An industrial plant machine learning system includes a machine learning model, providing machine learning data, an industrial plant providing plant data and an abstraction layer, connecting the machine learning model and the industrial plant, wherein the abstraction layer is configured to provide standardized communication between the machine learning model and the industrial plant, using a machine learning markup language.

A method for the predictive maintenance of an automatic machine for manufacturing or packing consumer articles comprising the steps of: detecting and recording at least a sampling series relating to at least one motorization metric of at least one electric actuator, by means of at least one respective local control unit; transmitting the recorded sampling series to a data processing unit; defining at least one multidimensional tolerance horizon within an anomaly matrix having as dimensions at least two statistical features based on at least one sampling series detected and relative at least to the detected motorization metric; calculating the two statistical features in order to define the position of an actual condition within the anomaly matrix; determining, based on the position of the actual condition in the anomaly matrix and the multidimensional tolerance horizon, the imminence of necessary maintenance.

A method of providing artificial intelligence (AI) functionality to target legacy customer outreach platforms of a plurality of tenant enterprises includes storing a plurality of AI templates, each of which is associated with one or more AI routines, generating a campaign object associating one or more of the AI templates with a tenant enterprise from among the plurality of tenant enterprises, transforming a communication on a switching network associated with the tenant enterprise according to the one or more AI templates associated with the campaign object, and providing the transformed communication to a target legacy customer outreach platform of the tenant enterprise.

A method of configuring robot fleets with additive manufacturing capabilities includes receiving a request for a robotic fleet to perform a job and determining a job definition data structure based on the request. The job definition data structure defines a set of tasks to be performed in furtherance of the job. The method includes determining a provisioning configuration for each additive manufacturing system based on the task to which the additive manufacturing system is assigned, the set of 3D printing requirements, the printing instructions, and the status of the additive manufacturing system. The method includes provisioning the additive manufacturing system based on the provisioning configuration and a set of additive manufacturing system provisioning rules that are accessible to an intelligence layer to ensure that provisioned systems comply with the provisioning rules. The method includes deploying the robotic fleet based on the robotic fleet configuration data structure to perform the job.

Some embodiments include electric power demand stabilization methods and systems that may include measuring the power draw of a plurality of controllable devices; determining a rolling average power draw for the plurality of controllable devices over a period of time; measuring an instantaneous power draw of the plurality of controllable devices; and calculating a power budget comprising the difference between the instantaneous power draw and the rolling average power draw. In the event the power budget is positive, increasing power to at least a first subset of the plurality of controllable devices. In the event the power budget is negative, decreasing power to at least a second subset of the plurality of controllable devices.

Provided are a system, method, and computer program product for optimizing a manufacturing process. The method includes receiving manufacturing data associated with a manufacturing process for manufacturing a product. The manufacturing data may include data from a plurality of data sources associated with a plurality of stages of the manufacturing process, and the manufacturing data may include values for a plurality of parameters including at least one process parameter value and at least one quality parameter value. The method includes generating a time-sequenced data structure including the manufacturing data and transforming the time-sequenced data structure to a positionally-dimensioned data structure based on timing data associated with the plurality of stages. The method includes determining a new value for the at least one process parameter value based on the positionally-dimensioned data structure and at least one algorithm and optimizing the manufacturing process based on the new value.

A system includes an industrial automation component configured to receive a first voltage from a voltage source to enable the industrial automation component to perform one or more operations, a mechanical device configured to generate a second voltage, and a display configured to present image data. The display is configured to maintain presentation of the image data in absence of the first voltage received from the voltage source or the second voltage received from the mechanical device. The system also includes processing circuitry configured to use the second voltage generated by the mechanical device to adjust the image data presented by the display when the first voltage is unavailable.

A maintenance planning system for a production plant comprises: a production planning system for determining a production sequence for the production plant; an automation system for controlling production in the production plant; a state monitoring system for acquiring states of the production plant and its components; and a business planning system for the economic management of production and maintenance in the production plant. The maintenance planning system is designed for determining maintenance measures for the production plant. When determining the maintenance measures, the maintenance planning system takes into account the information of the production planning system, the automation system, the state monitoring system and the business planning system and performs optimization with regard to an economic utilization of the production plant. The disclosure further relates to a method for determining maintenance measures for a production plant and corresponding computer programs.

There is provided an equipment element maintenance analysis system including: a history information acquirer that is attached with at least one equipment element and acquires, at a predetermined timing, operation history information on a piece of manufacturing equipment for manufacturing a product; an error rate calculator that calculates an error rate based on the number of errors related to each of the at least one equipment element, included in the operation history information; a maintenance determiner that determines maintenance necessity of each of the at least one equipment element; and a notifier that notifies an information item on an equipment element determined to require maintenance among the at least one equipment. The error rate calculator calculates, as a latest error rate, an error rate in a latest predetermined period from the acquired operation history information, the maintenance determiner determines an equipment element with the latest error rate greater than or equal to a predetermined value among one or more equipment elements with a large number of errors as the equipment element that requires maintenance, the one or more equipment elements being included in the at least one equipment element, and the notifier lists the information on the equipment elements with a large number of errors in order, and notifies the number of errors and requirement of maintenance.

A robotic electric arc welding system includes a welding torch, a welding robot configured to manipulate the welding torch during a welding operation, a robot controller operatively connected to the welding robot to control weaving movements of the welding torch along a weld seam and at a weave frequency and weave period, and a welding power supply operatively connected to the welding torch to control a welding waveform, and operatively connected to the robot controller for communication therewith. The welding power supply is configured to sample a plurality of weld parameters during a sampling period of the welding operation and form an analysis packet, and process the analysis packet to generate a weld quality score, wherein the welding power supply obtains the weave frequency or the weave period and automatically adjusts the sampling period for forming the analysis packet based on the weave frequency or the weave period.