A robot state monitoring device 2 comprises: a camera which captures an image of a robot 3 under the control of a controller; a moving image generation unit which associates video data of the robot 3 acquired by the camera with input/output signals DO[1], AO[1], DI[1], and AI[1] of the controller along a time axis 830, and generates a moving image showing a state change of the robot 3 and the input/output signals DO[1], AO[1], DI[1], and AI[1]; and a moving image playback device which plays back the moving image generated by the moving image generation unit

The moving image generation unit acquires the values of the input/output signals DO[1], AO[1], DI[1], and AI[1] at the recording time of each frame under the same cycle as the frame rate of the video data.

A graphical programming interface for programming an inspection process of samples. The inspection process is a recognition process, based on images, of defects of samples. A graphical programming interface includes at least one image input type block including an input into a memory of a multiplicity of images of at least one sample, a multiplicity of operation type blocks. Each operation type block includes at least one operation which processes images, a multiplicity of sorting type blocks. The graphical programming interface allows at least one image input type block to be selected if more than one input type block is provided for, at least one operation type block among the multiplicity of operation type blocks, and at least one sorting type block among the multiplicity of sorting type blocks.

A method for computer-implemented determination of sensor positions in a simulated process of an automation system, wherein the simulated process includes a digital process description of an automation task to be executed by components, the process description including a movement specification describing the movement of the components during execution of the automation task, and including a digital sensing description defining a sensing task to be performed by a sensor during execution of the automation task and at least one sensing constraint of the sensor and the sensor volume of the sensor.

An image-processing unit, a system, an automatic loading and/or unloading system and a method for detecting a locking device on a movable object, wherein the locking device locks a plurality of movable objects to one other and is arranged in a locking region of the movable object and the movable object is moved via a loading device, where the locking region can be sensed in a sensing region of at least one camera and an image-processing unit evaluates a signal of the camera to detect locking devices in a locking region of a movable object so as to detect locking devices efficiently and reliably.

A system for determining a health condition and an anomaly of a field equipment 104 is provided. The system includes sensors 106A-N which sense information associated with the equipment 104, a field device 110 which receives the sensor data from sensors 106A-N, a camera unit 108 that captures visual data of the equipment 104 and a server 112. The server 112 includes a database 114 that stores the sensor data and the visual data. The server 112 further includes a fault detection module 202 that processes the sensor data to determine a fault or the health condition of the equipment 104, an image processing module 204 that is trained to detect the irregularities/anomaly in the equipment 104 by processing the visual data, and a report generation module 206 that generates an automated health report 212 based on the detected anomaly and the health condition of the equipment 104.

A system and method for monitoring the condition of a travelling grate machine including univocally identifying each pallet car; collecting a plurality of condition indicating parameters for the wheels, the grate bars, the car body and/or the side walls of the pellet car; attributing the collected condition indicating parameters to an individual pallet car; storing the collected condition indicating parameters for each pallet car in a database; evaluating the condition of the travelling grate machine; comparing the different condition indicating parameters collected by the different sensor means of each pallet car to reference parameters and/or to previously collected condition indicating parameters of that same pallet car; identifying the faults in each pallet car based on this comparison; classifying each pallet car according to its need of maintenance based on the severity of different identified faults; and determining the pallet car in most need of maintenance based on this classification.

A method for tracking a target object using a detection apparatus includes calculating movement state information of the target object with respect to the detection apparatus according to one or more monitored images, calculating a distance between the target object and the detection apparatus, in response to the distance being not greater than a pre-set distance threshold, estimating, based on first state information including the movement state information, second state information of the target object after a pre-set delay duration, and performing an operation with respect to the target object according to the estimated second state information.

The present disclosure describes a computer-implemented method to manage an industrial plant facility, the method including: monitoring multiple streams of input data from a network of sensors at the industrial plant facility, wherein the network of sensors include one or more camera devices; determining, by a server computer, an event during construction or operation of the industrial plant facility based on analyzing the multiple streams of input data in real-time; and based on the determined event, generating a notification to alert at least one operator of the industrial plant facility.

Drones (e.g., unmanned aerial vehicles, or “UAVs”) equipped with cameras and sensors may be configured to travel throughout the field environment of a process plant to monitor process plant conditions. Onboard computing devices associated with the drones control the movement of the drones through the field environment of the process plant. The onboard computing devices interface with the cameras and other sensors and communicate with user interface devices, controllers, servers and/or databases via a network. The onboard computing devices may receive drone commands from user interface devices and/or servers, or may access drone commands stored in one or more databases. The onboard computing devices may transmit data captured by the cameras and/or other sensors to UI devices, controllers, servers, etc. Accordingly, the user interface devices may display data (including live video feeds) captured by the drone cameras and/or drone sensors to an operator in a human machine interface application.

A control apparatus (20) includes a control device (21) and the control device (21) includes: a detector (214) that detects an entering person (P) entering a range (E1) of motion of an arm (11); and a limiter (215) that, upon detection of the entering person (P) entering the range (E1) of motion by the detector (214), sets a behavior prohibition range (E2) of the arm (11) where the arm (11) is kept from coming into contact with the entering person (P), thus limiting a behavior of the arm.