An automated feature extraction method with application to empirical model development from machining power data
- Submitting institution
-
Loughborough University
- Unit of assessment
- 12 - Engineering
- Output identifier
- 743
- Type
- D - Journal article
- DOI
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10.1016/j.ymssp.2019.01.023
- Title of journal
- Mechanical Systems and Signal Processing
- Article number
- -
- First page
- 21
- Volume
- 124
- Issue
- -
- ISSN
- 0888-3270
- Open access status
- Compliant
- Month of publication
- February
- Year of publication
- 2019
- URL
-
-
- Supplementary information
-
-
- 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
-
3
- Research group(s)
-
-
- Proposed double-weighted
- No
- Reserve for an output with double weighting
- No
- Additional information
- The feature extraction algorithms reported in this paper have been generalised and applied within a number of industrially based application services within the automotive industry. For example: (i) in 2015-current to determine the location and cleanliness of returnable transit items (RTI) that transport components. System eradicated a global engine warranty issue (£30M) traced to RTI cleanliness, (ii) in monitoring services for high value portable components. (iii) for the evaluation of a novel manufacturing solution to identify sources of variability of engine crankshafts.
- Author contribution statement
- -
- Non-English
- No
- English abstract
- -
