A synchronizing device includes: a machine data acquisition portion which acquires at least one type of machine data related to operation of a machine in a time series based on first time information; a measurement data acquisition portion which acquires at least one type of measurement data measuring the state of the machine in a time series based on second time information; a first extraction portion which extracts, from any of the machine data, a moment at which a feature set in advance indicating a predetermined event is expressed; a second extraction portion which extracts, from any of the measurement data, a moment at which a feature set in advance indicating the predetermined event is expressed; and an output portion which synchronizes a moment extracted by the first extraction portion and a moment extracted by the second extraction portion, and outputs the machine data and the measurement data.

A system is provided for measurement data management in a distributed environment. The system comprises at least one storage system adapted to obtain raw measurement data or intermediate results from at least one measurement site via a network. In addition, the system further comprises a database, operatively connected to the said storage system, adapted to be accessed remotely by the measurement site via the network. The storage system or the measurement site is further adapted to perform successive processing steps on the raw measurement data along a process chain in order to generate measurement results, whereby associating metadata with the raw measurement data and with the measurement results. In this context, the metadata associated with each measurement result of the successive processing steps is provided with a new reference as well as a reference to the reference of the measurement result from the preceding processing step.

Disclosed is a method of predictively maintaining equipment by means of a distribution map. The method can: extract a peak value based on a change in the amount of energy required for the equipment to perform a working process in a normal state; generate the distribution map based on the extracted peak value; and predictively detect, in advance, abnormalities of the equipment on the basis of a change in a distribution probability of a detection section having a low distribution probability and a somewhat high risk in the generated distribution map, so as to induce maintenance and replacement of the equipment to be carried out in a timely manner. Thus, enormous financial losses due to equipment failure may be prevented.

A method for predictive maintenance of equipment via a distribution chart is disclosed. Peak values are extracted based on a change in an amount of energy required for performing a work process by the equipment in a normal state, a distribution chart of the extracted peak values is constructed, and an abnormal symptom of the equipment is predictively detected in advance based on a change in distribution probability of a detection section having a low distribution probability and somewhat high risk in the constructed distribution chart thereof such that maintenance and replacement of the equipment are induced to be carried out at an appropriate time. Thus, an enormous monetary loss caused by a failure in the equipment may be prevented in advance.

A data processing apparatus includes a processor. The processor generates visualization data for displaying estimation results of manufacturing conditions based on estimation results and relationship data. The relationship data includes first relationship data as a relationship between first manufacturing conditions recorded during an analysis, and second relationship data as a relationship between second manufacturing conditions corresponding. The processor divides the estimation results of the manufacturing conditions into a first group based on the first relationship data, and into a second group based on the second relationship data. The processor generates the visualization data based on a change in manufacturing condition relationship between the first group and the second group.

A method and device for predicting an anomaly in a manufacturing process. The method includes receiving time-series equipment data including one or both of sensor data and specification data, converting the time-series equipment data into an image, dividing the image into a plurality of patch images, outputting a probability for each class associated with a sign of an anomaly in the time-series equipment data by inputting the plurality of patch images to a pretrained artificial neural network (ANN), and predicting the sign of the anomaly in the time-series equipment data by adjusting a probability weight for each class based on a preset standard.

When an event such as maintenance occurs, a model in which a tendency of input data changes due to an influence of the maintenance and retraining is required is specified. Model identifiers that specify the machine learning models and are unique to the machine learning models and sensor identifiers that specify the sensors that output the sensor data serving as the input data of the machine learning models and are unique to the sensors are managed to be in association with each other. A degree of influence indicating a change in tendency of the sensor data before and after the maintenance operation is performed is obtained for each maintenance event identifier that specifies a maintenance operation performed on a device and is unique to the maintenance operation, and the degree of influence is managed in association with each of the sensor identifiers. When the sensor whose degree of influence satisfies a predetermined condition is influenced by the maintenance operation, the model identifier associated with the sensor identifier of the sensor satisfying the condition is presented.

A plant monitoring device (20) is provided with: a detection value acquisition unit (211) that acquires a bundle of detection values; a first Mahalanobis distance calculation unit (212) that calculates a first Mahalanobis distance; a plant state determination unit (213) that determines whether the operation state of a plant is normal or abnormal; a cause detection value estimation unit (214) that estimates a cause detection value which represents a cause of the abnormality of the plant; a second Mahalanobis distance calculation unit (215) that calculates a second Mahalanobis distance by increasing or decreasing the detection value estimated as the cause detection value; and an identification unit (216) that identifies whether the abnormality can be relieved by increasing or decreasing the detection value estimated as the cause detection value.

A method for diagnosing the dynamics of a sensor in the fresh air or exhaust gas tract of internal combustion engines, as well as a computer program, a computer program product and a corresponding vehicle. It is provided that a corresponding sensor for measuring a pressure signal in the fresh air or exhaust gas tract of an internal combustion engine of a vehicle, which is coupled with a control and computing unit, is checked with respect to its dynamic characteristics. Measured values of differing frequency ranges are measured, these being used as the basis for a calculation of a characteristic. After the calculation, the characteristic is compared with a threshold value stored in a user-defined manner in the control and computing unit. If the calculated characteristic deviates from the threshold value, at least one triggering signal is triggered, so that an impaired sensor dynamic of the sensor is displayed.

A method including receiving a data packet over a network, the data packet having a size. The method also includes parsing the data packet into a header and a body. The method also includes identifying a protocol type from the header and the size. The method also includes identifying a signal characteristic of signal data in the body. The method also includes identifying a classification of a source sensor which generated the data packet based on the protocol type and the signal characteristic. The method also includes generating a metadata file based on the source sensor. The method also includes labeling the data packet with the metadata file to form a labeled data packet.