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Article Dans Une Revue International Journal of Data Science and Analytics Année : 2023

Functional anomaly detection: a benchmark study

Résumé

The increasing automation in many areas of the Industry expressly demands to design efficient machine learning solutions for the detection of abnormal events. With the ubiquitous deployment of sensors monitoring nearly continuously the health of complex infrastructures, anomaly detection can now rely on measurements sampled at a very high frequency, providing a very rich representation of the phenomenon under surveillance. In order to exploit fully the information thus collected, the observations cannot be treated as multivariate data anymore and a functional analysis approach is required. It is the purpose of this paper to investigate the performance of recent techniques for anomaly detection in the functional setup on real datasets. After an overview of the state of the art and a visual-descriptive study, a variety of anomaly detection methods are compared. While taxonomies of abnormalities (e.g., shape, location) in the functional setup are documented in the literature, assigning a specific type to the identified anomalies appears to be a challenging task. Thus, strengths and weaknesses of the existing approaches are benchmarked in view of these highlighted types in a simulation study. Anomaly detection methods are next evaluated on two datasets, related to the monitoring of helicopters in flight and to the spectrometry of construction materials namely. The benchmark analysis is concluded by a recommendation guidance for practitioners

Dates et versions

hal-04268013 , version 1 (02-11-2023)

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Guillaume Staerman, Eric Adjakossa, Pavlo Mozharovskyi, Vera Hofer, Jayant Sen Gupta, et al.. Functional anomaly detection: a benchmark study. International Journal of Data Science and Analytics, 2023, 16 (1), pp.101-117. ⟨10.1007/s41060-022-00366-5⟩. ⟨hal-04268013⟩
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