(1/µ)2 ; For RMS values BT Y-axis
Normalised random error 0r 1
Mean value: 0r = (2/BT)½ (1/µ) 2
RMS value(µ=0): 0r = 0,5/(BT)½ Figure 2 — Normalized random errors for mean and mean square value estimates 3.3 Synchronous averaging 3.3.1 General Reciprocating and rotating machinery operating under steady-state conditions produce periodic components (i.e. signals that exactly repeat themselves after a time interval T1, called the period) such that: p(t) = p(t + iT1); i = 1, 2, 3,.
(5)
(6) 3.3.2 Instruments and software Many of the modern special-purpose signal processing computers produced for dynamic signal analyses provide a synchronous averaging mode.
A trigger signal should be provided to the analyser that will initiate new signal segments at a desired instant during a period p(t).
The averaging may be accomplished directly on the signal segments, or in the frequency domain on the Fourier transforms of the segments. 3.3.3 Triggering procedures Synchronous averaging is most effective when the trigger signal is a noise-free indicator of the phase during each period p(t). The time base accuracy of the trigger signal determines the accuracy of the magnitude of the resulting synchronous averaged signal, i.e. time base errors in the signal cause a reduction in the indicated signal amplitude with increasing frequency. 3.3.4 Signal-to-noise enhancement The signal-to-noise level enhancement for a synchronous averaged signal is shown in Figure 3, where q is the number of segments used in the ensemble averaging operation. Letting σx and σn be standard deviations of the signal and noise, respectively, the signal-to-noise level enhancement in decibels is defined as: /10log/ea10bSNSNRSNR=
(7) where
SNRa
= (σx/σn) 2 after the synchronous averaging;
SNRb
is the same ratio before the synchronous averaging.
S/N = 10 log10 q Figure 3 — Signal-to-noise level enhancement with synchronous averaging 3.4 Filtered signals 3.4.1 General The detailed characteristics of the preferred filters should be known, and their possible consequences on the interpolations of the resulting signals should be carefully assessed. NOTE Vibration data are often acquired over a wider frequency range than may be of interest for certain applications. It is common in such applications to low pass filter the signals to obtain time histories representing only the low frequency portion of the signal. In a large number of cases, the low frequency signals are digitized and used as inputs to finite element computations. The practice of defining maximum low frequency loads using low pass filtered signals involves a subjective judgement in that the resulting signal is heavily dependent on the cut-off frequency, roll-off rate and phase shift of the low pass filters. Occasionally, dynamic data signals are high pass filtered to AC couple the data. 3.4.2 Analogue filtering The simplest way to limit the frequency range of a signal is to low pass filter the analogue signal directly. If the signal is to be later digitized, the low pass filtering can be easily accomplished using the anti-aliasing filter for the A/D converter. NOTE Many analogue anti-aliasing filters introduce a non-linear phase shift near the filter cut-off frequency that may distort the signal time history.
a) YX b) YX c) YX Key X-axis Time t Y-axis Instantaneous value x(t) Figure 4 — Periodic (a), random (b) and mixed signals (c) 4.2.3 Non-stationary data In most cases the time dependence of a signal is in question, and therefore a statistical test for stationarity is necessary. For this purpose a moving average is performed over the measuring duration. NOTE Non-stationary signals are those which are ongoing, but have at least one important average property that varies with time, as illustrated in Figure 5b). In many cases, a time-varying property of a measured signal can be anticipated from the characteristics of the experiment producing the signal.
It follows that the distinction between non-stationary and transient data in this case is dependent on the response characteristics of the test item. One way to assess the response time of a non-stationary excitation is in terms of the mean square response time of a single degree-of-freedom system to a step random excitation, given as: n14fτζ≈
(8) where τ is the time required for a single degree-of-freedom system to reach 95 % of full mean square response; ζ is the damping ratio of a single degree-of-freedom system;
For example, if fn ≈ 50 Hz and ζ ≈ 0,025, then τ ≈ 0,2 s. If the time varying mean square value of a non-stationary signal varies from a minimum to a maximum in less than 0,2 s, then the signal should be viewed as a transient for testing purposes, and correspondingly should be analysed as a transient. NOTE For this testing approach to be valid, the time variations in the non-stationary environment should be slow compared to the response characteristics of the item to be tested, so that the test item would essentially have a fully developed response at any instant during the non-stationar
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