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.
Highlights
The 350 kV light ion accelerator facility [4] at King Fahd University of Petroleum and Minerals (KFUPM) employs some 15 Balzers turbo molecular vacuum pumps of various capacities to achieve a minimum vacuum level of 1.33 × 10−4 Pa
We considered the development of abductive network models that continuously perform go-no go checks on the pump condition by classifying the frequency spectrum as representing a good or a bad pump
GMDH-based abductive machine learning has been used for diagnosing a turbo molecular pump as good or bad as judged by low frequency vibration spectra collected in both the radial and axial directions. 100% diagnosis accuracy for a 500-case evaluation population is maintained for training data bases as small as 10 cases
Summary
The 350 kV light ion accelerator facility [4] at King Fahd University of Petroleum and Minerals (KFUPM) employs some 15 Balzers turbo molecular vacuum pumps of various capacities to achieve a minimum vacuum level of 1.33 × 10−4 Pa. These include statistical pattern recognition methods such as Bayesian classifiers and discriminant functions [11], artificial neural networks modeled roughly on how the human brain is believed to function [26], as well as methods for the induction of decision trees [9] These techniques vary in their accuracy, complexity, computational requirements during training, and their ability to provide human-like explanations for their conclusions. Such variations have led to newer techniques combining good features from various methods An example of such ‘hybrids’ is the AIM abductive network tool [20] which draws on statistical and multiple regression analysis methods as well as neural networks, resulting in a faster and more automated approach to model synthesis. Results are related to data in the literature on the performance of similar neural network approaches
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