A method for detection and characterisation of structural non-linearities using the Hilbert transform and neural networks
- Submitting institution
-
Imperial College of Science, Technology and Medicine
- Unit of assessment
- 12 - Engineering
- Output identifier
- 282
- Type
- D - Journal article
- DOI
-
10.1016/j.ymssp.2016.06.008
- Title of journal
- Mechanical Systems and Signal Processing
- Article number
- -
- First page
- 210
- Volume
- 83
- Issue
- 1
- ISSN
- 0888-3270
- Open access status
- Compliant
- Month of publication
- June
- Year of publication
- 2016
- URL
-
-
- Supplementary information
-
10.1016/j.ymssp.2016.06.008
- Request cross-referral to
- -
- Output has been delayed by COVID-19
- No
- COVID-19 affected output statement
- -
- Forensic science
- No
- Criminology
- No
- Interdisciplinary
- No
- Number of additional authors
-
2
- Research group(s)
-
-
- Proposed double-weighted
- No
- Reserve for an output with double weighting
- No
- Additional information
- The work introduced a new and efficient way to accurately detect and characterise the presence of nonlinearities in experimentally obtained frequency response functions. The simplicity and robustness of the approach has led to an integration into the Rolls-Royce Dynamic Data analysis toolbox, enabling for the first time the interpretation of challenging engine test data (contact: FoEREF@ic.ac.uk). The presented algorithms were used for the interpretation of the complex Trent XWB stator nonlinear responses. The work also formed the basis for the awarded EPSRC prosperity grant CORNERSTONE WP3 “Dynamic Response and Load Management” (EPSRC EP/R004951/1 and Rolls-Royce: £2m).
- Author contribution statement
- -
- Non-English
- No
- English abstract
- -