A control system in which a plurality of controllers and a redundant controller are connected to a common network, the plurality of controllers are divided into a highly available controller and other normal controllers, the redundant controller obtains context data of the highly available controller to hold the context data in a memory, holds programs of the plurality of controllers in a non-volatile memory, and holds the program of the highly available controller in the memory, and an arithmetic unit substitutes processing of the highly available controller by using the context data and the program of the highly available controller held in advance in the memory when a failure occurs in the highly available controller.

An automated system includes transducers, at least one computing device, and at least one automated apparatus. The transducer(s) is/are driven and sensed using drive-sense circuit(s). A drives and senses drive and sense a transducer via a single line, generates a digital signal representative of a sensed analog feature to which the transducer is exposed, and transmits the digital signal to the computing device. The computing device receives digital signals from at least some of drive-sense circuits and process them in accordance with the automation process to produce an automated process command. The automated apparatus executes a portion of an automated process based on the automated process command.

Systems, methods, and apparatus for prediction of maintenance service for machines. In one example, one or more sensors are configured to generate a sensor data stream during operation of a machine. An artificial neural network (ANN) is configured to receive the sensor data stream and predict a maintenance service for the machine based on the sensor data stream. For example, the ANN can be trained using the sensor data stream collected within a predetermined time period of a machine being newly-installed in an assembly line or other industrial automation facility. The machine can be considered to be operating in a normal condition during the predetermined time period such that the ANN can be trained to detect anomaly that deviates from the normal patterns of the sensor data stream. For example, the ANN can be a spiking neural network (SNN).

An extension device for one or more automation devices in an industrial system is provided. Industrial data processing units capable of performing data processing based on one or more artificial neural networks are provided. To enable and/or accelerate one or more computations in an industrial system, thereby simplifying integration of artificial intelligence into the industrial system, and to simplify data exchange between an extension device capable of processing data using artificial intelligence and an automation device, one or more results of the one or more computations are obtained. The results indicate one or more states of the industrial system. The one or more results are provided via a process state model shared with the automation device to monitor and/or control the industrial system.

A method and apparatus for efficiently dismantling hard disk drives, including a holder assembly to clamp a hard disk drive, a scanner that reads identification information of the hard disk drive. A chain conveyor belt indexes the hard disk drive to a position at which a logic board of the hard disk drive is peeled off and dropped into a bin, and to a position at which a lid of the hard disk drive is separated from a housing by way of a wedge, and indexes to a punch position at which magnets are punched in a direction from the logic board side of the hard disk drive. By separating and disposing the logic board and the lid, each hard disk drive is efficiently and securely dismantled.

A system for performing industrial automation control may include a first device that generates a first set of data formatted according to a first protocol and a second device that generates an automation command. The automation command may control the first device based on the first set of data. The second device may interpret data formatted according to a second protocol. The system may include a broker system coupled between the first device and the second device to transform data communicated between the devices, such as the first set of data, the automation command, or both. For example, the broker system may transmit a set of data generated to communicate the first set of data to the second device formatted according to the second protocol as opposed to being formatting according to the first protocol.

Embodiments of system and methods for providing centralized management of a software defined automation (“SDA”) system are disclosed. The SDA system comprises of a collection of controller nodes and logically centralized and yet physically distributed collection of compute nodes by monitoring activities of the compute nodes. In accordance with some embodiments, one or more components of the system monitor execution, network and security environments of the system to detect an event in a first environment. In response to the detected event, at least one component in the first environment is remediated, the remediation of the first environment creating a trigger to cause remediation of at least one component in each of a second and third environments.

A machine automation system for controlling and operating an automated machine. The system includes a controller and sensor bus including a central processing core and a multi-medium transmission intranet for implementing a dynamic burst to broadcast transmission scheme where messages are burst from nodes to the central processing core and broadcast from the central processing core to all of the nodes.

A method for generating a dynamic model of an industrial plant having: a plurality of physical processes that are dependent such that an outcome of at least one first process is fed into at least one second process; a plurality of low-level controllers, each controller acting upon at least one physical process such that at least one process variable of the at least one physical process is controlled to match a set-point of the low-level controller; and a plurality of sensors, each sensor measuring at least one process variable of one of the physical processes, and/or of the plant as a whole, the set-points of the low-level controllers and current values of the process variables measured by the sensors being the inputs of the model, and predicted future values of the process variables that are likely to result from applying the set-points to the low-level controllers being the outputs.

A system may include a data delivery pipeline communicatively coupled to one or more microservices that receive a dataset transmitted through the data delivery pipeline. The system may also include a first microservice that receives a first dataset corresponding to operation technology (OT) data or information technology (IT) data and determines a second dataset based on the first dataset. The system may also include a second microservice that receives the second dataset from the first microservice via the data delivery pipeline, determines an action to perform in an industrial automation component of an industrial automation system based on an analysis of the second dataset, and transmits the action to the industrial automation component via the data delivery pipeline.