The present invention discloses a model-free online recursive optimization method for a batch process based on variable period decomposition. Variable operation data closely related to product quality is acquired, optimization action on each subset is integrated on the basis of time domain variable division on the process by utilizing a data driving method and a global optimization strategy is formed, based on which an online recursive error correction optimization strategy is implemented. According to the method, the online optimization strategy is formed completely based on the operation data of the batch process without needing prior knowledge or a model of a process mechanism. Meanwhile, the optimized operation locus line has better adaptability by using the online recursive correction strategy, and thus the anti-interference requirement of the actual industrial production is better met.

Monitoring a processing asset for one of an oil processing facility and a gas processing facility includes a data acquisition circuit structured to interpret a plurality of detection values corresponding to input received from a detection package, the detection package comprising at least one of a plurality of input sensors, each of the plurality of input sensors operatively coupled to at least one of a plurality of process components; a data analysis circuit structured to analyze a subset of the plurality of detection values to determine a status parameter; and an analysis response circuit structured to adjust a process utilizing the processing asset in response to the status parameter comprising altering at least one process parameter selected from the process parameters consisting of: a temperature, an operating speed, a utilization value of one of the plurality of process components, and a process flow.

The present disclosure includes a method for receiving, by the processing system, reporting packets from one or more respective sensors of the plurality of sensors. Each reporting packet is sent from a respective sensor and indicates sensor data captured by the respective sensor; performing, by the processing system, one or more edge operations on one or more instances of sensor data received in the reporting packets. Generating one or more sensor kit packets based on the instances of sensor data. Each sensor kit packet includes at least one instance of sensor data. Outputting the sensor kit packets to the data handling platform. Receiving the sensor kit packets from the edge device. Generating the digital twin of said industrial setting including a digital replica of at least one industrial component of said industrial setting and being at least partially based on the sensor kit packets.

Methods, system and apparatus for monitoring vibration sensitive industrial equipment is disclosed. The system may include a data acquisition circuit structured to interpret a plurality of detection values, each of the plurality of detection values corresponding to input received from at least one of a plurality of input sensors, each of the plurality of input sensors operatively coupled to at least one of a plurality of components of the equipment, a signal conditioning circuit structured to process a subset of the detection values on multiples of a key frequency, a vibration analysis circuit structured to identify vibration in at least one of the plurality of components, a data analysis circuit structured to analyze the plurality of detection values and determine a status parameter value, and an analysis response circuit structured to take an action in response to the status parameter value.

The present disclosure describes systems for data collection in an industrial environment having a self-sufficient data acquisition box for capturing and analyzing data in an industrial process. A system can include a data circuit for analyzing a plurality of sensor inputs, a network control circuit for sending and receiving information related to sensor inputs to an external system, wherein the system provides sensor data to one or more similarly configured systems, and wherein the data circuit dynamically nominates a similarly configured system capable of providing sensor data to replace the system.

An industrial machine predictive maintenance system may include an industrial machine data analysis facility that generates streams of industrial machine health monitoring data by applying machine learning to data representative of conditions of portions of industrial machines received via a data collection network. The system can utilize a distributed ledger to track one or more transactions executed in an automated data marketplace for industrial Internet of Things data. The distributed ledger distributes storage for data indicative of the one or more transactions across one or more devices, wherein the data indicative of the one or more transactions corresponds to transaction records. A transaction record stored in the distributed ledger represents one or more of sensor data, the condition of an industrial machine, orders or the requests for service and parts, an issue associated with the condition of a machine, or a hash used to identify the transaction record.

Systems and methods for detecting operating characteristics of an industrial machine are disclosed. The detecting can include generating one or more image data sets using raw data captured by one or more data capture devices and identifying one or more values corresponding to a portion of the industrial machine within a point of interest represented by the one or more image data sets. The one or more values can be compared to corresponding predicted values and a variance data set can be generated based on the comparison of the one or more values and the corresponding predicted values. An operating characteristic of the industrial machine can be identified based on the variance data and data indicating a detection of the operating characteristic can be generated.

An industrial machine predictive maintenance system may include an industrial machine data analysis facility that generates streams of industrial machine health monitoring data by applying machine learning to data representative of conditions of portions of industrial machines received via a data collection network. The system may include an industrial machine predictive maintenance facility that produces industrial machine service recommendations responsive to the health monitoring data by applying machine fault detection and classification algorithms thereto. The system detects an operating characteristic of an industrial machine, such as vibration, using one or more sensors of a mobile data collector and identify, as a condition of the industrial machine, a characteristic for the industrial machine within the knowledge base. The system can determine severity of the condition and predict and execute a maintenance action to perform against the industrial machine based on the severity of the condition.

A system and method for causing a mobile data collector to perform a maintenance action on an industrial machine are disclosed. The mobile data collector can be deployed for detecting and monitoring vibration activity of a portion of an industrial machine. The mobile data collector can be controlled to approach a location of the industrial machine such that a vibration sensor of the mobile data collector can record a measurement of the vibration activity, which can be transmitted as vibration data to a server over a network. The server can determine a severity of the vibration activity and predict a maintenance action to perform. A signal indicative of the maintenance action can be transmitted to the mobile data collector to cause the mobile data collector to perform the maintenance action. A record of the predicted maintenance action can be stored within a ledger associated with the industrial machine.

An integrated platform system controlled with hardware and software for just-in-time, local manufacturing is disclosed. System hardware may include a fermentation system, a cross-flow system, a disposable formulation system, a robotic fill-finish system, and a quality control test and release system.