Fault Diagnosis for Rotating Machinery Using Multiple Sensors and Convolutional Neural Networks
- Submitting institution
-
The University of Lancaster
- Unit of assessment
- 12 - Engineering
- Output identifier
- 280606319
- Type
- D - Journal article
- DOI
-
10.1109/TMECH.2017.2728371
- Title of journal
- IEEE/ASME Transactions on Mechatronics
- Article number
- -
- First page
- 101
- Volume
- 23
- Issue
- 1
- ISSN
- 1083-4435
- Open access status
- Deposit exception
- Month of publication
- July
- Year of publication
- 2017
- 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
- Yes
- Number of additional authors
-
4
- Research group(s)
-
-
- Proposed double-weighted
- No
- Reserve for an output with double weighting
- No
- Additional information
- This paper presents pioneering work in developing a highly accurate fault diagnosis approach that outperforms the vast majority of diagnosis results in aligned literature. The work was supported by the Canadian Mitacs Accelerate Program (grant no. IT06592) and conducted in collaboration with Istuary Innovation Group. It secured further funding from the Mitacs Accelerate Program (grant no. IT08608) with Istuary for further development and implementation. This paper has been identified as an ESI highly cited paper and has been widely cited internationally by a number of leading sensing and condition monitoring groups (e.g. GIT, Case Western, Maryland, Brunel).
- Author contribution statement
- -
- Non-English
- No
- English abstract
- -
