Monitoring device comprising at least one processor for segmenting different time series of sensor data points representing at least one measured parameter of a technical system to detect different types of physical processes of the technical system, comprising an analysing unit comprising the at least one processor configured to iteratively for each sensor data point of a time series of sensor data points and iteratively for each time series of all different sensors, - obtain an actual sensor data point, - determine whether the actual sensor data point is an outlier and mark it accordingly, - 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 by learning a new regression model based on the actual data point and a predefined second number of preceding sensor data points, and - create a segment comprising all sensor data points of one learned regression model, and a display unit comprising the at least one processor configured to - display each sensor data point of the time series of sensor data points indicating the determined segment or being an outlier, wherein the segment of subsequent data points belonging to one learned regression model indicates a gradually developing physical process and the segment is assigned to the physical process of the technical system. an analysing unit comprising the at least one processor configured to iteratively for each sensor data point of a time series of sensor data points and iteratively for each time series of all different sensors, - obtain an actual sensor data point, - determine whether the actual sensor data point is an outlier and mark it accordingly, - 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 by learning a new regression model based on the actual data point and a predefined second number of preceding sensor data points, and - create a segment comprising all sensor data points of one learned regression model, and - obtain an actual sensor data point, - determine whether the actual sensor data point is an outlier and mark it accordingly, - 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 by learning a new regression model based on the actual data point and a predefined second number of preceding sensor data points, and - create a segment comprising all sensor data points of one learned regression model, and a display unit comprising the at least one processor configured to - display each sensor data point of the time series of sensor data points indicating the determined segment or being an outlier, - display each sensor data point of the time series of sensor data points indicating the determined segment or being an outlier, wherein the segment of subsequent data points belonging to one learned regression model indicates a gradually developing physical process and the segment is assigned to the physical process of the technical system.