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 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 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: Method for the optical inspection of containers, where the containers are transported with a conveyor as a container mass flow, where the containers are each captured with an optical inspection unit as first image data, and where the first image data is evaluated with an image processing unit for contamination and/or defects on the respective container, where the first image data of several containers is overlaid to form an overlay image, and where the overlay image is evaluated for the presence of points of contamination in a beam path of the optical inspection unit.

Abstract: Method for the optical inspection of containers, where the containers are transported with a conveyor as a container mass flow, where the containers are each captured with an optical inspection unit as first image data, and where the first image data is evaluated with an image processing unit for contamination and/or defects on the respective container, where the first image data of several containers is overlaid to form an overlay image, and where the overlay image is evaluated for the presence of points of contamination in a beam path of the optical inspection unit.