Contact Bernadin Namoano
Areas of expertise
- Through-life Engineering Services
Background
Bernadin has received his MSc degree in computer science in 2013 from Polytechnique University (France). Before joining Cranfield University in 2017, he worked as software engineer in Paris, focusing on architectural design for big data, big time series data analysis, and software lifecycle implementation and maintenance.
He completed his PhD funded by EPSRC and Unipart-rail/Instrumentel, at Cranfield University in the field of condition monitoring applied to railway assets, and was awarded the EPSRC doctoral fellowship Prize.
Current activities
Bernadin's main research areas have been the development of novel architectures and algorithms to tackle big time series data challenges for predictive maintenance purposes. He is currently working on his Research fellow Prize at Digital Engineering and Manufacturing center in Cranfield University. His work involves the development, implementation, and testing of novel methods for big time series analysis, text mining, image processing and simulation for predictive maintenance.
Clients
- EPSRC
- Babcock
- Rolls-Royce Plc
- BAE Systems
- Instrumentel
- Unipart Rail
- Network Rail
Publications
Articles In Journals
- Namoano B, Ruiz-Carcel C, Emmanouilidis C & Starr AG (2022) Data-driven wheel slip diagnostics for improved railway operations, IFAC-PapersOnLine, 55 (19) 103-108.
- Shimizu M, Perinpanayagam S & Namoano B (2022) A real-time fault detection framework based on unsupervised deep learning for prognostics and health management of railway assets, IEEE Access, 10 (October) 96442-96458.
- Namoano B, Emmanouilidis C, Ruiz Carcel C & Starr A (2020) Change detection in streaming data analytics: a comparison of Bayesian online and martingale approaches, IFAC-PapersOnLine, 53 (3) 336-341.
Conference Papers
- Alotaibi A, Durazo Cardenas I, Namoano B & Starr A (2022) rack geometry deterioration modelling for asset management: A visual analytics approach. In: 11th International Conference on Through-life Engineering Services - TESConf2022, Cranfield, 8-9 November 2022.
- Shimizu M, Perinpanayagam S & Namoano B (2022) A fault detection technique based on deep transfer learning from experimental linear actuator to real-world railway door systems. In: Annual Conference of the Prognostics and Health Management Society, Nashville, 31 October - 4 November 2022.
- Shimizu M, Perinpanayagam S & Namoano B (2022) Real-time techniques for fault detection on railway door systems. In: 2022 IEEE Aerospace Conference, Big Sky, MT, 5-12 March 2022.
- Namoano B, Starr A, Emmanouilidis C & Ruiz Carcel C (2019) Online change detection techniques in time series: an overview. In: 2019 IEEE International Conference on Prognostics and Health Management (ICPHM), San Francisco, 17-20 June 2019.
