Contact Minoru Shimizu
Background
Minoru Shimizu received his Bachelor in Physics and Master in Chemistry and Material Science from Tokyo Institute of Technology. Before he joined Cranfield, Minoru has worked for several years as a research engineer in a construction and mining machinery manufacturer, Japan. He has experience in developing IoT visualization systems to improve customer productivity. His research interests include fault detection, diagnosis, and prognosis by Machine Learning and Deep Learning for railway asset systems.
Current activities
Minoru is currently pursuing his MSc by research degree in Transport Systems at the Integrated Vehicle Health Management(IVHM) Centre, Cranfield University.
Publications
Articles In Journals
- Shimizu M, Perinpanayagam S, Namoano B & Starr A (2023) Real-time prognostics and health management without run-to-failure data on railway assets, IEEE Access, 11 28724-28734. Dataset/s: 10.17862/cranfield.rd.7327148
- 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 96442-96458.
Conference Papers
- 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.
