A method for monitoring an industrial plant. In a first part of the industrial plant, first parameters are provided. The industrial plant is used to produce and/or process a fibrous material web. Second parameters are provided in a second part. The parameters are stored, preferentially as time series. In the case of a web break in the second part, the second parameters are first analyzed for a second anomaly. If no second anomaly can be detected, the first parameters are analyzed for a first anomaly. During the analysis, the parameters which were stored in a time range before the web break are preferably examined. If a first or second anomaly is detected, these, and optionally measures to avoid such web breaks, are displayed to the user. Optionally, the first parameters and/or the second parameters can be set so as to avoid future web breaks.

A method for monitoring an industrial plant. In a first part of the industrial plant, first parameters are provided. The industrial plant is used to produce and/or process a fibrous material web. Second parameters are provided in a second part. The parameters are stored, preferentially as time series. In the case of a web break in the second part, the second parameters are first analyzed for a second anomaly. If no second anomaly can be detected, the first parameters are analyzed for a first anomaly. During the analysis, the parameters which were stored in a time range before the web break are preferably examined. If a first or second anomaly is detected, these, and optionally measures to avoid such web breaks, are displayed to the user. Optionally, the first parameters and/or the second parameters can be set so as to avoid future web breaks.

A computer program product, an industrial installation in particular a paper-making machine, an apparatus and method for predicting a position of a web break of a fibrous material web that has occurred or is imminent, wherein the method includes capturing parameters, in particular speeds of rollers for transporting the fibrous material web or a web tension thereof, where the parameters are advantageously stored in the form of time series, a self-learning algorithm is used to detect the imminent web break and to determine the position of the web break which is imminent and/or has occurred, where the basis for the detection or the determination is a deviation of the respective parameter, such as from a temporal mean of the respective parameter.

A computer program product, an industrial installation in particular a paper-making machine, an apparatus and method for predicting a position of a web break of a fibrous material web that has occurred or is imminent, wherein the method includes capturing parameters, in particular speeds of rollers for transporting the fibrous material web or a web tension thereof, where the parameters are advantageously stored in the form of time series, a self-learning algorithm is used to detect the imminent web break and to determine the position of the web break which is imminent and/or has occurred, where the basis for the detection or the determination is a deviation of the respective parameter, such as from a temporal mean of the respective parameter.

A machine for dewatering and drying a paper, cardboard or other fibrous web, to which machine a fibrous stock suspension is fed which is formed at least partially from waste paper. The machine includes at least two press nips which are formed in each case by two press rolls, wherein a fibrous web runs through first press nip with a water absorbing dewatering belt on both sides. The fibrous web then runs through a second press nip with at least one other dewatering belt and a smooth impermeable transfer belt on both sides. A transfer belt transfers the fibrous web after second press nip to a belt in a downstream machine unit, e.g. dryer group. The dryer group allows the fibrous web to be guided in a serpentine manner alternatively over heated dryer cylinders and vacuum equipped guide rolls.

A machine for dewatering and drying a paper, cardboard or other fibrous web, to which machine a fibrous stock suspension is fed which is formed at least partially from waste paper. The machine includes at least two press nips which are formed in each case by two press rolls, wherein a fibrous web runs through first press nip with a water absorbing dewatering belt on both sides. The fibrous web then runs through a second press nip with at least one other dewatering belt and a smooth impermeable transfer belt on both sides. A transfer belt transfers the fibrous web after second press nip to a belt in a downstream machine unit, e.g. dryer group. The dryer group allows the fibrous web to be guided in a serpentine manner alternatively over heated dryer cylinders and vacuum equipped guide rolls.

The invention relates to a method, a computer program product and a machine vision system (30), comprising at least one lighting device (34), at least one image sensor (31 a-c) and a data processing device (32), the system in a first mode illuminating a first object (35) using a first type of illumination and capturing images of the first object at a first image capturing frequency, when the first object (35) is on a second object (33), transmitting the captured image data to the data processing device for analysis, and changing the system for monitoring the second object in a second mode, if absence of the first object on the second object is detected from the image data, wherein said at least one image sensor (31 a-c) is reconfigured to capture images at a second image capturing frequency from the second object.

A system and method for labelling normal and abnormal regions in data related to a paper machine for web break prediction and labelling individual parameters for root cause analysis, using machine learning models, includes using the machine learning models in real-time to predict breaks in the paper web, analyzing root cause for the breaks in the paper web, and estimating a time to break. An auto-data-labeling framework helps in adaptive learning for autonomous model improvement of the deployed model, transfer learning, shortlisting parameters and automating feasibility study.

A system and method for labelling normal and abnormal regions in data related to a paper machine for web break prediction and labelling individual parameters for root cause analysis, using machine learning models, includes using the machine learning models in real-time to predict breaks in the paper web, analyzing root cause for the breaks in the paper web, and estimating a time to break. An auto-data-labeling framework helps in adaptive learning for autonomous model improvement of the deployed model, transfer learning, shortlisting parameters and automating feasibility study.

Aspects of the present invention provide methods, systems, and/or the like for: (1) receiving current thermal imaging data for a portion of a paper web in the paper manufacturing process; (2) determining force data for the portion of the paper web; (3) processing, to produce a first data analysis result, current thermal imaging data, the force data, and paper profile data using a machine-learning model trained with respective historical thermal imaging data, respective historical force data, and respective paper profile data for each respective prior paper breakage event in a set of prior paper breakage events; (4) generating, based on the first data analysis result, a prediction as to an occurrence of the paper break on the portion of the paper web; (5) identifying a preventative action based on the prediction; and (6) facilitating performance of the preventative action.