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.

Aspects of the disclosure relate to computing platforms that utilize improved techniques for dynamic event verification. A computing platform may receive first source data comprising driving data associated with a vehicle over a time period. Based on the first source data, the computing device may determine that the vehicle experienced an event, resulting in an event output. In response to determining the event output, the computing device may generate a request for second source data associated with the vehicle over the time period. The computing device may receive, from a sensor device, the second source data. Based on a comparison of the first source data to the second source data, the computing platform may determine an event comparison output. The computing platform may determine that the event comparison output exceeds a predetermined comparison threshold, and may send an indication of an event in response.

A diagnostic apparatus of the invention acquires normal data related to an operating state during normal operation of an industrial machine, stores the normal data, generates a learning model by learning based on the stored normal data, and performs an estimation process for normality or abnormality of an operation of the industrial machine using the learning model. The diagnostic apparatus of the invention further generates verification data including at least one piece of abnormal data based on the stored normal data to verify validity of the learning model on receiving a result of the estimation process using the learning model based on the verification data.

Some implementations of the disclosure are directed to tapping input/output (I/O) data from an industrial control system (ICS) or applying the tapped I/O data to a learned model to perform predictive or prescriptive maintenance. In one implementation, a method comprises: tapping I/O data from a controller of an ICS while the controller executes first control code to control one or more devices of the ICS; transmitting the tapped I/O data over a network to a second system; and executing, via the second system, second control code comprising an original or modified version of all or a subset of the first control code of the ICS, wherein the second control code executes in response to receiving the tapped I/O data. The output of executing the second control code may be provided to a model to predict a future event involving the ICS or to prescribe maintenance of the ICS.

A production facilities monitoring method monitors an operation status of a plurality of production facilities of a same kind located at a plurality of production sites and includes: a data information preparation step of aggregating operational data of each of the production facilities for each of the production sites; a data accumulation step of accumulating the operational data aggregated at the data information preparation step into a computer located at a data accumulation site; a data analysis step of analyzing a current operation status at each of the production facilities, using current operational data and past operational data accumulated at the data accumulation step; and an operation status determination step of determining whether operation is abnormal at each of the production facilities, based on an analysis result of the data analysis step.

An operation method for production facilities operates production facilities of a same kind located at production sites, and includes: a data information preparation step of aggregating operational data of each of the production facilities for each of the production sites; a data accumulation step of accumulating the operational data aggregated at the data information preparation step into a computer located at a data accumulation site; a data analysis step of analyzing an operation status of each of the production facilities, using the operational data accumulated at the data accumulation step; a data display step of displaying information on analyzed the operation status of each of the production facilities on a display unit located at each of the production sites; and a facility operation step of referring to the displayed information and operating a production facility located at a second production site from a first production site.

An abnormality diagnosis system performs an abnormality diagnosis of a plurality of motor systems that include a motor for moving a moving body. The abnormality diagnosis system identifies a comparison target system that is the motor system, among the motor systems, that is a comparison target in relation to a diagnosis target system that is the motor system to be a target of the abnormality diagnosis. The abnormality diagnosis system acquires a state-related value that is a value related to an operation state of the motor from each of the diagnosis target system and the comparison target system. The abnormality diagnosis system performs a comparison of the state-related value that is acquired from the diagnosis target system and the state-related value that is acquired from the comparison target system, and diagnoses a presence or absence of an abnormality in the diagnosis target system using a result of the comparison.

A method for predicting an operating anomaly comprises steps of (i) taking an assembly comprising at least a first and a second equipment item, each equipment item comprising a first operating parameter, (ii) recording and storing measurements over time of the first parameters for the first and the second equipment items, (iii) collecting the measurements during or after the completion of at least one part of an operating cycle, (iv) processing the collected measurements to detect a possible malfunction of the first and second equipment items by establishing a coefficient of determination, (v) emitting a first notification indicating the possible malfunction and/or triggering additional steps if the first coefficient of determination is less than a first threshold, and (vi) emitting a second notification and/or adjusting the first threshold if the first coefficient of determination is greater than or equal to the first threshold.

An output controller obtains a pair of safety state inputs, and, at each of a first microcontroller and the second microcontroller determines whether the pair of safety state inputs both show an unasserted state. Responsive to determining that the pair of safety state inputs both show an unasserted state, the output controller determining a normal state, and otherwise the output controller determines a safe state. The output controller outputs a binary software command reflecting either a normal state or a safe state, and converts the binary software command to a hardware command that maintains the state of voltage of a circuit where the binary software command reflects a normal state and otherwise switches to a safe state. The controller compares readback output values from the two microcontrollers, and generates an output therefrom.

A cloud-based industrial controller that controls devices, processes, and other assets of an industrial automation system via control algorithms that execute on a cloud platform is presented. A cloud-based collection component collects information from the industrial automation system via cloud gateways associated with the industrial automation system or extrinsic data sources. The cloud-based industrial controller can monitor and analyze the information, generate control instructions based on the analysis results, and communicate the control instructions to the devices, processes, and/or other assets of the industrial automation system to control operation of the industrial automation system. The cloud-based industrial controller also can interface with a industrial plant-based industrial controller, wherein the cloud-based industrial controller can determine supplemental control instructions to the industrial plant-based industrial controller, based on the information, including extrinsic information, to assist in controlling the industrial automation system and control decision-making.