Instrumentation & Measurement Magazine 24-9 - 49

Fig. 2. Representative 100 Hz BLM signals with a clearly visible alignment spike. (a) The first one in the series of spikes and (b) spurious spikes, following inward
collimator movements at about t = 0.5 s (from [43]).
positioned immediately downstream. A collimator is aligned
when both jaws are centered around the beam orbit, and this
occurs once a particular pattern of spike has been detected in
the BLM signal for each jaw. Although the BBA is semi-automated
[35], the manual detection and classification of the
alignment spikes required is a tedious and time-consuming
procedure that has to be carried out by system experts.
During the initial beam commissioning period taking place
each year, the collimators have to be aligned so to ensure their
correct setup: at injection energy, 79 collimators are aligned,
while 75 collimators are aligned at top energy.
The campaigns of collimator alignment involve continuously
moving the jaws towards the beam, while ignoring
non-alignment spikes, until a clear alignment spike is observed.
The alignment spike shown in Fig. 2 indicates that the
beam halo has been touched by the moving jaw. It is made of
a steady-state signal, followed by a loss spike, with temporal
decay of losses, and then a steady-state signal following
the spike. The second steady state, featuring losses larger than
the first one, is due to the continuous scraping of halo particles
when the jaws are in fixed positions, which is induced by a
transport mechanism towards high amplitude like a diffusive
process. Any other spikes not following this pattern are classified
as non-alignment spikes. Achieving a reliable alignment
implies the capability of correctly identifying the spikes in the
December 2021
BLM signal, and in a typical alignment campaign, hundreds of
such spikes require analyses.
The spike recognition process can be seen as a classification
problem, and training of ML models was performed to discriminate
the two spike patterns in the BLM losses [37]. Data
were collected during 11 semi-automatic collimator alignment
campaigns carried out in 2016 and 2018 (injection and top energy).
In total, 6446 samples were catalogued, out of which
4379 represented alignment spikes whereas 2067 spurious
spikes. The information acquired in alignment campaigns consisted
of 100 Hz BLM data and collimator jaw positions stored
at a frequency of 1 Hz.
Fourteen manually engineered features describing the
shape of the spikes observed in the BLM signal (see [37]
for a detailed description of the features considered) were
extracted from this data set, and the most relevant were
selected by means of the strength of association between
each pair of variables, determined using the Spearman
correlation. Five different ML models analyzed the featureselection
stage, to check how they ranked the importance
of each feature. These models were individually trained,
exploiting all features, and the features were ranked in
ascending order, depending on their relevance. A Sequential
Forward Selection algorithm (SFS) [38] was applied
to distill the most appropriate features using the best
IEEE Instrumentation & Measurement Magazine
49

Instrumentation & Measurement Magazine 24-9

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