Systems and methods for boiler regulation are disclosed. The system can receive boiler data from a boiler and compare the boiler data to a normal operating range to detect an abnormality. Based on a plurality of rules, the system can identify an anticipated root cause and at least one corrective action. Based on the at least one corrective action, the system can generate and/or output instructions for the boiler to perform the at least one corrective action. The system can display an indication of the abnormality and/or the at least one corrective action.

Apparatus and associated methods relate to a vibrational sensing system (VSS) including an accelerometer and a data processor, which determines an “operational state” of a mechanical drive unit, the processor further employing the “operational state” to gate learning of long-term vibrational data to exclude collection of non-operational data, the long-term data collected to calculate alarm thresholds. For example, vibrations from a target motor are sensed by a coupled accelerometer. Vibrational data from the accelerometer is fed into a data processor which determines the operational state of the motor. The operational state (e.g., on/off indication) may gate data collection such that data is only acquired during on-time, which may advantageously create accurate baselines from which alarm thresholds may be generated, and nuisance alarms may be avoided.

A device configured to monitor a vibrating object, the device comprising a common housing holding an accelerometer for sampling vibration signatures of the vibrating object, resulting in vibration samples, and a computing device comprising a data processor and a memory having stored thereon a computer program product for monitoring the vibrating object, the computer program product comprising an input module to receive the vibration samples, an analysis module to analyze the vibration samples to derive asset health scores, a machine learning model to determine asset operating ranges, and an output module to output messages, wherein the computer program product when running on the data processor causes the computing device to receive during a time interval, having an end time t1, the vibration samples from the accelerometer, resulting in a time series vector array, and to analyze the vibration samples comprising deriving from the time series vector array a baseline asset health score and deriving from the time series vector array a time series asset health score, and to subject at least a part of the time series asset health score to the machine learning model for determining at least one asset operating range, and to receive a further vibration sample at a monitor time t2 wherein the monitor time t2 is subsequent to the end time t1, and to derive from the further vibration sample an asset health score, and to determine if the asset health score falls within an operating range determined by the machine learning model, resulting in a monitor result, and to output a message depending on the monitor result, and wherein the device consumes less current than 20 mA.

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.

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.

In a device engineering method, data of a plant network hierarchy (PNH) and registered devices (RD) in a device management server are synchronized to a device simulation server (SS). In a simulation mode, at least one function of device management system is executed via communicating with simulated devices (SD), while the device management system being not communicatively coupled to any physical control station configured to control physical field devices in a real plant. Virtual parameters are introduced into the SD. For device configuration, simulation is made of configurable device parameters; non-configurable device parameters; and device status, with SD generated from DD files in the PNH for the RD in the FDCS. Parallel communications including sending communication requests from a CRH component to the SD in the PNH from the CRH component in the FDCS to the SD in the PNH are executed simultaneously.

Parameter settings and operational data are received from machines for a current predefined time interval. For each machine, a corresponding health metric value is calculated based on the received operational data and machine health data, and stored in association with the received corresponding parameter settings. Associated unknown health metric values are estimated for machines associated with combinations of parameter settings different from the received parameter settings having at least one of the combinations of parameter settings with an associated previously determined health metric value, and at least one other of the combinations of parameter settings with the associated unknown health metric value, based on the corresponding calculated health metric value and the corresponding previously determined health metric value. Associated parameter settings for at least one healthiest machine and at least one least healthy machine are determined based on the stored health metric values and are automatically adjusted.

Provided is a cultivation assistance system including a cultivation condition acquisition unit configured to acquire a cultivation condition under which a plant is cultivated, a trouble acquisition unit configured to acquire a trouble occurrence situation in cultivation of the plant, a model generation unit configured to generate, by using the cultivation condition and the trouble occurrence situation, a model for predicting one of a cultivation condition or a trouble from the other, and an estimation unit configured to estimate, by using the model, a cultivation condition for suppressing occurrence of a trouble in cultivation of the plant. The cultivation assistance system includes a preprocessing unit to perform preprocessing on data of at least one of the cultivation condition or the trouble occurrence situation. The model generation unit is to generate, by using the preprocessed data, a model for predicting one of the cultivation condition or the trouble from the other.

A platform for updating one or more properties of one or more digital twins including receiving a request for one or more digital twins; retrieving the one or more digital twins required to fulfill the request from a digital twin datastore; retrieving one or more dynamic models corresponding to one or more properties that are depicted in the one or more digital twins indicated by the request; selecting data sources from a set of available data sources based on the one or more inputs of the one or more dynamic models; obtaining data from selected data sources; determining one or more outputs using the retrieved data as one or more inputs to the one or more dynamic models; and updating the one or more properties of the one or more digital twins based on the one or more outputs of the one or more dynamic models.

Systems and methods for production line optimization include: receiving, from each of a plurality of equipment along the production line, equipment data at each interval of a plurality of intervals, the equipment data including equipment speed data, equipment state data, and equipment fault data; analyzing the equipment data with one or more optimization rules to identify an optimization setting; and, outputting the optimization setting to a production line device for modification of the production line.