An apparatus is provided for detecting fault conditions in the energy supply of a load. The apparatus comprises a fail-safe input-output (I/O) module circuit and a diagnostic circuit coupled to the fail-safe input-output (I/O) module circuit. The fail-safe input-output (I/O) module circuit includes a first switch coupled to a first resistor divider and a first output that supplies a DC supply voltage to the load via a first wiring to reduce a first voltage of the first output down to a first readback diagnostic output and a second switch coupled to a second resistor divider and a second output that supplies the DC supply voltage to the load via a second wiring to reduce a second voltage of the second output down to a second readback diagnostic output. The diagnostic circuit is to provide a first readback measurement signal from the first readback diagnostic output and provide a second readback measurement signal from the second readback diagnostic output. The apparatus is configured to provide the first readback measurement signal and the second readback measurement signal for the first output and the second output with and without the load such that when the first and second switches are open or OFF the first voltage at the first output with the load and the second voltage at the second output with the load indicate a NOT broken wire condition with respect to the first and second wirings while the first voltage at the first output without the load and the second voltage at the second output without the load indicate a broken wire condition with respect to the first and second wirings.

A controller includes a feature quantity generation unit that generates, from data associated with a control target, a feature quantity appropriate for detecting an abnormality in the control target, a machine learning unit that performs machine learning using the feature quantity, an abnormality detection unit that detects the abnormality based on an abnormality detection parameter determined from a learning result of the machine learning, and the feature quantity, an instruction unit that instructs the abnormality detection unit to detect the abnormality, and a data compression unit that compresses data about the feature quantity and provides the compressed data to the machine learning unit and the abnormality detection unit. The instruction unit transmits a request for detecting the abnormality to the abnormality detection unit. The abnormality detection unit detects the abnormality without returning a response to the request.

An I/O system having redundant removable and/or replaceable components. Each of the removable/replaceable components can be removed by displacement parallel to a common axis. An I/O device having an I/O base with a lock-out toggle to prevent installation of one or more I/O modules to the I/O base unless a ground screw has been secured to supporting structure.

An I/O system having redundant removable and/or replaceable components. Each of the removable/replaceable components can be removed by displacement parallel to a common axis. An I/O device having an I/O base with a lock-out toggle to prevent installation of one or more I/O modules to the I/O base unless a ground screw has been secured to supporting structure.

A controller includes a control operation unit that cyclically performs a control operation for controlling a control target, a data generator that generates data showing a chronological change in a value associated with a control target, and an analyzer that analyzes the data and outputs an analysis result in a predetermined analysis target period. The analysis target period includes a plurality of sections. The data generator sequentially outputs section data showing a chronological change in a value for each of the plurality of sections in the analysis target period. The analyzer analyzes the sequentially output section data for each section. When an analysis of section data for a section in the analysis target period shows a predefined result, the analyzer eliminates an analysis of section data for one or more sections in the analysis target period that are subsequent to the section for which the analysis shows the predefined result.

Various systems and methods may be used to implement a software defined industrial system. For example, an orchestrated system of distributed nodes may run an application, including modules implemented on the distributed nodes. The orchestrated system may include an orchestration server, a first node executing a first module, and a second node executing a second module. In response to the second node failing, the second module may be redeployed to a replacement node (e.g., the first node or a different node). The replacement mode may be determined by the first node or another node, for example based on connections to or from the second node.

Apparatus, systems, and methods for communicating data between a controller and a multiplicity of field devices operating in a process plant are provided. The system includes distributed marshaling modules coupled by a head-end unit to I/O cards in communication with the controller. The distributed marshaling modules communicate with the field devices via respective electronic marshaling components converting signals between the field devices and the I/O cards. The distributed marshaling modules are coupled to the head-end unit by a ring communication architecture, such that the distributed marshaling modules may each be located relatively proximate to the field devices to which they are coupled.

A power distribution grid for a facility, such as a data center, is located within the facility. The power distribution grid includes a plurality of power transport elements arranged in a grid pattern and nodes located at intersections of the grid pattern. Electrical loads are supplied power via respective nodes of the power distribution grid. Also, each node is supplied power from more than two transport elements, such that one or more transport elements can fail and electrical loads connected to a particular node associated with the failed transport elements continue to receive electrical power supplied to the particular node from at least two different transport elements.

Disclosed is a system for swapping springs present in a product. A data receiving module receives metadata associated to a spring. The metadata comprises a pitch, vector coordinates and alike. A comparison module compares the pitch associated to the spring with a predefined threshold value thereby categorizing each spring into one of a category including a utilized spring category and an underutilized spring category. A determination module determines an underutilized spring, amongst the underutilized spring category, based on at least one of the vector coordinates and the pitch, when the spring is categorized in the utilized spring category. Subsequent to determining the underutilized spring, the swapping module swaps the spring with the underutilized spring by using a control mechanism.

An information processing device includes an actuator emulator simulating a behavior of a first drive apparatus that is for driving a first control target, an actuator emulator simulating a behavior of a second drive apparatus that is for driving a second control target, a storage device for storing a PLC program including an instruction group with respect to the actuator emulator and a robot program including an instruction group with respect to the actuator emulator, a timer generating a virtual time, and a PLC emulator for repeatedly executing the instruction group included in the PLC program in each predetermined first control period in accordance with measurement using the virtual time, and a robot controller emulator for sequentially executing the instruction group included in the robot program in a predetermined execution order in accordance with the virtual time.