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System condition monitoring through Bayesian change point detection using pump vibrations (2020)
Presentation / Conference Contribution
Tochev, E., Rengasamy, D., Pfifer, H., & Ratchev, S. System condition monitoring through Bayesian change point detection using pump vibrations. Presented at IEEE 16th International Conference on Automation Science and Engineering (CASE 2020), Hong Kong, China

This paper presents a method for vibration analysis and a simple test bench analogue for the solder pumping system in an industrial wave-soldering machine at a Siemens factory. A common machine fault is caused by solder build-up within the pipes of t... Read More about System condition monitoring through Bayesian change point detection using pump vibrations.

A Function-Behaviour-Structure design methodology for adaptive production systems (2019)
Journal Article
Sanderson, D., Chaplin, J. C., & Ratchev, S. (2019). A Function-Behaviour-Structure design methodology for adaptive production systems. International Journal of Advanced Manufacturing Technology, 1-12. https://doi.org/10.1007/s00170-019-03823-x

Adaptive production systems are a key trend in modern advanced manufacturing. This stems from the requirement for the system to respond to disruption , either in the form of product changes or changes to other operational parameters. The design and r... Read More about A Function-Behaviour-Structure design methodology for adaptive production systems.

In-process tool wear prediction system based on machine learning techniques and force analysis (2018)
Journal Article
Gouarir, A., Martínez-Arellano, G., Terrazas, G., Benardos, P., & Ratchev, S. (2018). In-process tool wear prediction system based on machine learning techniques and force analysis. Procedia CIRP, 77, 501-504. https://doi.org/10.1016/j.procir.2018.08.253

This paper presents an in-process tool wear prediction system, which uses a force sensor to monitor the progression of the tool flank wear and machine learning (ML), more specifically, a Convolutional Neural Network (CNN) as a method to predict tool... Read More about In-process tool wear prediction system based on machine learning techniques and force analysis.