Example implementations described herein involve systems and methods that can involve extracting features from each of a plurality of time-series sensor data, the plurality of time-series sensor data associated with execution of one or more operations; clustering the extracted features into a plurality of tasks that occur from execution of the one or more operations, each of the plurality of tasks associated with a clustering identifier (ID) from the clustering; and calculating a cycle time of the cycle based on the initiation and end of the cycle recognized by referencing a cycle pattern model, wherein the cycle pattern model comprises configuration information of a cycle including a set from a plurality of the clustering IDs.

An industrial monitoring system is disclosed. In an embodiment it includes a device layer, a device driving layer, a data sharing layer, a data routing layer, a data scheduling layer, a service layer, and a presentation layer. The data sharing layer, the data routing layer, and the data scheduling layer are organized by using a non-relational database, or organized in a hybrid way by using the non-relational database and a relational database. The industrial monitoring system disclosed can implement processing such as real-time storage and sharing of a large amount of data, and data mining and retrieval.

A software and hardware implemented solution that enables manufacturing organizations to manage knowledge, manage information, comply with regulations, and operate regulated manufacturing in a more efficient and technically innovative way.

A production system includes at least one production machine, a machine management device, and a production management device. The production machine transmits cumulative total use time data to the machine management device. The production management device has a transfer part which transfers scheduled production number information, recommended inspection time information, and inspection reservation information. The machine management device has a determination part which determines, based on the cumulative total use time data of each production machine and the recommended inspection time information, whether an inspection is necessary within a predetermined period of time, and an extraction part which extracts a predetermined number of dates from the extracted reservable dates, in order of smallest to largest scheduled production number, based on the inspection reservation information and the scheduled production number information.

Techniques for determining device-specific information such as commissioning data, location information, images, and other data descriptive of a process device installed in a plant include obtaining the device-specific information at a local device during the process device's commissioning. Based on this information, the local device determines the relative order of the process device within a process flow, and may determine a process element alignment map indicating the activation order of a plurality of process elements within the flow. A user may modify the map at the local device. The map is transmitted to a process control big data network for use in discovery and learning analytics. The device-specific information and/or the map may be utilized to generate, at the local device, representations/views of the process flow, which may include real-time operational data. A user may zoom in or out on these views for more or less detail.

A system for monitoring and analyzing data in a distributed process control system is provided. The system includes a user interface having a set of user controls for selecting and configuring data blocks to create a data diagram representing a data model. The data blocks are associated with data operations, such as data analytics functions, and may be configured by the user for particular instances of general blocks. The data blocks are interconnected by wires conveying outputs or inputs of the blocks, which may also connect data sources to the data blocks. The data sources may include on-line data (i.e., data streams) or off-line data (i.e., stored data) from the process control system. Additional user controls may be used to evaluate the data diagram or convert the data diagram from an off-line to an on-line version.

A work process management system includes at least one work device and a tag type individual controller that is directly or indirectly attached to a work object to control the work device. Each of the work device includes a work-device-side communicator, a work part, and a work-device-side control part. The tag type individual controller includes an individual-controller-side storage in which a work content of a work process performed with the work device is stored, an individual-controller-side communicator, and an individual-controller-side calculation controller that transmits the work content of the work process performed with the work device in which a performance result is reflected to the work-device-side communicator, and additionally stores the received performance result in the individual-controller-side storage.

Techniques for determining device-specific information such as commissioning data, location information, images, and other data descriptive of a process device installed in a plant include obtaining the device-specific information at a local device during the process device's commissioning. Based on this information, the local device determines the relative order of the process device within a process flow, and may determine a process element alignment map indicating the activation order of a plurality of process elements within the flow. A user may modify the map at the local device. The map is transmitted to a process control big data network for use in discovery and learning analytics. The device-specific information and/or the map may be utilized to generate, at the local device, representations/views of the process flow, which may include real-time operational data. A user may zoom in or out on these views for more or less detail.

A system for providing access to locally stored process image data to other devices in an industrial production environment includes a plurality of controller devices and a process image backbone. Each respective controller device comprises the following: a volatile computer-readable storage medium comprising a process image area; a non-volatile computer-readable storage medium; a control program configured to provide operating instructions to a production unit; an input/output component configured to update the process image area during each scan cycle or upon the occurrence of one or more events with process image data items associated with the production unit; and a historian component configured to locally store the process image data items of the process image area as time series data in the non-volatile computer-readable storage medium. The process image backbone provides the plurality of controllers with uniform access to the process image data items of each programmable logic device.

A method for operating a plurality of production plants by a manufacturing execution system (MES), includes providing central computational hardware as a regional hub running MES software configured to manage production processes throughout the plants, configuring a first MES for a first single plant connected to the regional hub, creating for each further plant the respective manufacturing executions system by inheriting all configurations of the first MES to the further MESs, and configuring the additional plants over time in the existing solution in the regional hub without reinstalling software of the MES and without reapplying some configurations already performed in the first plant. In multi-plant solutions, the TCO is decreased. The same number of servers hosting the MES/MOM solution serving one plant, manages multiple plants. The set of servers constitutes a single MES/MONM solution distributed environment hosted by the same regional hub or single virtual datacenter for Cloud providers.