A control system includes a support device used for developing a user program to be executed on a controller, and a display device that provides a supervisory control screen by referring to information held by the controller. The support device extracts a relationship between an output signal and one or more input signals defined by the user program and generates input-output correspondence information, and transmits the input-output correspondence information or screen data reflecting the input-output correspondence information to the display device. The display device provides, on the basis of the input-output correspondence information or the screen data reflecting the input-output correspondence information, the supervisory control screen showing the current value of a selected output signal and the current value of one or more input signals associated with the output signal by referring to the values of input and output signals held by the controller.

A decision support system and method for an industrial plant is configured and operates to: obtain a causal graph modeling causal assumptions relating to conditional dependence between variables in the industrial plant; obtain observational data relating to operation of the industrial plant; and perform causal inference using the causal graph and the observational data to estimate at least one causal effect relevant for making decisions when operating the industrial plant.

A method and system for monitoring a rail vehicle are provided. The method includes: a local device acquires current operating data and historical operating data of a rail vehicle from a controller of the rail vehicle, herein the local device, which is connected with the controller, is a portable testing device; the local device obtains fault information according to the current operating data and the historical operating data, herein the fault information includes at least one of a fault code and a fault description; the local device obtains a maintenance plan corresponding to the fault information, herein the maintenance plan is obtained through a remote device or a server side; and the local device maintains the rail vehicle according to the maintenance plan. The disclosure solves a technical problem in the traditional art that a troubleshooting process of a rail vehicle is difficult, and when the rail vehicle has a fault, the fault cannot be solved timely.

Systems, methods and apparatus include a computer-implemented method that performs the following. First inputs are received from a first monitoring of the main power distribution system (MPDS) at a safety instrumented system (SIS) logic solver. The first inputs include power status information for equipment monitored by a main distribution switchgears (MDS). A second input is received at the SIS logic solver, from a second monitoring of the MPDS. The second input includes power status information for equipment monitored by a motor control center (MCC). SIS logic solver logic in the SIS logic solver is executed by the SIS logic solver using at least one of the first and second MPDS inputs. Upon confirmation of a power failure in the MPDS detected instantly by MDS or MCC functional safety capable controllers, the SIS logic solver logic generates an output signal to cut an incoming feed to processing plants, leading to discard power failure as a worst credible design case scenario for flare and disposal relief systems, the worst credible design case scenario caused by at least one of the first inputs and the second inputs.

A monitoring target selecting device configured to output a measurement parameter to an abnormality diagnosis device to diagnose an abnormal event of a plant based on a correlation value representing a mutual correlation between measurement parameters, and includes a classification unit to acquire a plurality of measurement parameters measured in the plant, classify a change behavior of measured value over a time for each of the plurality of measurement parameters in a first period, and classify a change behavior of a measured value over a time for each of the plurality of measurement parameters in each of the first period and a second period, and a selection unit to select the measurement parameter as a measurement parameter to be output to the abnormality diagnosis device on the basis of a result of comparing a behavior of the measurement parameters in the first period to the second period.

An industrial plant operator intervention system for use in an industrial plant includes a processing unit configured to monitor and analyze industrial plant operation data to detect an anomaly in the industrial plant operation data that warrants initiating an operator intervention, and in response to detecting the anomaly, automatically determine a user interface configuration of a user interface to be presented to a designated operator who is to perform the operator intervention. The user interface configuration is determined on the basis of technical context data, including industrial plant operation data associated with the anomaly, and on the basis of operator data pertaining to the designated operator, in such a manner that an anomaly-related and operator-specific user interface configuration is obtained.

According to some embodiments, system and methods are provided including receiving, via a communication interface of an event detection and classification module comprising a processor, data from one or more sensors in a system; determining an event occurred based on the received data; applying a coherency similarity process to the received data via a classification module; determining whether the event is an actual event or a mal-doer event based on an output of the classification module; transmitting the determination of the event as the actual or the mal-doer event; and modifying operation of the system based on the transmitted output. Numerous other aspects are provided.

A method includes acquiring data indicating a plurality of sensor detection values arranged in order along a specific variable axis, detecting, in the data, a plurality of change points that are data points at which the sensor detection value on the specific variable axis changes by a predetermined value or more, and calculating, for a target change point that is one change point among the plurality of change points, an index value representing a degree of abnormality using the target change point and a reference change point that is another change point.

A diagnostic device, which is communicatively connected to a server that learns an abnormality of an industrial apparatus and generates a classification learning model for the abnormality, includes a sensor signal acquisition unit that acquires a sensor signal including a measurement value measured by a sensor in the industrial apparatus. The diagnostic device also includes an apparatus diagnosis unit that diagnoses whether the industrial apparatus is normal or abnormal based on the acquired sensor signal, a classification unit that classifies the abnormality based on the sensor signal and the classification learning model, when the industrial apparatus is abnormal, and a transmission unit that determines whether to transmit the sensor signal to the server based on the diagnosis result and/or and the classification result, and transmits the sensor signal to the server when it is determined that the sensor signal can be transmitted.

A method and an automation component for identifying a process-disrupting automation component in an industrial automation assembly, wherein a process disruption is determined in a first automation component and examined by a first local analysis device, where an automation component arranged upstream and/or an automation component arranged downstream is first determined by each automation component, an interrogation message is sent from a first automation component to a second automation component and the same interrogation message or a further interrogation message is recursively sent by the second automation component to a third automation component arranged upstream or downstream of the second automation component and processed, where in the event of a locally determined disruption, the relevant automation component sends a response message, which is back-propagated to the origin and signaled such that a decentralized error analysis becomes possible, even with a changing system topology, without the need for any redesign work.