Abstract: Assistance apparatus for automatically identifying failure types of a technical system is provided including at least one processor configured to determine for each sensor data a set of specific temporal courses of first time series of the sensor data of the sensor and assign a symbolic representation to each of the different specific temporal courses, provide at least one failure pattern, obtain more than one monitored time series of sensor data of the technical system, each of them divided into a sequence of time segments, and automatically assign to each time segment a symbolic representations according to the temporal course of the sensor data in the time segment, calculate a similarity measure for the set of symbolic representations of a selected time interval, determine a ranking of the failure pattern depending on decreasing values of the calculated similarity measure, and output the ranking.

Abstract: A monitoring device including an analysing unit configured to obtain an actual sensor data point, determine whether the actual sensor data point is an outlier, determine whether the actual data point represents a discontinuity, determine a slope by a regression model of a slope equation of a straight line in time fitted to at least a predefined first number of subsequently obtained sensor data points, and determine whether the actual sensor data point belongs to the learned regression model, if the actual sensor data point does not belong to the learned regression model, determine a new slope based on the actual data point and a predefined second number of preceding sensor data points, and create a segment including all sensor data points, and display each sensor data point indicating the determined segment or being an outlier.

Abstract: A method for calibrating an electronic assembly during a manufacturing process is provided, including the steps: determining a calibration value for the assembly which for a predefined input value gives a deviation between an actual output value output by the assembly and a predefined desired output value, transmitting the calibration value to the assembly, and storing the calibration value in the assembly, wherein the calibration value of the assembly is determined by a machine learning method executed in a calibration device, and the machine learning method is trained by training data, which include historical calibration values of a plurality of assemblies of the same type and parameters of assemblies of the same type, which are dependent on the manufacturing process and/or express physical properties.

Abstract: An evaluation framework for a generated dataset of a data generation algorithm such as a generative adversarial network is provided. The generated dataset includes a plurality of iterations of multiple instances of generated time series of data points. The evaluation framework provides multiple views. A first view includes at least one distance measure. The at least one distance measure is between the multiple instances of the generated time series and multiple instances of a reference time series, as a function of the plurality of iterations.