Using abductive machine learning for online vibration monitoring of turbo molecular pumps

Article type: Research Article

Authors: Abdel‐Aal, R.E.^; | Raashid, M.

Affiliations: Center for Applied Physical Sciences, Research Institute, King Fahd University of Petroleum and Minerals, Dhahran 31261, Saudi Arabia

Note: [] Corresponding author: R.E. Abdel‐Aal, P.O. Box 1759, KFUPM, Dhahran 31261, Saudi Arabia. Fax: + 966 3 860 4281; E‐mail: radwan@kfupm.edu.sa.

Abstract: Turbo molecular vacuum pumps constitute a critical component in many accelerator installations, where failures can be costly in terms of both money and lost beam time. Catastrophic failures can be averted if prior warning is given through a continuous online monitoring scheme. This paper describes the use of modern machine learning techniques for online monitoring of the pump condition through the measurement and analysis of pump vibrations. Abductive machine learning is used for modeling the pump status as ‘good’ or ‘bad’ using both radial and axial vibration signals measured close to the pump bearing. Compared to other statistical methods and neural network techniques, this approach offers faster and highly automated model synthesis, requiring little or no user intervention. Normalized 50‐channel spectra derived from the low frequency region (0–10 kHz) of the pump vibration spectra provided data inputs for model development. Models derived by training on only 10 observations predict the correct value of the logical pump status output with 100% accuracy for an evaluation population as large as 500 cases. Radial vibration signals lead to simpler models and smaller errors in the computed value of the status output. Performance is comparable with literature data on a similar diagnosis scheme for compressor valves using neural networks.

Keywords: Vibration monitoring and diagnostics, statistical vibration analysis, turbo molecular pumps, machine learning, abductive networks

Journal: Shock and Vibration, vol. 6, no. 5-6, pp. 253-265, 1999