An insulating glazing unit includes at least one spacer, which is shaped around the periphery to produce a spacer frame and delimits an inner region, a first glass pane, which is arranged on a pane contact surface of the spacer frame and a second glass pane, which is arranged on a second pane contact surface of the spacer frame, and the glass panes project beyond the spacer frame and an outer region is formed, which is filled, at least in some sections with a sealing element. At least one NFC transponder is arranged in the inner region, the NFC transponder includes an electronics unit, which is arranged on the inner surface of the spacer, and an antenna unit, which is arranged on the inner surface of one of the glass panes, and the electronics unit is electrically conductively connected to the antenna unit.

Systems, methods, and apparatus for prediction of maintenance service for machines. In one example, one or more sensors are configured to generate a sensor data stream during operation of a machine. An artificial neural network (ANN) is configured to receive the sensor data stream and predict a maintenance service for the machine based on the sensor data stream. For example, the ANN can be trained using the sensor data stream collected within a predetermined time period of a machine being newly-installed in an assembly line or other industrial automation facility. The machine can be considered to be operating in a normal condition during the predetermined time period such that the ANN can be trained to detect anomaly that deviates from the normal patterns of the sensor data stream. For example, the ANN can be a spiking neural network (SNN).

An illustrative example method of monitoring value added activity includes positioning a detector near a selected portion of a machine using a clip for situating the detector in a position where the detector can detect at least one electrical characteristic associated with operation of a machine; communicating an indication of the detected electrical characteristic between the detector and a user interface; and displaying a visual representation of value added activity information based the indication. The value added activity corresponds to human operator performance that is distinct from machine performance during a manufacturing or assembly process.

A diagnostic device (10) includes an acquirer (101) and a diagnoser (140). The acquirer (101) acquires a plurality of input signals including a target signal to be diagnosed for abnormality. The diagnoser (140) diagnoses, using a first index value relating to the target signal and a second index value relating to the plurality of input signals based on a correlation between the plurality of input signals, whether an abnormality occurs. The first index value indicates a degree of similarity of a waveform of the target signal to a predetermined reference waveform. The second index value is a value that is based on comparison between the target signal and a predetermined pattern and is calculated from values of the plurality of input signals.

The present invention relates to an equipment diagnosis method using equipment diagnosis system comprising: an imaging module (110) for collecting image data by photographing the equipment having an equipment controller, in which a PLC is loaded, embedded therein; a diagnostic module (120) including hardware having software for diagnosing whether the equipment is normal or abnormal; and a plurality of IoT sensor units (130) for monitoring an object to be monitored, and thus a user can quickly diagnose, identify, and cope with a specific cause of an equipment failure on the basis of objective data provided from a PLC memory area, and image file, and an IOT sensor unit at the occurrence of various types of events generated by a diagnostic module for each condition designated by the user according to the state of equipment.

According to an exemplary embodiment of the present disclosure, a computer program stored in a computer readable storage medium is disclosed. The computer program performs operations for processing input data when the computer program is executed by one or more processors of a computer device, the operations including: obtaining input data based on sensor data obtained during manufacturing of an article by using one or more manufacturing recipes in one or more manufacturing equipment; inputting the input data to a neural network model loaded to the computer device; generating an output by processing the input data by using the neural network model; and detecting an anomaly for the input data based on the output of the neural network model.

A method for estimating a Remaining Useful Life of a subject equipment, with a preliminary phase including the following steps: acquire test observations (step 10) and produce test time series (S.sub.x) of at least one signature; partition the test time series to obtain severity classes corresponding to the ageing phases of the test equipment devices (step 14); carry out an initial learning of a diagnosis model on the test equipment devices (step 45); perform a second learning of a signature prediction model (step 51). There is also an operational phase including the following steps: acquire observations when in operation on the subject equipment and produce an extrapolated time series using the prediction model; classify the extrapolated time series using the diagnosis model and derive the remaining useful life of the subject equipment.

A production management system 100 includes an operating state acquisition apparatus 20a and a production management apparatus 10. The operating state acquisition apparatus 20a includes a detector 21a that is retrofitted to be mounted on a production equipment 31 disposed on a manufacturing line L1 or retrofitted to be disposed in a vicinity of the production equipment 31, and which outputs a detection signal indicating an operating state of the production equipment 31, and includes a transmitter 22a that transmits the detection signal. The production management apparatus 10 includes a generator for generating information on production state of the manufacturing line L1 by use of the detection signal received from the operating state acquisition apparatus 20a, and includes a display device for displaying the generated information on production state.

A method and a computer program for discovering possible errors in a production process for manufacturing metal products. The method involves removing at least one process parameter value from the cluster or adding at least one process parameter to the cluster. Second Z-score values, which are compared with the first Z-score values, are then determined for the thus-altered cluster. The changes in the Z-score values provide suggestions for troubleshooting and process optimization which are direct and can be implemented immediately.

Methods, systems, and devices for analyzing vibration data and for identifying and tracking vibration anomalies in industrial machines are described. In various embodiments, the system described herein collects, transforms, and analyzes sensor data from one or more machines, such as industrial machines. The system may identify one or more sensors that are experiencing vibrational anomalies. In various embodiments, the system: collects and analyzes vibration data for a set of one or more vibration-related sensors of one or more industrial machines; determines an occurrence of one or more anomalies based on the vibration data as compared to a threshold; tracks anomalies in collected vibration data for the one or more industrial machines of the facility; generates a report of vibration data for the one or more vibration-related sensors of the facility; and reports industrial machines in the facility that may deviate from a target performance.