There are provided a repair determination section that determines, based on failure information on the facility for manufacturing a product, a repair time required to repair the facility, and a recovery plan creation section that creates a recovery plan in accordance with a predetermined production evaluation indicator, based on the repair time and production information on a line with the failed facility, one or more other facilities, and on one or more other lines.

In example implementations described herein, there are systems and methods for processing sensor data from an equipment over a period of time to generate sensor time series data; processing the sensor time series data in a kernel weight layer configured to generate weights to weigh the sensor time series data; providing the weighted sensor time series data to fully connected layers configured to conduct a correlation on the weighted sensor time series data with predictive maintenance labels to generate an intermediate predictive maintenance label; and providing the intermediate predictive maintenance label to an inversed kernel weight layer configured to inverse the weights generated by the kernel weight layer, to generate a predictive maintenance label for the equipment.

An Industrial Internet of Things (IIoT) agent module or device preferably used as or in place of a Supervisory Control and Data Acquisition (SCADA) data node system and/or conventional SCADA system, which is operatively coupled and in communication with an IIoT cloud platform, so as to perform control and data acquisition operations and exchange data and commands automatically or in response to Inputs from the IIoT cloud platform; wherein all production details/settings/parameters, including process logic, control methodology, product recipe, and data point setup, can be dynamically changed based on decision/input/command of the IIoT cloud platform, such that the IIoT cloud platform might completely “re-configure/re-program” a software portion of the IIoT agent module or device governing its working behavior or characteristics by sending a reconfiguration/reprogramming information.

A technique involves modular storage of network service plan components and provisioning of same. A subset of the capabilities of a service design system can be granted to a sandbox system to enable customization of service plan offerings or other controls.

Systems for self-organizing data collection and storage in a refining environment are disclosed. An example system may include a swarm of mobile data collectors structured to interpret a plurality of sensor inputs from sensors in the refining environment, wherein the plurality of sensor inputs is configured to sense at least one of: an operational mode, a fault mode, a maintenance mode, or a health status of a plurality of refining system components disposed in the refining environment, and wherein the plurality of refining system components is structured to contribute, in part, to refining of a product. The self-organizing system organizes a swarm of mobile data collectors to collect data from the system components, and at least one of a storage operation of the data, a data collection operation of the sensors, or a selection operation of the plurality of sensor inputs.

A communication module (100) for monitoring a machine (200) includes a serial interface (110) connectable to a serial interface of the machine (200); a telecommunications interface (120) connectable to a telecommunications network; and a control unit (130) configured to transmit data received via the serial interface (110) to a monitoring server (300) via the telecommunications interface (120). The module (100) provides network connectivity to industrial machinery, thereby providing a means of remotely monitoring such machines, including legacy machines which may not otherwise support network connectivity.

Methods and systems for sensor fusion in a production line environment are disclosed. An example system for data collection in an industrial production environment may include an industrial production system comprising a plurality of components, and a plurality of sensors each operatively coupled to at least one of the components; a sensor communication circuit to interpret a plurality of sensor data values in response to a sensed parameter group; and a data analysis circuit to detect an operating condition of the industrial production system based at least in part on a portion of the sensor data values; and a response circuit to modify a production related operating parameter of the industrial production system in response to the detected operating condition.

A computer-implemented method of generating a training data set for training an artificial intelligence module includes providing first and second data sets, the first data set including first data elements indicative of a first operational condition, the second data set including second data elements indicative of a second operational condition that matches the first operational condition. The method further comprises determining a data transformation for transforming the first data elements into the second data elements; applying the data transformation to the first data elements and/or to further data elements of further data sets, thereby generating a transformed data set; and generating a training data set for training the AI module based on at least a part of the transformed data set.

Systems and methods for implementing one or more compiled workflows at a biological foundry are provided. A representation of an uncompiled workflow to produce engineering targets is obtained. The representation is translated into a first corresponding instance of a compiled workflow. If the translation satisfaction of various threshold translation criteria is determined, and the first corresponding instance of the compiled workflow is executed to complete a first portion of manufacture of the engineering targets. If the executing satisfaction of various threshold execution criteria is determined, the representation is translated into a second corresponding instance of the compiled workflow different from the first corresponding instance. If this translation satisfaction of the various threshold translation criteria is determined, the second corresponding instance of the compiled workflow is executed to complete a second portion of the manufacture of the engineering targets.

A method of configuring robot fleets with additive manufacturing capabilities includes receiving a request for a robotic fleet to perform a job and determining a job definition data structure based on the request. The job definition data structure defines a set of tasks to be performed in furtherance of the job. The method includes determining a provisioning configuration for each additive manufacturing system based on the task to which the additive manufacturing system is assigned, the set of 3D printing requirements, the printing instructions, and the status of the additive manufacturing system. The method includes provisioning the additive manufacturing system based on the provisioning configuration and a set of additive manufacturing system provisioning rules that are accessible to an intelligence layer to ensure that provisioned systems comply with the provisioning rules. The method includes deploying the robotic fleet based on the robotic fleet configuration data structure to perform the job.