The need for reliable operations of linear accelerators is critical for the spread of this technique in medical environment. At CERN, where LINACs are used for particle research, similar issues are encountered, such as the appearance of jitters in plasma sources (2MHz RF generators), that can have significant impact on the subsequent beam quality in the accelerator. The “SmartLINAC” project was established as an effort to increase LINACs' reliability by means of early anomaly detection and prediction in its operations, down to the component level. The research described in this article reviews the different techniques used to detect anomalies, from their earlier signals, using data from 2MHz RF generators. This research is an important step forward in the SmartLINAC project but represents only its beginning. The authors used four different techniques in an effort to determine the most appropriate one to detect anomalies on the generators' data. The main challenge came from the nature of the data having a noised signal and presenting several kinds of anomalies from different sources, and from the lack of available exhaustive and precise labelling. This research allowed us to understand better the nature of the data we are working with and start addressing the project's objectives, not only identifying and differentiating possible anomalies, but also forecasting to potential breakdowns.
1
In this project, we investigate jitters on LINAC4’s 2MHz RF plasma generators’ forward
power’s history. LINAC4 is a linear accelerator designed to become CERN’s Large Hadron Collider’s
(LHC) source of proton beams after its 2019-2020 shutdown. It is designed to accelerate negative
hydrogen ions to 160 MeV for the LHC’s injection chain [
Several sets of data, presenting different kinds and amounts of anomaly periods have been used to in the framework of the experiments described in this paper. The series presented about 9 million entries from different RF sources. About 30 jittering periods caused by human manipulations and 3 periods of jittering as investigated. Those data has been separated for training and test purposes. In this chapter, the nature of the data will be described using the training set as a reference. Captions are made every 1.2 seconds, they contain a date and a power in Watts mainly included between 30’000 W and 50’000 W, depending of the current configuration of the source. Some, rare and isolated data range between 30’000 W and 0 W for unexplained reasons, sometimes, the source was captured as powered down, registering 0 W.
Relatively frequently, the source presents some especially violent jittering; those are power scans, resulting from human manipulations and are referred as such in the present document. Power scans are intentional change of power in order to observe effects on the source. The prime concern treated in this article are anomalies appearing overtime and presenting constant and long jitter periods. 3 3.1
In this approach, referred hereafter as “first approach” we chose to first divide the data into two categories “significant” and “noise”. The data were then smoothed to normalize them over a period of time. From then, it was possible to highlight the shift from average over time during and before long jitter periods. Furthermore, it was possible to understand the structures of a jitter based on the provided samples. Smoothing was used to understand tendencies in data. This step was necessary to differentiate punctual peaks from increasing tendencies.. Graphically, outside periods of anomalies, the tendency varies in different shades of dark red and black as represented on Figure 1.On the opposite, a higher deviation value appears in shades of bright red as represented on Figure 2.
This approach allowed us to detect jitters periods but not to differentiate efficiently their nature (systemic or human manipulation). However, this approach showed itself informative in another way. Indeed, jitters do not appear suddenly but progressively, with symptoms as early as days before. Short periods of higher power delta are frequent at any time, but their density increase systematically before periods of jitters. First symptom have been identified by the technique in 1) and jitters where first observed it 2). In this example, the difference between the early symptoms and the jitters is of more than two days. A low delta is represented by darker shades of red, the higher the delta, the brighter the color. The empirical parameters for the estimation of noised areas is that deltas higher than average by 100% on the last 100 samples, remaining so for at least one hour are anomalies. This approach proved itself efficient to detect and predict jitters periods. 3.2
This approach is characterized by the fact that no information on LINAC4 internal
maintenance processes was used (unsupervised), for example, on the possible causes of jitter: human
manipulations or environmental factors. Thus, only contains RF power sources output and four
problematic time intervals that were manually marked by domain expert were used. The method is
based on the search for features that distinguish the marked problem intervals of jitter from the rest of
the data. The feature is a subsequence of data for which the distance to the selected subsequence does
not exceed the threshold determines the “proximity zone”. The Euclidean metric is chosen as a
measure of the distance between the subsequences. Algorithm 1 describes in detail the steps of the
method. Firstly, a subsequence of a given length k is randomly selected. Then, for different values of
the threshold t, the positions of the subsequences are determined, which are close to the selected
subsequence. The resulting set of X is clustered by the kernel density estimation (KDE) method [6, 7].
The Adjusted Rand Index (ARI) [
This method consists in processing the sequence by sliding window and calculating the
statistical features for the fragments of the sequence located in this window [
In this approach, statistical calculations are based on time series’ statistical metrics. Kalman
filter is usually performant at describing the random structure of experimental measurements [
All approaches showed themselves able to detect anomalies when they were occurring, each bringing their own information. 3.1 Filtering, smoothing and distance from average highlighted the first signals of an anomaly and showed its growths structure. 3.2 Label-related clustering, showed the possibility to solve the problem using machine learning approach, with an excellent scalability, which is essential for the adaptability of the solution to our project. 3.3 Sequence analysis using statistical features highlighted the possible clustering in the data, giving us an opportunity to differentiate in depth states and stages of anomalies. Finally, 3.4 clarified for us the nature of the noise present in the data and allowed to differentiate jittering by their origins. Figure shows the jittering period labelling.
As shown on the figure 3, all techniques developed detects first symptoms of jittering before it was signaled on the original dataset (labelling represented on the last line of the figure). As developed in chapter 3.1, the first method presented detects anomalies way ahead of their apparition. The two methods using noise filtering techniques (3.1 and 3.4) are less prone to punctual false positive labelling and more importantly the results doesn’t seems to be altered by the absence of this information, which seems to signify the data removed were indeed non-informative as assumed. Those characteristics will allow us to develop an adaptable and scalable technique to detect, identify and to some extents forecast anomalies using machine learning, in order to maximize the adaptability of our method. The statistical method that will be used is however still to be defined. 5
The initial approach of this study, to use different approaches and focus on their respective
input showed itself rewarding as in allowed us to discover and understand previously supposed
features in data we initially had very little information about. The core objective of the SmartLINAC
project is to realize predictive maintenance. In other words to predict anomalies. If it has been done
successfully in this project, this application is yet far from what is needed in the project. What we
observed in the framework of this study is the informativeness of one specific data source. This study
should now be adapted for its use in production in LINAC4’s facilities, thus also allowing the testing
of its abilities. If the breakdown forecasts obtained in this study might be interesting at CERN’s
facilities, where maintenance for the instruments we are working with is available day and night with a
Mean Time To Repair rounding under an hour [
This work was made in collaboration with CERN openlab. It was partially financially supported by the Russian Foundation for Basic Research under grant # 19-29-01135, # 18-37-00418, # 17-01-00972 and by the Ministry of Science and Higher Education within the State assignment to the FSRC “Crystallography and Photonics” RAS No. 007-GZ/Ch3363/26 (theoretical results). [2] CERN, "Linear accelerator 4," CERN, [Online]. https://home.cern/science/accelerators/linear-accelerator-4. [Accessed 17 August 2019]. Available: [3] V. Greco, "A partnership-mentorship approach," in ICEC workshop, CERN, Geneva, 2017. [4] G. P. M. S. V. K. H. Xiong, " Enhancing data analysis with noise removal," IEEE Transactions on Knowledge and Data Engineering, vol. 18, no. 3, pp. 304 - 319, 2006. [5] R. P. Y. G. A. K. Yann Donon, "Key point detection on images: A new polyvalent method," in ITNT proceedings, Samara, 2019. [6] M. Rosenblatt, "Remarks on Some Nonparametric Estimates of a Density Function," The Annals of Mathematical Statistics, vol. 27, no. 3, p. 832–837, 1956. [7] E. Parzen, "On Estimation of a Probability Density Function and Mode," The Annals of Mathematical Statistics, vol. 33, no. 3, p. 1065–1076, 1962.