Modules​​​​​​​​​​​​​​

Visualize and process data in time domain

• Calculate and display statistical values of several measurement files
• Data processing using the formula compiler with mathematical and logical functions including an automatic strain gauge - rosette evaluation
• Analysis and correction of measurement errors by removing signal drifts, outliers/spikes, shifts/offsets or subtraction of the mean value
• Calculation of the external forces of a component based on calibrated strain gauges
• Integration and differentiation of signals
• Extraction or combination of measurement channels
• Append and repeat different time signals
• Convert data into the supported binary and ASCII formats
• Filtering of time signals (low pass, high pass, band pass, band stop, frequency response, filter curve, moving average, Savitzy-Golay)
• Data reduction taking into account maxima, minima, extreme values or mean value
• Interpolating and resampling signals as well as expanding/compressing defined time ranges
• Cut out areas manually or automatically (based on GPS coordinates for example)

Processing and visualization of time data based on classifications and calculated damage values

• Rainflow classification and derived level crossing, range count, peak/valley, staircase collective, ...
• Multidimensional rainflow counting
• Time at level counting up to 3 dimensions.
• Counting in relation to distance
• Processing of Rainflow results (deletion, addition/multiplication, extrapolation)
• Lifetime analysis based on the Miner algorithm and real or synthetic S/N curves
• Damage equivalent peak/valley data reduction for FEM and test bench investigations, lifetime prediction
• Damage based track mixing (e.g.: customer use vs. test track)
• Interface to FEMFAT max
• Damage-based data reduction
• Damage trend in the time domain
• Load cycle reduction
• Block program generation

Virtual iteration is based on determination of the excitation of a model in the time domain using dynamic simulation (usually multi-body simulation). Using the iteration process with simulation analogous to the real test bench allows to adjust external loadings on a structure in such a way that internal measurements, i.e. proper load flow, ca be reproduced with desired accuracy (solution of a non-linear inverse problem). This can be used to replace procedures such as the time-consuming measurement of bearing or spindle loads. However, measuring the internal signals are usually relatively simple (e.g. wheel center or body accelerations, suspension travels, etc.). 
Another important aspect is that with the virtual iteration, road profiles can also be determined using full vehicle simulation.

The following possibilities and advantages result using virtual iteration:

• Excellent convergence between measurement and simulation
• Efficient method to generate absolute displacements (e.g. vertical displacement at tire patch, frame movement for add on parts like cab, tank, engine, exhaust system)
• Transfer to similar vehicles (invariant load)
• Full vehicles, subsystems and test benches can be simulated based on real road load data measurements
• Convenient and automated process for Adams, SIMPACK, MotionSolve, RECURDYN and VI-GRADE
• Generation of 3D road based on measured responses of a full vehicle

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If the correlation of dynamic simulation results and measured signals differs a lot from the real behavior, the model must be analyzed in detail for modifications to get a better comparability. This manual process of parameter modification during the simulation is a typical step in the workflow of a project. This process can be very complex and time consuming.

FEMFAT LAB model improvement allows to improve model parameters of an MSC.ADAMS model automatically based on road load data (RLD). Typical used signals of RLD in such a process are accelerations, relative displacements or angles, strain gauges, load cells or wheel force transducer.

The following possibilities and advantages result using model improvement:

• Supports optimization of linear and nonlinear parameters (mass properties, stiffness, damping, …)
• A diagnostic tool supports the identification of the relevant parameters (parameter influence analysis)
• Measured signals are reproduced as best as possible regarding to relative damage or RMS value
• Fast and easy to use algorithm (few simulations required)

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