Systems and methods for simulating cyber-physical systems are disclosed. A plurality of geographic simulation layers representing respective infrastructure sectors of a real-world environment may be generated, and the layers may be linked together with one another to create a multi-layer simulation. The associations between the layers of the simulation may be adjusted, and characteristics of the simulation layers themselves may be adjusted, to ensure that the simulation conforms to characteristics of the real-world environment being simulated. In some embodiments, a multi-user simulation system allows users at separate terminals to execute attack inputs and defense inputs against the simulation to try to destabilize and stabilize the simulation, respectively. Results of the attack inputs and defense inputs may be simultaneously displayed on a plurality of terminals.

A method for derivation of a machine signature includes receiving sensor information from a primary sensor, where the primary sensor is positioned to receive information from a portion of an industrial operation, and receiving sensor information from one or more secondary sensors. The secondary sensors are arranged to provide additional information about the industrial operation indicative of current operating conditions of the industrial operation. The method includes using the sensor information from the secondary sensors and machine learning to determine if the portion of the industrial operation is operating in a normal condition and, in response to determining that the portion of the industrial operation is operating normally, using sensor information from the primary sensor during the normal operating condition to derive a primary sensor signature for the sensor information from the primary sensor.

A machining diagnosis device (100) includes a machining data acquirer (3) to acquire, from a machining tool (1), machining data including a result of machining performed based on a machining condition, a cutting section extractor (4) to extract, from the acquired machining data, a cutting section corresponding to a stable machining period and machining data for the cutting section, a cleansing unit (8) to acquire the machining condition for cutting and perform, in accordance with the acquired machining condition, cleansing of the machining data extracted by the cutting section extractor (4), a feature calculator (9) to calculate a feature based on the cleansed machining data, and a machining diagnoser (11) to diagnose machining based on the calculated feature.

A method, system and computer program product for a heuristic determination of in-process damage class control to manage expected output product category. The heuristic technique determines the predicted damages and their ranges while keeping the initial expected defects, the respective classes and range of defect and mitigation. The method dynamically computes a damage mitigation range of operation while being within the overall constraints and completes the computation in smaller number of loops being run at the edge computers so that the manufacturing equipment can operate at a higher velocity for higher quality of the output. The method includes a step of reducing error of the co-efficient and damage counts. An Internet of Things (IoT) based robot is used to mitigate the damages in the manufacturing steps to ensure that the output class of the product remains what was expected at the start despite damages and mitigation measures.

Methods and systems for autonomous and semi-autonomous vehicle control, routing, and automatic feature adjustment are disclosed. Sensors associated with autonomous operation features may be utilized to search an area for missing persons, stolen vehicles, or similar persons or items of interest. Sensor data associated with the features may be automatically collected and analyzed to passively search for missing persons or vehicles without vehicle operator involvement. Search criteria may be determined by a remote server and communicated to a plurality of vehicles within a search area. In response to which, sensor data may be collected and analyzed by the vehicles. When sensor data generated by a vehicle matches the search criteria, the vehicle may communicate the information to the remote server.

The disclosure relates to a method for operating a production plant having a plurality of processing stations, wherein the workpieces to be processed can be transported to the various processing stations by means of a transport system, wherein a plurality of computers are provided which are connected for data exchange via a computer network, the production plant being controlled by means of a plurality of control processes. According to the disclosure, during the operation of the production plant an initial decision is taken automatically about which control process is executed on which computer, wherein the initial decision is then implemented automatically.

An information processing device includes: a memory; and a processor coupled to the memory and configured to: learn a classification rule that classifies an abnormal degree of a production facility from a text feature amount based on the text feature amount obtained from a number of texts included in a plurality of pieces of log data obtained in a predetermined process of the production facility and production history information of the production facility; extract a text feature amount of log data to be monitored obtained in the predetermined process of the production facility; and determine an abnormal degree of the production facility when the log data to be monitored is obtained based on the text feature amount and the classification rule.

Data is received that includes a feed of images of a plurality of objects passing in front of each of a plurality of inspection camera modules forming part of each of a plurality of stations. The stations can together form part of a quality assurance inspection system. The objects when combined or assembled, can form a product. The received data derived from each inspection camera module can be separately analyzed using at least one image analysis inspection tool. The analyzing can include visually detecting a unique identifier for each object. The images are transmitted with results from the inspection camera modules along with the unique identifiers to a cloud-based server to correlate results from the analyzing for each inspection camera module on an product-by-product basis. Access to the correlated results can be provided to a consuming application or process via the cloud-based server.

Systems and methods for simulating cyber-physical systems are disclosed. A plurality of geographic simulation layers representing respective infrastructure sectors of a real-world environment may be generated, and the layers may be linked together with one another to create a multi-layer simulation. The associations between the layers of the simulation may be adjusted, and characteristics of the simulation layers themselves may be adjusted, to ensure that the simulation conforms to characteristics of the real-world environment being simulated. In some embodiments, a multi-user simulation system allows users at separate terminals to execute attack inputs and defense inputs against the simulation to try to destabilize and stabilize the simulation, respectively. Results of the attack inputs and defense inputs may be simultaneously displayed on a plurality of terminals.

Methods and systems of predicting the growth rate of hydrogen-induced cracking (HIC) in a physical asset (e.g., a pipeline, storage tank, etc.) are provided. The methodology receives a plurality of inputs regarding physical characteristics of the asset and performs parametric simulations to generate a simulated database of observations of the asset. The database is then used to train, test, and validate one or more expert systems that can then predict the growth rate and other characteristics of the asset over time. The systems herein can also generate alerts as to predicted dangerous conditions and modify inspection schedules based on such growth rate predictions.