The SmartANOMALY project is an evolution and broadening of the SmartLINAC project, which launched in June 2019. The main goal of the original project was to create a platform for anomaly detection and maintenance planning for linear accelerators, which are used widely in medicine and high-energy physics research.
Technologies related to artificial intelligence (AI) are opening up new possibilities for anomaly detection. Given the array of large particle accelerators at CERN, the Organization has significant expertise in detecting anomalies in highly complex systems. This expertise has the potential to be applied to a range of scientific and industrial activities — including (but not limited to) other fields where particle accelerators are used, such as medicine. This project has been supported by CERN's Knowledge Transfer group.
After more than a year of development, promising results were achieved, demonstrating the potential of our innovative algorithms for detecting anomalies — as well as perhaps even predicting their effects to some extent. Today, the project’s primary focus is on medical accelerators. However, we see potential in training our solution on more sources, such as on compressor engines or complex industrial processes.
It is common practice to use alternative data sets when training anomaly-detection systems. Therefore, the distinguishing aspect of our research is that several approaches, based on statistics and neural-network technologies, are being combined in order to offer a system that can be adapted to different sources.
Given that demand for such tools is growing rapidly, we believe the time is right to formally enlarge the scope of the research started through SmartLINAC. Thus, we have created our new, broader SmartANOMALY project.
This new project incorporates our existing investigations with linear accelerators, and will also allow new actors to take part in the development of the anomaly detection tool for complex systems. We are currently discussing possible applications in the automotive and food-processing industries. Given that our research is now entering a new phase, we encourage actors from industry and academia to get in touch with us. We are keen both to develop the existing activities within this project and to explore new opportunities for enlarging its scope.
- Y. Donon, Smart Anomaly Detection and Maintenance Planning Platform for Linear Accelerators (3 October). Presented at the 27th International Symposium Nuclear Electronics and Computing (NEC’2019), Montenegro, 2019. cern.ch/go/nb9z