Software for Efficient Measurement, Analysis and Monitoring of Sound and Vibrations

WaveImage is the most comprehensive solution for the monitoring and diagnosis of components, materials, engines, plants, devices and buildings. The software offers various possibilities for determining vibration characteristics by means of the most popular methods of the experimental and operational modal analysis. In the market, WaveImage is the only software providing this variety of combinations of algorithms for the modal analysis.

Software WaveImage
Software WaveImage: Determining the natural vibration of an airplane


Consistent monitoring of mechanical structures and buildings by optimization of maintenance- and quality management processes during the complete product lifecycle. WaveImage gives support as an automated complete system for early fault detection and non-destructive examination - this enables you to take care of daily tasks easily. The continious monitoring and automated maintenance offers several advantages like lower operational costs, higher safety and efficiency of processes.

Further application fields:

  • Engineering, construction, shipbuilding and automobile industry
  • Aerospace technology
  • Defence technology
  • Appliances and entertainment electronics
  • Building industry
WaveImage Monitoring
Software interface for online condition monitoring / quality control: green = no damage / red = damage

Software Components

WaveImage MODAL

WaveImage Modal is a software that allows determining vibrational properties via:

Operational modal analysis (OMA):
For structures with stochastic or environmental excitation, where a measurable and aimed excitation is not possible.

Experimental modal analysis (EMA):
For structures with measurable and aimed excitation (e.g. with an electrodynamic shaker or an impulse hammer)

Operating Deflection Shape analysis (ODS):
To calculate vibrational properties under actual operating conditions

Order Analysis (OA):
To analyse rotational structures

Environmental factors analysis (EFA):
For structures with environmental influences, such as temperature and humidity

Finite-Elemente-Analysis (FEA):
For simulating vibrational properties based on structural geometry and material  properties

Signed differential mapping (SDM):
To adjust the FE model to measured data-based modal results (through OMA and EMA)

Measurement data for dynamic structure analysis can be recorded by means of acceleration, speed and displacement transducers and processed using WaveImage Modal.


Automated and real-time system for online condition and structural health monitoring via sound

  • Modular user interface for the classification of sound data (structure-borne, air-borne and ultra sound) for the optimization of maintenance and quality control processes
  • For monitoring, a signature containing the sound response at several locations of the  corresponding structure with a suitable description of the current state is created under actual
    operating conditions
  • The selection of the characteristics and sensors for generating this signature and the definition of the classes are problem-related and can therefore be set separately in the software

Universal applicability for a wide range of applications

  • Modular system with 3 components for classification
  • The modular design allows for the single components to be compiled individually according to the requirements of the user
  • The modular design and diversity of various methods of artificial intelligence and signal processing presented here are currently not achieved by any other software on the market
WaveImage Monitoring
Flow chart for the design phase of the classifier

1. Component for Extraction:

  • Filtering (high or lowpass or bandpass)
  • (Background) noise removal

2. Component for Selection:

  • Usage of the preprocessed time data to characterize significant features
  • Methods from the time domain (eg. statistical measures eg kurtosis and skewness)
  • Methods from frequency domain (spectrum, octave spectrum, third-octave spectrum, spectrogram, scale, order spectrum)
  • The selected features are then used to construct the classifier (single or multiple class classifier possible)

3. Component for Classification:

  • Support Vector Machines  
  • Hidden Markov Models
  • Distance-based Classifiers (K-Means, Fuzzy-C-Means, Ellipsoid)
  • Distribution-based Classifier (Bayes Classifier)
  • Density-based Classifier (K-Nearest Neighbor)