A computerized system for beam measurements and control at the KFUPM 350 kV ion accelerator

Abdel-Aal, R.E.; Alsunaidi, M. A.

doi:10.1016/0168-9002(91)90016-j

Cited by 2 publications

(1 citation statement)

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“…Cut-off frequency on the anti-aliasing filter was selected to prevent aliasing effects due to the higher frequency components of the vibration signal. Vibration waveform records, each being 4K-samples in length, were acquired by a VAX 3200 workstation running the XSYS data acquisition and analysis package [2,13]. Software in the VAX computed the magnitude of the fast Fourier transform (FFT) of the sampled record and generated data bases for training and evaluating the AIM models.…”

Section: Aim Abductive Machine Learningmentioning

confidence: 99%

Using Abductive Machine Learning for Online Vibration Monitoring of Turbo Molecular Pumps

Abdel-Aal

Raashid

1999

Shock and Vibration

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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.

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Section: Aim Abductive Machine Learningmentioning

confidence: 99%