A low toughness workpiece cutting apparatus, a low toughness workpiece manufacturing method and a low toughness workpiece manufacturing program for predicting an occurrence of defect and/or non-occurrence of defect before a cutting process of low toughness material. A defect prediction device is provided with a storage device, a processor and an interface. The storage device stores tool data that represent physical characteristics and a shape of a tool, cutting data that represent a group of parameters of a cutting process to be performed to a workpiece by use of the tool and material data that represent physical characteristics and a shape of the workpiece. The processor performs an analysis of deformation of the workpiece due to a cutting force and an analysis of fracture due to the deformation, and performs a prediction of an occurrence of defect and/or a non-occurrence of defect of the workpiece due to the cutting process.

A computer implemented method for the maintenance optimization of a fleet or group of turbomachinery assets is disclosed. The method comprises the step of model training and setup, aiming at setting configurations parameters, that can be executed offline, and the step of online calculation on new input data, which is based on detected data and extracted statistical features. An anomaly identification and classification follow, thus calculating a risk assessment, for estimating the risk that an anomaly might cause any event that requires a maintenance task to be executed on one or more assets of the fleet.

A digital twin model based plant operation and optimization is provided. Systems and methods of the solution can receive data on assets of a plant, the assets' topology, connectivity and flow and deployed physical instruments along with the measurements of the physical instruments. The solution generate a model having a plurality of layers based on the received data and provide one or more virtual instruments in the model. The solution can determine, based on a set of relationships for interactions between assets and the received measurements input into the model, one or more virtual measurement for the one or more virtual instruments and generate, responsive to a comparison with a threshold, a notification to service at least one of the assets at the plant.

A manufacturing management device includes first cell planning device connected to the manufacturing machine of first manufacturing cell, and second cell planning device connected to the manufacturing machine of second manufacturing cell. The planning part of the first cell planning device calculates quantity of operation that would be left unfinished with respect to the quantity stipulated in a manufacturing plan. The first cell planning device transmits a request for transfer including information on the quantity of operation that would be left unfinished to the second cell planning device. The second cell planning device modifies at least one of the operation period and the period of maintenance work of the manufacturing machine in the second manufacturing cell so as to increase the quantity of operation during the predetermined period.

Provided is an embedding analysis-based facility predictive maintenance method performed by a server, including (a) collecting time-series operation data of at least one machine; (b) deriving abnormal state information to determine whether the collected time-series operation data deviates from a time-series threshold, deriving an embedding result pattern through embedding analysis on the collected time-series operation data, and mapping the embedding result pattern and the abnormal state information; (c) building an abnormal pattern analysis model by performing machine learning on each mapped information and analyzing whether the embedding result pattern indicates abnormality or normality; and (d) when new time-series operation data is collected, applying the new time-series operation data to the abnormal pattern analysis model to derive current state information and future prediction state information.

A system for monitoring maintenance for manufacturing apparatuses in a facility is provided. The system includes at least one manufacturing apparatus, the manufacturing apparatus including an RF identification tag and a module, and a control system. The control system is configured to receive, from the module, operational status information that indicates time periods during which the manufacturing apparatus is in operation, determine a cumulative time duration of operation for the manufacturing apparatus, retrieve a predetermined maintenance interval for the manufacturing apparatus, determine whether the cumulative time duration of operation exceeds the predetermined maintenance interval, and in response to a determination that the cumulative time duration of operation exceeds the predetermined maintenance interval, i) output a signal that causes the manufacturing apparatus to discontinue operation, and ii) cause instructions for completing a required maintenance procedure to be displayed on a display device in communication with the control system.

A plurality of basic simulations independent of one another are carried out, which determine respective remaining service life predictions for the machine. The remaining service life predictions and characteristic data are fed to a neural network, which outputs weights for the remaining service life predictions. A final prediction is calculated from the remaining service life predictions by weighting the remaining service life predictions relative to one another. A hybrid model is produced, which results from the combination of the basic simulations with the neural network. The remaining service life can be predicted not only for a small number of machines for which a specific simulation model has been manually created. The hybrid model enables condition monitoring for any further types and configurations of machines that merely belong to the same machine class. The basic simulations can therefore also be applied to previously unknown machines.

Provided is information to determine whether a maintenance process should be performed on a plant. Provided is an analysis apparatus, including: an operation value analysis unit configured to analyze operation values including a first operation value when a maintenance process is performed on an analysis target plant and a second operation value when the maintenance process is not performed on the analysis target plant, each of the operation values depending on at least any of an amount or quality of deliverables produced or emissions emitted by the analysis target plant under a designated condition; and a value comparison unit configured to compare the first operation value to the second operation value.

A multi-platform industrial search system facilitates indexing and searching of plant-wide data residing on multiple different data platforms. The industrial search system automatically inventories industrial devices and other data sources located throughout a plant, and identifies available data items on each data source. The search system indexes the discovered data items in a federated data model that can subsequently be searched to locate data items or tags of interest. The federated data model records references to data items found on different types of data sources, including but not limited to industrial controller programs, human-machine interface applications, data historians, device documentation stores, inventory tracking systems, and other such data sources. The search system can also initiate automated searches of the federated data model in response to detected performance issues and deliver notifications and contextual information to plant personnel.

Catalysts used for catalytic reforming are treated with organic chloride to condition the catalysts. Chloride treaters may be located in the product streams to remove the chloride contaminants. The continuous catalyst reforming process, including the catalyst reformer unit and chloride treaters, may be monitored in order to predict when adsorbent replacement or regeneration is needed. For example, one or more sensors and measurement devices may be used to monitor certain conditions or parameters. A system may be configured to take one or more actions in response to certain conditions or parameters being met.