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Using Abductive Machine Learning for Online Vibration Monitoring of Turbo Molecular Pumps
Shock and Vibration
1070-9622
Using abductive machine learning for online vibration monitoring of turbo molecular pumps
R.E. Abdel-Aal 0
M. Raashid 0
0 Center for Applied Physical Sciences, Research Institute, King Fahd University of Petroleum and Minerals , Dhahran 31261 , Saudi Arabia
-
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
1. Introduction
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. Table 1 gives a summary
of the specifications and operating conditions for a
typical 0.5 m3/s pump, model TPU 510, and its electronic
drive unit model TPC 300. Many of the pumps run
continuously for extended periods, and operational
experience has shown that bearing failures while the pump is
running at full speed can completely destroy the pump.
Such failures often occur without adequate warning
signs that can be detected through routine manual
inspection. Even in cases when there is a change in the
pump noise, this may go unnoticed in the noisy
environment of the accelerator vault or may occur after
normal working hours when the facility is left unattended.
A turbo pump is an expensive piece of equipment, and
pump failures can also be costly in terms of lost beam
time if there is a need to wait for in-house repair or for a
replacement pump to be ordered from abroad. We have
recently initiated work on the development of an online
monitoring scheme for the accelerator pumps with the
objective of automatically detecting abnormalities in
the pump condition and warning the accelerator
operator in advance to avert serious failures. The importance
of continuous online monitoring for critical machinery
is well established [
21
], since monthly or weekly
manual measurements may not be frequent enough or
consistent enough to detect developing problems.
Vibration analysis truth tables have been used for
many years as a guide for diagnosing vibrations in
rotating machinery, but conclusive results often require
further evidence [
24
]. Recent advances in computers,
instrumentation, and signal processing techniques have
made online predictive vibration monitoring of
machinery available and cost-effective approach in many
situations [
21
]. Techniques used include time domain
and frequency domain analysis as well as
combinations of both. Univariate time series analysis [
29
] and
multivariate linear regression methods [
19
] have been
employed to model normal vibration behavior in the
time domain. Problems with the first approach include
strong nonstationarity of the vibration time series, as in
the case of reciprocating machinery. The second
technique suffers from difficulties in determining suitable
relevant time series that explain variations in the
vibration data, as well as strong correlations between the
various input time series. The two techniques require
complex computations and considerable user
intervention for each analysis performed, which makes them
difficult to implement online using simple portable
apparatus. Frequency domain techniques use the
frequency spectrum of the vibration signal as a signature
for the pump condition, e.g. [
8
].
A recent trend in many areas of applied sciences has
been to resort to a machine learning approach when a
rigorous algorithmic solution becomes too complex or
when the underlying relationships between inputs and
outputs are not known. With this approach, a (...truncated)