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Personal profile


  • 1983 Graduated from the University of Hull with a degree in Mathematics.
  • 1991 Returned to higher education studying a PGCE in Further Education at Huddersfield University. Successfully completed and- awardedJuly 1992.  Teaching experience was gained at Kirklees College and Universitat Greissfault, Neubrandenburg, Germany. In September 1992  I began lecturing within the School of Mathematics and Computing at the University of Huddersfield. Lecturing in theoretical and applied Mathematics and Statistics to a wide range of students across the University.
  • Having an increasing interest in statistics and, in particular its uses and applications, I completed the MSc (Applied Statistics) with Sheffield Hallam University (1994 to 1997) as a ‘distance learning student’. Completion of which led to the many exciting collaborative works I’ve been involved in with Calderdale and Kirklees Health Authority and more recently in the School of Engineering.
  • 2017 awarded Doctor of Philosophy (Engineering) from the University of Huddersfield in recognition of a programme of work entitled ‘Characterisation of condition monitoring information for diagnosis and prognosis using advanced statistical models’. Outside the University I am a member of Holmfirth Harriers Athletics Club and Colne Valley Singers.

Research Expertise and Interests

  • Condition Monitoring-statistical analysis of mechanical systems using vibration signal outputs. Fault detection in reciprocating compressors.
  • Modelling System Behaviours.
  • Input Volume Reduction Methodologies for Predictive Classifiers.
  • Predictive Analytics for Applied Process Monitoring.
  • Non-linear Systems Approaches to Detection of Deviant Events.
  • Autonomous Abnormality Assessment.
  • Evidence based health care.

Research Degree Supervision

Click Here to see all postgraduate research opportunities with Dr Ann Smith

Fingerprint Fingerprint is based on mining the text of the person's scientific documents to create an index of weighted terms, which defines the key subjects of each individual researcher.

  • 2 Similar Profiles
Reciprocating compressors Engineering & Materials Science
Classifiers Engineering & Materials Science
Fault detection Engineering & Materials Science
Statistical methods Engineering & Materials Science
Health Engineering & Materials Science
Condition monitoring Engineering & Materials Science
Monitoring Engineering & Materials Science
Variable speed drives Engineering & Materials Science

Network Recent external collaboration on country level. Dive into details by clicking on the dots.

Research Output 2014 2018

  • 3 Conference contribution
  • 2 Article

A Gas Path Fault Contribution Matrix for Marine Gas Turbine Diagnosis Based on a Multiple Model Fault Detection and Isolation Approach

Yang, Q., Li, S., Cao, Y., Gu, F. & Smith, A., 27 Nov 2018, In : Energies. 11, 12, 23 p.

Research output: Contribution to journalArticle

Open Access
Fault Detection and Isolation
Gas Turbine
Multiple Models
Fault detection
Gas turbines

Maintaining model efficiency, avoiding bias and reducing input parameter volume in compressor fault classification

Smith, A., Gu, F. & Ball, A., 23 Aug 2016, Proceedings of 2016 7th International Conference on Mechanical and Aerospace Engineering, ICMAE 2016. Institute of Electrical and Electronics Engineers Inc., p. 196-201 6 p. 7549534

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Open Access
Wavelet decomposition
Learning systems
Data reduction
Statistical methods
Reciprocating compressors
Signal analysis
1 Citations

The detection of shaft misalignments using motor current signals from a sensorless variable speed drive

Benghozzi, A., Smith, A., Gu, F. & Ball, A., 2015, Vibration Engineering and Technology of Machinery - Proceedings of VETOMAC X, 2014. Kluwer Academic Publishers, Vol. 23. p. 173-182 10 p.

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Variable speed drives
Fault detection
Failure analysis
Energy dissipation
4 Citations

Fault diagnosis of reciprocating compressors using revelance vector machines with a genetic algorithm based on vibration data

Ahmed, M., Smith, A., Gu, F. & Ball, A. D., 24 Oct 2014, ICAC 2014 - Proceedings of the 20th International Conference on Automation and Computing: Future Automation, Computing and Manufacturing. Institute of Electrical and Electronics Engineers Inc., p. 164-169 6 p. 6935480

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Reciprocating compressors
Failure analysis
Genetic algorithms