Contact Dr Samir Khan
- Tel: +44 (0) 1234 754667
- Email: Samir.S.Khan@cranfield.ac.uk
Areas of expertise
- Communications Systems
- Computing, Simulation & Modelling
- Instrumentation, Sensors and Measurement Science
- Systems Engineering
Background
Dr. Samir Khan his BEng and PhD from Loughborough University. He was the leading researcher working on the No-Fault Found research project between 2011-2015, at the Through-life Engineering Services Centre within Cranfield University, collaborating with Rolls-Royce, Jaguar Land Rover, BAE Systems, and MoD. In particular, he has developed machine learning solutions for aeronautical systems. Other research work includes performing fault analysis and condition monitoring from track-side feedback sensors, development of IoT technologies, analytics platforms for maintenance decision making and intelligent monitoring of intermittent failures and false alarms in electronic systems. He is a chartered engineer and a member of IEEE and IET.
Current activities
Dr Khan is currently involved in:
Achieving deliverables of the 'No-Fault Found' (NFF) research project
A member of the 'No-Fault Found' working group
Delegation of research tasks and overseeing project progress
Liaising with British Standards Institute (BSI) and working towards the standardisation of NFF taxonomy
Evaluating clients NFF costs involved during their maintenance
Organisation of the industrial day/seminars for dissemination of research results
Publication of the research work
Teaching and supervision of research and PhD students
Health and Safety Manager, First Aider and Fire Marshall.
Clients
- BAE Systems PLC
- Bombardier Inc
- Ministry of Defence
- Rolls-Royce Holdings PLC
Publications
Articles In Journals
- Kontaxoglou A, Tsutsumi S, Khan S & Nakasuka S. (2024). Multifidelity Framework for Small Satellite Thermal Analysis. Journal of Spacecraft and Rockets, 61(1)
- Khan S, Yairi T, Tsutsumi S & Nakasuka S. (2024). A review of physics-based learning for system health management. Annual Reviews in Control, 57
- Del Amo IF, Erkoyuncu JA, Bulka D, Farsi M, Ariansyah D, .... (2024). Advancing fault diagnosis through ontology-based knowledge capture and application. IEEE Access, 12
- Oyedeji OA, Khan S & Erkoyuncu JA. (2024). Application of CNN for multiple phase corrosion identification and region detection. Applied Soft Computing, 164
- Oyedeji OA, Khan S & Erkoyuncu JA. (2024). Leveraging Ontology Development to Enhance Corrosion Visualisation in Engineering Design. Procedia CIRP, 128
- Deng H, Khan S & Erkoyuncu JA. (2024). An Investigation on Utilizing Large Language Model for Industrial Computer-Aided Design Automation. Procedia CIRP, 128
- Mohamed NH, Khan S & Jagtap S. (2024). Waste 4.0: transforming medical waste management through digitalization and automated segregation. Discover Sustainability, 5(1)
- Palmarini R, Fernández del Amo I, Ariansyah D, Khan S, Erkoyuncu JA, .... (2023). Fast augmented reality authoring: fast creation of AR step-by-step procedures for maintenance operations. IEEE Access, 11
- Mohamed NH, Khan S & Jagtap S. (2023). Modernizing medical waste management: unleashing the power of Internet of Things (IoT). Sustainability, 15(13)
- Khan S, Tsutsumi S, Yairi T & Nakasuka S. (2021). Robustness of AI-based prognostic and systems health management. Annual Reviews in Control, 51
- Khan S, Farnsworth M, McWilliam R & Erkoyuncu JA. (2020). On the requirements of digital twin-driven autonomous maintenance. Annual Reviews in Control, 50
- Erkoyuncu JA & Khan S. (2020). Olfactory-based augmented reality support for industrial maintenance. IEEE Access, 8
- Khan S & Yairi T. (2020). Diagnosing Intermittent Faults through Non-linear Analysis. IFAC-PapersOnLine, 53(2)
- Khan S, Liew CF, Yairi T & McWilliam R. (2019). Unsupervised anomaly detection in unmanned aerial vehicles. Applied Soft Computing, 83
- Erkoyuncu JA, Roy R, Shehab E, Durugbo C, Khan S, .... (2018). An effective uncertainty based framework for sustainable industrial product-service system transformation. Journal of Cleaner Production, 208
- Khan S, Gorringe C & Farnsworth M. (2018). Evaluating diagnostic failures during system design. CIRP Journal of Manufacturing Science and Technology, 21
- McWilliam R, Khan S, Farnsworth M & Bell C. (2018). Zero-maintenance of electronic systems: Perspectives, challenges, and opportunities. Microelectronics Reliability, 85
- Khan S & Yairi T. (2018). A review on the application of deep learning in system health management. Mechanical Systems and Signal Processing, 107
- Erkoyuncu JA, Khan S, Eiroa AL, Butler N, Rushton KR, .... (2017). Perspectives on trading cost and availability for corrective maintenance at the equipment type level. Reliability Engineering & System Safety, 168
- Khan S, Farnsworth M & Erkoyuncu JA. (2016). A novel approach for No Fault Found decision-making. CIRP Journal of Manufacturing Science and Technology, 17
- Khan S, Phillips P & Jennions IK. (2015). Studying the impact of intermittent variations using sensitivity analysis. International Journal of Condition Monitoring, 5(3)
- Erkoyuncu JA, Khan S, Hussain SMF & Roy R. (2015). A framework to estimate the cost of No-Fault Found events. International Journal of Production Economics, 173
- Khan S. (2015). Research study from industry-university collaboration on “No Fault Found” events. Journal of Quality in Maintenance Engineering, 21(2)
- Khan S. (2015). Maintenance Requirements in Aerospace Systems. Procedia CIRP, 38
- Khan S, Phillips P, Jennions IK & Hockley C. (2013). No Fault Found events in maintenance engineering Part 1: current trends, implications and organizational practices. Reliability Engineering & System Safety, 123
- Khan S, Phillips P, Hockley C & Jennions IK. (2013). No Fault Found events in maintenance engineering Part 2: Root causes, technical developments and future research. Reliability Engineering & System Safety, 123
- Khan S, Goodall RM & Dixon R. (2013). Non-uniform sampling strategies for digital control. International Journal of Systems Science, 44(12)
- McWilliam R, Khan S & Purvis A. Modelling the Positional and Orientation Sensitivity of Inductively Coupled Sensors for Industrial IoT Applications. International Journal of Simulation: Systems, Science & Technology
- DENG H, Namoano B, ZHENG B, Khan S & Ahmet Erkoyuncu J. From Prediction to Prescription: Large Language Model Agent for Context-Aware Maintenance Decision Support. PHM Society European Conference, 8(1)
- Nishikawa T, Khan S, Tsutsumi S, Omata N, Shibukawa T, .... Regularized Regression Techniques for Model Reduction in Spacecraft Thermal Analysis. Journal of Spacecraft and Rockets