Abstract: The invention relates to a container treatment machine for treating containers, in particular in the beverage-processing industry, medical technology, or the cosmetics industry, the container treatment machine comprising a control unit for controlling the function of the container treatment machine and at least one treatment unit for treating the containers; the container treatment machine is designed to treat the containers in exactly one way; the container treatment machine comprises at least one component which can output data relating to its operating state and/or the operating state of the container treatment machine to the control unit; and the control unit comprises a neural network which is configured and trained to use the data to determine whether a deviation of the operating state of the container treatment machine from a normal state is present and/or imminent.

Abstract: A method for optically inspecting containers in a drinks processing system, wherein the containers are transported as a container mass flow using a transporter and captured as camera images by an inspection unit arranged in the drinks processing system, and wherein the camera images are inspected for faults by a first evaluation unit using a conventional image processing method, wherein the camera images with faulty containers are classified as fault images and the faults are correspondingly assigned to the fault images as fault markings, wherein the camera images with containers considered to be good quality are classified as fault-free images, the fault images, the fault markings and the fault-free images are compiled as a specific training data set, and wherein, using the specific training data set, a second evaluation unit is trained in situ with an image processing method working on the basis of artificial intelligence.

Abstract: A container treatment machine comprises a treatment unit, for the treatment of containers, and container receptacles, in which containers can be received such that they can rotate about an axis, the container treatment machine comprising a camera, for capturing an image of a container transported upstream of the treatment unit in a container receptacle, and an alignment module, the alignment module being designed to rotate a container into a target position by actuating the container receptacle. The alignment module comprises a neural network, which, by processing the image of a container transported upstream of the treatment unit in a container receptacle, can determine a necessary rotation of the container from the current position of same to the target position, and the alignment module can control the rotation of the container receptacle on the basis of the determined rotation.

Abstract: The invention relates to a container treatment machine for treating containers, in particular in the beverage-processing industry, medical technology, or the cosmetics industry, the container treatment machine comprising a control unit for controlling the function of the container treatment machine and at least one treatment unit for treating the containers; the container treatment machine is designed to treat the containers in exactly one way; the container treatment machine comprises at least one component which can output data relating to its operating state and/or the operating state of the container treatment machine to the control unit; and the control unit comprises a neural network which is configured and trained to use the data to determine whether a deviation of the operating state of the container treatment machine from a normal state is present and/or imminent.

Abstract: A container treatment machine comprises a treatment unit, for the treatment of containers, and container receptacles, in which containers can be received such that they can rotate about an axis, the container treatment machine comprising a camera, for capturing an image of a container transported upstream of the treatment unit in a container receptacle, and an alignment module, the alignment module being designed to rotate a container into a target position by actuating the container receptacle. The alignment module comprises a neural network, which, by processing the image of a container transported upstream of the treatment unit in a container receptacle, can determine a necessary rotation of the container from the current position of same to the target position, and the alignment module can control the rotation of the container receptacle on the basis of the determined rotation.

Abstract: A method for optically inspecting containers in a drinks processing system, wherein the containers are transported as a container mass flow using a transporter and captured as camera images by an inspection unit arranged in the drinks processing system, and wherein the camera images are inspected for faults by a first evaluation unit using a conventional image processing method, wherein the camera images with faulty containers are classified as fault images and the faults are correspondingly assigned to the fault images as fault markings, wherein the camera images with containers considered to be good quality are classified as fault-free images, the fault images, the fault markings and the fault-free images are compiled as a specific training data set, and wherein, using the specific training data set, a second evaluation unit is trained in situ with an image processing method working on the basis of artificial intelligence.

Abstract: A method for inspecting containers, wherein the containers are transported in the form of a container mass flow by a transporter and are recorded as first measurement data by a first inspection unit and as second measurement data by a second inspection unit, wherein the first measurement data and the second measurement data are evaluated jointly by an evaluation unit using an evaluation method operating based on artificial intelligence to give output data, in order to ascertain an inspection result, such as for example a fill level, from the output data.

Abstract: A container inspection device and a container inspection device for inspecting containers are provided. The container inspection device comprises at least one light source for illuminating containers in an inspection clock for inspecting the containers. The container inspection device drives the at least one light source such that the at least one light source is observed by a person as constantly shining independent of the inspection clock.

Abstract: A container inspection device and a container inspection device for inspecting containers are provided. The container inspection device comprises at least one light source for illuminating containers in an inspection clock for inspecting the containers. The container inspection device drives the at least one light source such that the at least one light source is observed by a person as constantly shining independent of the inspection clock.

Abstract: A method for examining filled containers, which are filled with CO2-containing products such as beer or lemonade, for contaminants such as glass fragments, including examining, e.g. by means of a camera, a container for gas bubbles in the product/in the container and/or for gas mists in the container, and sorting out the container, if gas bubbles are determined in the product and/or if CO2 mists are determined in the container, and re-examining the container at a later time.

Abstract: A device and a method for aligning containers including an evaluation unit, which is capable of determining the position and/or the rotary position of at least one feature of the surface of containers to be aligned, by calculating image data of said feature in at least two views that differ with respect to the imaged rotary position of the containers, and where picture distortions and contrast limitations caused by the perspective can be compensated. The reliability of rotary position recognition and correction can thus be improved when aligning the containers.

Abstract: A device and a method for aligning containers including an evaluation unit, which is capable of determining the position and/or the rotary position of at least one feature of the surface of containers to be aligned, by calculating image data of said feature in at least two views that differ with respect to the imaged rotary position of the containers, and where picture distortions and contrast limitations caused by the perspective can be compensated. The reliability of rotary position recognition and correction can thus be improved when aligning the containers.

Abstract: A method for examining filled containers, which are filled with CO2-containing products such as beer or lemonade, for contaminants such as glass fragments, including examining, e.g. by means of a camera, a container for gas bubbles in the product/in the container and/or for gas mists in the container, and sorting out the container, if gas bubbles are determined in the product and/or if CO2 mists are determined in the container, and re-examining the container at a later time.