Aurora DataViewer

HGL Aurora DataViewer
  • Time & Frequency domain visualisation & analysis
  • Worldwide connectivity – Thin Client/Server Architecture
  • Interactive viewing & cursor interrogation
  • Mode definition & alignment
  • Order & mode tracking
  • Phase analysis with Cartesian and Polar plots
  • Third-octave analysis (also 1/6, 1/12, 1/24)
  • Rainflow and cycle-counting analysis


More features:

  • Shaft Centre-line & Orbit analysis
  • Data Manipulation and Derived Channel Generation
  • Data validation – remove erroneous data from analysis
  • Data tagging – highlight regions of specific interest
  • Workflow Persistency – the system ‘remembers’ previous analysis and recreates it when reloading a data file
  • Multi-function plot types
  • User-configurable displays
  • User-definable colour maps

Aurora-DataViewer is part of the Aurora Analysis system, an integrated analysis platform that provides all the tools and capabilities required for summary and detailed analysis of both time and frequency domain data from a single application window.

The Aurora-DataViewer is the primary application for viewing and analysing processed dynamic data interactively. It can be used to view either time-domain or frequency-domain calibrated data.

The standard plot displays include:

  • Spectral Density and Waterfall Plots
  • Time History Plot
  • Amplitude Envelope and Spectrum Plots
  • Spectral Peak-hold Plot
  • Reference Peak-hold / Spectral Component Plot
  • Phase plots between multiple parameters
  • Shaft Speed vs. Time Plot
  • Performance Parameter vs. Reference Plot
  • Orbit Plots
  • Fractional octave (or constant percentage band) plots

The Aurora DataViewer display also provides the user with the ability to generate their own display templates which they can save, edit and load. The configurable displays allow task-specific plot arrangements to be created for individual analysis tasks. These options are available in the time domain and the frequency domain.

Users may manipulate the measured data and create a Derived channel by applying mathematical operations to the original analysed data. This is particularly useful for applying temperature corrections to strain gauge calibrations. These operations may be chained together, so that the result of one mathematical operation may be used as the input to a further calculation.

Any validation changes, feature annotations (data tagging), or data manipulation operations applied by the user are saved between sessions, so that operations don’t have to be repeated and to allow other users to benefit from the work.