Abstract: An automated cleaning machine may include one or more short cleaning cycles in which the duration of a cleaning cycle is shortened relative to the duration of a default cleaning cycle. During a short cleaning cycle, other cleaning cycle parameters may also be adjusted to ensure that the articles subjected to the short cleaning cycle are adequately cleaned and sanitized. For example, the wash temperature, rinse temperature, and/or cleaning product amounts or concentrations, may be adjusted to account for the shortened duration of the cleaning cycle. The automated cleaning machine may further include one or more short cycle mode(s) during which short cleaning cycle parameters are used and one or more default cycle mode(s) during which default cleaning cycle parameters are used.

Abstract: A method for testing a basic parameterization of a component in an automation system is provided. The method includes: starting a test run of the component in the automation system with the basic parameterization, measuring of a measured value data record during trial operation, access to a machine learning module comprising a pre-trained neural network, wherein the pre-trained neural network is pre-trained to calculate a target parameterization for the respective component for a measured value data set, wherein the basic parameterization is compared with the calculated target parameterization and in the event of deviation a result message for adapting the basic parameterization is provided, and receipt of the provided result message for adaptation of the basic parameterization.

Abstract: An error detection and localisation in a pneumatic system and in particular an error detection module includes a read-in interface for reading-in digital signals from the automation plant, a first processor unit designed to execute a detection algorithm for calculating an anomaly score for the automation plant on the basis of the set of read-in signals, a second processor unit which is designed—in the event that the anomaly score calculated with the first processor unit indicates an anomaly—to perform a machine localisation method for localising the error, wherein the machine localisation method has been trained in a training phase in order to calculate and as a result provide, on the basis of a detected circuit diagram of the automation plant with respect to the calculated anomaly score, probabilities of possible causes of error in relation to individual components of the automation plant.

Abstract: A method is provided for verifying a message behavior of a control unit for an automation system having a plurality of components, the control unit communicating with the components and the components communicating with each other via a communication network, with the steps being carried out on at least one component: receiving at least one message via the communication network, wherein the at least one message is provided by the controller analyzing the at least one received message according to a characteristic message description, and providing a verification message comprising a verification of the message behavior of the control unit as acceptable if the analyzed message matches the characteristic message description.

Abstract: A method for testing a basic parameterization of a component in an automation system is provided. The method includes: starting a test run of the component in the automation system with the basic parameterization, measuring of a measured value data record during trial operation, access to a machine learning module comprising a pre-trained neural network, wherein the pre-trained neural network is pre-trained to calculate a target parameterization for the respective component for a measured value data set, wherein the basic parameterization is compared with the calculated target parameterization and in the event of deviation a result message for adapting the basic parameterization is provided, and receipt of the provided result message for adaptation of the basic parameterization.

Abstract: An error detection and localisation in a pneumatic system and in particular an error detection module includes a read-in interface for reading-in digital signals from the automation plant, a first processor unit designed to execute a detection algorithm for calculating an anomaly score for the automation plant on the basis of the set of read-in signals, a second processor unit which is designed—in the event that the anomaly score calculated with the first processor unit indicates an anomaly—to perform a machine localisation method for localising the error, wherein the machine localisation method has been trained in a training phase in order to calculate and as a result provide, on the basis of a detected circuit diagram of the automation plant with respect to the calculated anomaly score, probabilities of possible causes of error in relation to individual components of the automation plant.