Some implementations of the disclosure are directed to a method, comprising: receiving tapped data that was tapped from a controller of an industrial control system (ICS) while the controller executed first control code to control ICS devices of the ICS, the tapped data used during execution of the first control code, and the tapped data comprising input data obtained from one or more input components of the controller communicatively coupled to the ICS devices, or output data obtained from one or more output components of the controller communicatively coupled to the ICS devices; and after receiving the tapped data, executing, using at least the tapped data, second control code that provides an emulation of the controller, the emulation comprising running, using at least the tapped data, a process of the first control code at a faster rate than it is run by the controller executing the first control code.

A flexible assembling system incorporating at least one manufacturing cell including at least one universal fixture, at least one position monitoring device (e,g., monitoring system, 3D vision system, 3D vision system/scanner, artificial intelligence position determination and monitoring, artificial intelligence vision system to locate part or a feature(s) of the part, and combinations thereof), and at least one programmable robot or machine (e.g., punch, weld, etc.). The at least one universal fixture is a non-dedicated fixture such that the fixture is usable with various products and not dedicated to only one specific part being manufactured.

Embodiments of a software defined automation system that provides a reference architecture for designing, managing and maintaining a highly available, scalable and flexible automation system. In some embodiments, an SDA system can include a localized subsystem including a system controller node and multiple compute nodes. The multiple compute nodes can be communicatively coupled to the system controller node via a first communication network. The system controller node can manage the multiple compute nodes and virtualization of a control system on a compute node via the first communication network. The virtualized control system includes virtualized control system elements connected to a virtual network that is connected to a second communication network to enable the virtualized control system elements to control a physical control system element via the second communication network connected to the virtual network.

Examples of techniques for event prediction in a control communication network are disclosed. Aspects include determining state data associated with one or more devices associated with a control communication network, generating, by a machine learning model, a feature vector comprising a plurality of features extracted from the state data, and determining one or more event predictions associated with the control communication network based at least in part on the feature vector.

A method to simulate operations of a manufacture plant comprising a plurality of machines, the method including receiving a capacity function and an elapsed time function associated with a first machine of the plurality of machines, wherein the capacity function and elapsed time function is defined by one or more parameters characterizing the first machine, receiving a record of historical production data associated with the first machine, calculating, based on the capacity function and the record of historical production data, an augmented capacity function and an augmented elapsed time function that is defined by the one or more parameters and a quantity relating to parts waiting for processing (WIP), and simulating the operations of the plant based on the augmented capacity function and augmented elapsed time function.

Systems and methods for data collection for an industrial heating process are disclosed. The system according to one embodiment can include a plurality of data collectors, including a swarm of self-organized data collector members, wherein the swarm of self-organized data collector members organize to enhance data collection based on at least one of capabilities and conditions of the data collector members of the swarm, and wherein the plurality of data collectors is coupled to a plurality of input channels for acquiring collected data relating to the industrial heating process, and a data acquisition and analysis circuit for receiving the collected data via the plurality of input channels and structured to analyze the received collected data using a neural network to monitor a plurality of conditions relating to the industrial heating process.

Provided is a simulation method performed by a process simulator, implemented with a recurrent neural network (RNN) including a plurality of process emulation cells, which are arranged in time series and configured to train and predict, based on a final target profile, a profile of each process step included in a semiconductor manufacturing process. The simulation method includes: receiving, at a first process emulation cell, a previous output profile provided at a previous process step, a target profile and process condition information of a current process step; and generating, at the first process emulation cell, a current output profile corresponding to the current process step, based on the target profile, the process condition information, and prior knowledge information, the prior knowledge information defining a time series causal relationship between the previous process step and the current process step.

Method, apparatus and computer program product for detecting vulnerability in an industrial control system, predicting maintenance in an industrial control system, and defining authorization rules for peripheral devices based on peripheral device categorization are described herein.

A control layer automation device comprises a processor, one or more control layer applications, a database, a wireless interface, a device memory. Each control layer application is configured to perform a discrete set of automation functions. The database comprises a plurality of operator device identifiers and the wireless interface allows the one or more control layer applications to communicate with a plurality of operator devices via the plurality of operator device identifiers. The device memory comprises the one or more control layer applications. The control layer application manager is configured to manage execution of the one or more control layer applications on the processor.

The present disclosure describes system and methods for inferring energy usage at multiple levels of granularity. One embodiment describes an industrial automation system including a first industrial automation component, a first sensor coupled to the first industrial automation component, in which the first sensor measures a first amount of power supplied to the first industrial automation component, a second industrial automation component that couples to the first industrial automation component, and an industrial control system that infers energy usage by the first industrial automation component and the second industrial automation component based at least in part on the first amount of power supplied to the first industrial automation component.