Functional Anomaly Detection: a Benchmark Study

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 recommendation guidance for practitioners.

Time Series to Images: Monitoring the Condition of Industrial Assets with Deep Learning Image Processing Algorithms

The ability to detect anomalies in time series is considered as highly valuable within plenty of application domains. The sequential nature of time series objects is responsible for an additional feature complexity, ultimately requiring specialized approaches for solving the task. Essential characteristics of time series, laying outside the time domain, are often difficult to capture with state-of-the-art anomaly detection methods, when no transformations on the time series have been applied. Inspired by the success of deep learning methods in computer vision, several studies have proposed to transform time-series into image-like representations, leading to very promising results. However, most of the previous studies implementing time-series to image encodings have focused on the supervised classification. The application to unsupervised anomaly detection tasks has been limited. The paper has the following contributions: First, we evaluate the application of six time-series to image encodings to DL algorithms: Gramian Angular Field, Markov Transition Field, Recurrence Plot, Grey Scale Encoding, Spectrogram and Scalogram. Second, we propose modifications of the original encoding definitions, to make them more robust to the variability in large datasets. And third, we provide a comprehensive comparison between using the raw time series directly and the different encodings, with and without the proposed improvements. The comparison is performed on a dataset collected and released by Airbus, containing highly complex vibration measurements from real helicopters flight tests. The different encodings provide competitive results for anomaly detection.

Anomaly detection with Wasserstein GAN

Generative adversarial networks are a class of generative algorithms that have been widely used to produce state-of-the-art samples. In this paper, we investigate GAN to perform anomaly detection on time series dataset. In order to achieve this goal, a bibliography is made focusing on theoretical properties of GAN and GAN used for anomaly detection. A Wasserstein GAN has been chosen to learn the representation of normal data distribution and a stacked encoder with the generator performs the anomaly detection. W-GAN with encoder seems to produce state of the art anomaly detection scores on MNIST dataset and we investigate its usage on multi-variate time series.