Len Gelman at University of Huddersfield, United Kingdom.
Professor. Dr. Len Gelman, PhD, DSc, is an internationally recognized authority in signal processing and condition monitoring. With a career spanning academia and industry, he has held prestigious roles at the University of Huddersfield and Cranfield University, and maintains active collaborations worldwide. A prolific researcher and educator, he has supervised over 175 MSc/BSc theses and 10 PhD dissertations, co-authored educational materials used globally, and developed award-winning MSc modules. He has delivered 40+ keynote lectures internationally, chaired 30+ international conferences, and contributed extensively to educational and professional training in Europe, Asia, Africa, and the Americas. His contributions have earned him numerous accolades, including the Rolls-Royce Innovation Award, William Sweet Smith Prize, and the Acoustical Society of America Award.
Publication Profile
Scopus
Google Scholar
Educational Details
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Doctor of Science (DSc, Habilitation) in Engineering
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PhD in Engineering
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MSc (Hons) in Engineering
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BSc (Hons) in Engineering
Professional Experience
Professor Len Gelman is a distinguished academic and research leader in signal processing and condition monitoring with over 25 years of academic and industrial experience. He is currently Professor and Chair at the University of Huddersfield, where he also serves as Director of the Maintenance Centre for Efficiency and Performance Engineering (2017–present). Prior to this, he held the Chair in Vibro-Acoustical Monitoring at Cranfield University (2002–2017). He has also held visiting professorships at globally recognized institutions such as Tsinghua University, Shanghai Jiao Tong University, Aalborg University, Auburn University, and more. His leadership roles include Chairmanship of the International Society for Condition Monitoring, Executive Director of ISCM, and past President of the International Institute of Acoustics and Vibration (USA). He has coordinated and led UKRI, EPSRC, and EU-funded projects, securing over £12 million in funding across his career, and worked with industry leaders including Rolls-Royce, Shell, SKF, and Caterpillar.
Research Interest
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Digital and nonlinear signal processing
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Condition monitoring and prognostics
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Vibro-acoustic diagnostics
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Fault detection and structural health monitoring
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Pattern recognition and AI for predictive maintenance
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Non-destructive testing and mechatronics
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Applications across aerospace, automotive, energy, and manufacturing industries
Author Metrics
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Total Citations: 2,223
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h-index: 24
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i10-index: 61
Top Noted Publication
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An automated methodology for performing time synchronous averaging of a gearbox signal without speed sensor
Authors: F. Combet, L. Gelman
Journal: Mechanical Systems and Signal Processing
Volume: 21, Issue 6, Pages: 2590–2606
Citations: 282
Year: 2007
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Optimal filtering of gear signals for early damage detection based on the spectral kurtosis
Authors: F. Combet, L. Gelman
Journal: Mechanical Systems and Signal Processing
Volume: 23, Issue 3, Pages: 652–668
Citations: 257
Year: 2009
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Adaptive vibration condition monitoring technology for local tooth damage in gearboxes
Authors: G. L., R. Zimroz, J. Birkel, H. Leigh-Firbank, D. M. Simms, B. Waterland, et al.
Journal: Insight – Non-Destructive Testing and Condition Monitoring
Volume: 47, Issue 8, Pages: 461–464
Citations: 67*
Year: 2005
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Novel detection of local tooth damage in gears by the wavelet bicoherence
Authors: F. Combet, L. Gelman, G. LaPayne
Journal: Mechanical Systems and Signal Processing
Volume: 26, Pages: 218–228
Citations: 61
Year: 2012
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Design and Off-Design Operation and Performance Analysis of a Gas Turbine
Authors: SI Ao, L. Gelman, D.W.L. Hukins, A. Hunter, A. Korsunsky
Journal: International Association of Engineers
Citations: 56
Year: 2016
Conclusion
Prof. Dr. Len Gelman is a highly deserving candidate for the Best Researcher Award, given his outstanding contributions to signal processing, condition monitoring, and fault detection. His career spans decades of impactful research that has influenced both academic theory and industrial applications. With a strong publication record, a proven ability to secure research funding, and extensive global influence through collaborations and leadership roles, Prof. Gelman stands out as an exemplar in his field.
While his work has already left an indelible mark on the industry, further integrating modern technologies like AI and expanding into emerging sectors could elevate his contributions to even greater heights. Overall, Prof. Gelman’s comprehensive academic, industrial, and educational impact makes him a highly suitable candidate for this prestigious award, and his continued contributions will likely shape the future of condition monitoring and signal processing.