We propose ARN, a semisupervised anomaly detection and generation method based on adversarial reconstruction. ARN exploits a regularized autoencoder to optimize the reconstruction of variants of normal examples with minimal differences, that are …
According to the smart manufacturing paradigm, the analysis of assets’ time series with a machine learning approach can effectively prevent unplanned production downtimes by detecting assets’ anomalous operational conditions. To support smart …
Fault prediction is an important topic for the industry as, by providing effective methods for predictive maintenance, allows companies to perform important time and cost savings. In this paper we describe an application developed to predict and …
The outlying property detection problem (OPDP) is the problem of discovering the properties distinguishing a given object, known in advance to be an outlier in a database, from the other database objects. This problem has been recently analyzed …