Load Data - Analysis, Dimensioning and Simulation

Dynamic loads play a central role for the development of mechanically loaded systems. For a design with regard to functional properties like strength and vibration characteristics, a good insight of working loads matters.

Load Data Analysis, Loading Statistics and Operation Optimization

Load data on system or component level are usually represented as time signals. This applies to field data and rig loads as well as for numerical simulation. The analysis process starts with the detection and correction of irregularities due to possible problems in the measurement process. Often, spikes, drifts or offsets need to be corrected. Next, rainflow-type algorithms and spectral analysis is applied to extract important characteristics of the load signals. Which methods are to be applied with which priority depends on the loads as well as on the system.

Eigen frequencies of the system or component as well as the signal frequencies of the loads play an important role. Moreover, the analysis of the correlation of multi-input signals is very important. For the subsequent statistical analysis, scalar key values such as pseudo-damage values are calculated.

All of these load data analysis methods are applied within many projects at ITWM and extended or modified if necessary. Besides projects within the derivation of reference loads for durability, we also deal with condition monitoring or the optimization of workflow.

Example Project: Improved Efficiency for Commercial Vehicles

Tractor fieldwork
© Photo ITWM

Tractor fieldwork

The fuel consumption of a modern vehicle does not only depend on its mechanical characteristics, but also on the electronic controls for its engine, gear box, and other consumers. The better these can adapt to variable usage conditions, the higher the efficiency of the vehicle. Potential applications are managing a hybrid drive by taking the topography into account, or allocating power within an agricultural or construction machine.

In cooperation with John Deere, ITWM is currently assessing the potential of using operating data and geographical information for efficiency improvement. The goal is to obtain better information about the operating point without resorting to expensive sensors, and to make such information available for electronic control units and driver assistance systems. The approach couples a (simplified) system model of the tractor with a statistical description of the environment (soil, weather, etc.), as the latter is usually not known with sufficient accuracy.