Abstract: Device and method for analyzing time series data monitored on a machine, wherein the device segments the time series data into multiple time segments, determines a cluster of time segments estimated to have the same dynamics of the time series data, then classifies the cluster based on label information associated with at least one of the time segments, presents at least a part of the time series data of the cluster to a user if none of the time segments of the cluster has associated label information, classifies the cluster and generates label information associated with the time segments of the cluster based on a user input received in response to presentation of the time series data, where the generated label information indicates a result of classifying the cluster.

Abstract: Assistance device for automatically generating training data of a time series of sensor data, further on called temporal sensor data, applied to train an Artificial Intelligence system used for detecting anomalous behavior of a technical system, including a processor configured to perform - obtaining historical temporal sensor data, dividing the historical temporal sensor data into a temporal sequence of segments and assigning one segment type out of several different segment types to each segment, iteratively for each segment, determining a neighborhood pattern of segment types, determining the most frequently occurring neighborhood pattern from all determined neighborhood patterns as reference pattern for normal operation of the technical system, -selecting a subsequence of segments out of the historical temporal sensor data, which is ordered according to the reference pattern, and - outputting the subsequence of segments for applying as training data.

Abstract: A method for detecting an abnormal behavior of a device, includes capturing data of at least two different sensors associated to the device within a temporal sequence of time intervals, estimating a relationship between two different sensors for each combination of two different sensors and for each of the time intervals by determining a precision matrix of a multivariate probabilistic model, each matrix element representing the relationship between two sensors, determining a temporal course of the precision matrix by applying the precision matrix of neighboring time intervals with at least one penalty, and identifying an abnormal behavior of the device, if the precision matrix of adjacent time intervals differs by a value larger than an expected threshold value.

Abstract: A device obtains a set of time series data monitored on a machine and further obtains first label information indicating a first time window in the time series data. The device determines a first probabilistic model, describing dynamics of the time series data inside the first time window, and a second probabilistic model describing dynamics of the time series data adjacent to the first time window. Based on the first and second probabilistic models, the device determines a first part of the time series data that is estimated to match the first probabilistic model and a second part of the time series data that is estimated to match the second probabilistic model, e.g., using a hidden Markov model. The device then determines second label information indicating a second time window which includes the first part of the time series data and excludes the second part of the time series data.

Abstract: Device and method for analyzing time series data monitored on a machine, wherein the device segments the time series data into multiple time segments, determines a cluster of time segments estimated to have the same dynamics of the time series data, then classifies the cluster based on label information associated with at least one of the time segments, presents at least a part of the time series data of the cluster to a user if none of the time segments of the cluster has associated label information, classifies the cluster and generates label information associated with the time segments of the cluster based on a user input received in response to presentation of the time series data, where the generated label information indicates a result of classifying the cluster.