About

Hesham Azzam

After a career spanning 39 years and from November 2024, Dr Hesham Azzam will continue to support his customers as a self-employed sole trader under the name of HAHN Spring.

During the 39-years, he founded HAHN Spring Limited, which was active from September 2010 to October 2024. Before then, he led technology developments at MJA Dynamics, Smiths Aerospace, GE Aviation Systems. He holds a BSc and was awarded an MSc and a PhD from the University of Southampton. He operated as a Chartered Engineer, a Member of the Royal Aeronautical Society and a Member of SAE International. In 1997 and 2005, the Institution of Mechanical Engineers elected him for two Safety Awards.

Dr Azzam’s developments have been embedded into Fleet and Usage Management Systems (FUMS) and Health and Usage Monitoring Systems (HUMS). He published more than 50 public domain papers; he has:

Led the development of tools that can operate on huge datasets and can be hosted in standalone or web-based software; the tools can be used for usage monitoring, signal processing, statistical analysis, anomaly detection, and can mimic the functions of HUMS and FUMS.
Led the development of FUMS for MOD platforms and the capture of F35 requirements for a Force Life Management off-board system.
Led a SAE international committee to develop the sections of ARP6461 that was published in September 2013 and covered guidance on validating, verifying and certifying SHM systems for civil aircraft.
Led a SAE international committee to develop AIR6245 that was published in November 2018 and covered perspectives on validating, verifying and certifying SHM systems for military aircraft.
Developed algorithms for an Oil & Gas condition-based system that can detect the failure modes of mechanical components for safe containment of hazardous fluids.
Developed advanced Artificial Intelligence (AI) tools including a fast-to-learn, easy-to-configure neural network that can learn models at a very fine resolution maintaining the accuracy of extremely small numbers in the presence of large numbers; the tools also include a multi-dimensional tool that can autonomously classify data and identify small and large classes of data objects of varying characteristics in a noisy environment.
Conducted theoretical and experimental investigations covering conversional helicopters, tandem/coaxial rotor helicopters and compound helicopters.
Pioneered the development of models with comprehensive rotor system fault simulations to aid the development of systems capable of early detecting catastrophic helicopter rotor faults.