Systems and methods for data collection for an industrial heating process are disclosed. The system according to one embodiment can include a plurality of data collectors, including a swarm of self-organized data collector members, wherein the swarm of self-organized data collector members organize to enhance data collection based on at least one of capabilities and conditions of the data collector members of the swarm, and wherein the plurality of data collectors is coupled to a plurality of input channels for acquiring collected data relating to the industrial heating process, and a data acquisition and analysis circuit for receiving the collected data via the plurality of input channels and structured to analyze the received collected data using a neural network to monitor a plurality of conditions relating to the industrial heating process.

The present disclosure relates to a method and a system for determining slip status of a drill string. The monitoring and control system is configured to obtain a hook load data at predetermined time intervals, and determine variation of hook load between the predetermined time intervals using the hook load data obtained. The monitoring and control system further determines a slip status of the drill string corresponding to each of the variation of hook load. The slip status is determined by comparing each of the variation of hook load with a threshold value of noise. The threshold value of noise is determined based on predetermined parameters of the drill string. The method and system of present disclosure accurately determines the slip status of the drill string, thereby improves operational efficiency of the drill rig.

An operation mode specifying unit specifies an operation mode of a machine by comparing time-series data of an amplitude and a frequency of measurement data obtained from a sensor with definition data of the operation mode of the machine created in advance by an operation mode data creation unit. In addition, an abnormality diagnosis unit performs processing of cluster analysis for the measurement data obtained from the sensor or the like, and diagnoses abnormality of the machine according to diagnosis procedure information that is set in advance depending on the set operation mode and an abnormality mode of the machine.

An apparatus comprises an electronic equipment chassis comprising a housing and at least one lid, the housing comprising a control panel with a first set of one or more status indicators. The apparatus also comprises at least one latch configured for securing the at least one lid to the housing, the at least one latch comprising a second set of one or more status indicators. The apparatus further comprises a processing device configured to determine status information for electronic equipment housed in the electronic equipment chassis, the status information characterizing whether at least one of opening and removing the at least one lid is safe to perform at a given time, and controlling, based at least in part on the determined status information, at least one of the first set of indicators and at least one of the second set of indicators.

Embodiments include a server system including logic of an edge computing device. A network includes a cloud platform able to receive state change events from a state module, and execution of the program logic results in process steps of a method that include transmitting a plurality of attributes from the cloud platform to the at least one edge computing device, where the plurality of attributes can be associated with a device of a distributed environment coupled to the network. A further step includes receiving from the state module, by the edge computing device, current state data of the device, and a subsequent step includes performing a comparison based on a set of rules of the attributes, by the edge computing device, of the current state data. Further, based on the comparison, the method includes sending, by the edge computing device, an update to a human-machine-interface module.

Various embodiments of the teachings herein include an integrity monitoring system for runtime integrity monitoring of a control device connected to sensors and/or actuators and comprising an automation device for collecting operating state data of the control device. The system may include an integrity monitoring unit detachably connectable directly to the control device to monitor the integrity status of the control device on the basis of operating state data transferred from the automation device to the integrity monitoring unit.

A control apparatus for controlling an industrial machine includes a deterioration diagnostic unit that has a diagnostic function, which is repeatedly executed at predetermined timing, for diagnosing deterioration of each component of the industrial machine and outputs an alarm signal in response to deterioration equal to or more than a predetermined level in each component, an alarm control unit that outputs a stop command to the industrial machine based on the alarm signal and informs that the industrial machine is in an alarm stop state, and a cancellation operation receiving unit that receives the cancellation operation for cancelling the alarm stop state regardless of deterioration of each component. The deterioration diagnostic unit cancels the alarm stop state brought on by the diagnostic function based on the cancellation operation, and allows the industrial machine to operate until the diagnostic function is executed next.

A method for diagnosing a vehicle electrical system of a vehicle including a plurality of intercommunicating arithmetic logic units. A diagnostic application is executed on one arithmetic logic unit of the plurality of arithmetic logic units. The diagnostic application receives a diagnostic inquiry from an external diagnostic unit. The diagnostic inquiry is analyzed by the diagnostic application. Based on the content of the diagnostic inquiry, the diagnostic application sends data to at least one arithmetic logic unit and/or sends a diagnostic response to the external diagnostic unit.

Implementations of systems for monitoring industrial equipment may include: a processor coupled with one or more sensors. The systems may include one or more input/outputs coupled with the sensors. The one or more input/outputs may be configured to couple with one or more peripheral devices. The processor may be configured to electrically couple with a remote server. The remote server may be configured to process data received from the one or more sensors and instruct, through the processor, the one or more peripheral devices to make an adjustment.

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.