A method for identifying imbalances in facility operations. The method includes determining an existence of an imbalance between a first volume of a component at a first point in the facility and a second volume of the component at a second point in the facility, calculating a probability likelihood of the imbalance being caused by one or more likely errors of a set of known likely errors, determining an impact magnitude on the imbalance of each of the one or more likely errors, and assigning an importance ranking to each of the one or more likely errors based on the probability likelihood and the impact magnitude.

The information processing device includes a matching unit 32A and a display control unit 33A. The matching unit 32A is configured to match a database 21A, which associates first detection data indicative of a past state of maintenance target equipment 3 with first maintenance information relating to maintenance of the maintenance target equipment 3 in the past state, with second detection data indicative of a current state of the maintenance target equipment 3. The display control unit 33A is configured to display, based on a result of the matching by the matching unit 32A, second maintenance information relating to maintenance in accordance with the current state of the maintenance target equipment 3 on a display unit 45A.

A diagnosis device stores a model used for diagnosing the condition of an industrial machine in a storage unit, acquires data related to the condition of the industrial machine, and based on the acquired data, determines the condition of the industrial machine by using the model stored in the storage unit. Then, in response to detecting that a component of the industrial machine has been replaced based on the acquired data and the data related to the determined condition of the industrial machine, the diagnosis device adapts the model stored in the storage unit to the condition of the industrial machine whose component has been replaced.

The present invention provides a data fusion and reconstruction method for fine chemical industry safety production based on a virtual knowledge graph. In view of the characteristics of fine chemical industry safety production data, such as a large amount of structured data, a multi-source heterogeneous database and a strong sequential logic, the present invention innovatively proposes a method of using a virtual knowledge graph to complete the fusion and reconstruction of a traditional database for fine chemical industry. The present invention fuses static structured knowledge in the field of fine chemical industry with a real-time dynamic database for chemical industry safety production in the concept of ontologies for the first time to organize time series data in the form of entities. In addition, the mapping rules of the existing OBDA system are improved based on a data set of the present invention.

A method for learning and detecting an abnormal part of a device through artificial intelligence comprises: an information collection step for collecting a current waveform of a current value that changes over time in a driving state of at least one device and collecting information about a faulty part of the device, together with current waveform information before a fault occurs in the device; a model setting step for learning, by a control unit, information collected in the information collection step and setting a reference model of a current waveform for each faulty part of the device; and a detection step for, when an abnormal symptom of the device is detected in a real-time driving state, comparing, by the control unit, a real-time current waveform of the device and the reference model, and detecting and providing an abnormal part regarding the abnormal symptom of the device.

An equipment state monitoring device includes: a feature amount extracting unit to extract a feature amount of operation data in which a state of equipment is measured; an operation pattern determining unit to determine whether an operation pattern of the equipment when the operation data is measured is a learned pattern in which a determination range of a state of the equipment is learned or an unlearned pattern; a feature amount correcting unit to correct the feature amount of the operation data corresponding to the operation pattern determined as the unlearned pattern to correspond to the learned pattern on a basis of a relationship between an operation pattern of the equipment and a feature amount of operation data; and an equipment state determining unit to determine a state of the equipment on a basis of the corrected feature amount and a determination range of a state of the equipment.

Provided are an abnormality diagnosis system and an abnormality diagnosis method that can prevent wrongly diagnosing equipment as having an abnormality when the equipment actually does not have an abnormality. An abnormality diagnosis system 20 comprises a sampler 21 and a calculator 24. The calculator 24 is configured to: perform first abnormality determination of whether there is an abnormality based on a result of first principal component analysis; in the case where a result of the first abnormality determination is that there is an abnormality, and perform second abnormality determination of whether there is an abnormality based on a result of second principal component analysis; and in the case where a result of the second abnormality determination is that there is an abnormality, diagnose the equipment as having an abnormality.

Various embodiments of the teachings herein include an integrity monitoring system for runtime integrity monitoring of a control device connected to sensors and/or actuators and comprising an automation device for collecting operating state data of the control device. The system may include an integrity monitoring unit detachably connectable directly to the control device to monitor the integrity status of the control device on the basis of operating state data transferred from the automation device to the integrity monitoring unit.

The disclosed embodiments relate to a system that automatically adapts a prognostic-surveillance system to account for aging phenomena in a monitored system. During operation, the prognostic-surveillance system is operated in a surveillance mode, wherein a trained inferential model is used to analyze time-series signals from the monitored system to detect incipient anomalies. During the surveillance mode, the system periodically calculates a reward/cost metric associated with updating the trained inferential model. When the reward/cost metric exceeds a threshold, the system swaps the trained inferential model with an updated inferential model, which is trained to account for aging phenomena in the monitored system.

An abnormal irregularity cause identifying device includes a process data acquisition unit that reads process data output by sensors included in a production facility performing a batch stage and a continuous stage, a preprocessing unit that associates a range of a complete timing of the batch stage with an output timing of process data of the process data in the continuous stage based on a residence time of the processing target in the production facility, an abnormality determination unit that calculates an abnormality degree by using process data in the batch stage and process data in the continuous stage associated with each other by the preprocessing unit, and a cause diagnosis unit that determines, for each of the process data output by the corresponding one of the plurality of sensors, whether the abnormality degree calculated by the abnormality determination unit satisfies a predetermined criterion.