Various embodiments described herein relate to an enterprise data management dashboard. In this regard, a request to generate a dashboard visualization related to one or more assets is received. The request includes an asset descriptor describing the one or more assets. In response to the request, aspects of aggregated operational technology data within a knowledge graph data structure are correlated to provide one or more insights associated with the one or more assets. Additionally, the dashboard visualization is provided to an electronic interface of a computing device. The dashboard visualization includes visualization data for the one or more insights associated with the knowledge graph data structure.

A distributed multi-node control system (100) and method, relating to the field of control technology. The distributed multi-node control system (100) comprises: a first control node (11), a second control node (12), a plurality of servo nodes (20) and a plurality of execution devices (30), the first control node (11) and the second control node (12) being respectively communicationally connected to the plurality of servo nodes (20), the servo nodes (20) being electrically connected to the execution devices (30) and configured to control operating states of the corresponding execution devices (30), the first control node (11) being configured to control an operating state of at least one first servo node (21) among the plurality of servo nodes (20), the second control node (12) being configured to control an operating state of at least one second servo node (22) among the plurality of servo nodes (20).

A fault identification may be triggered by a component of a power generation system (PGS), such as a hardware component, a controller of a hardware component, a device of the PGS, a computer connected to the PGS, a computer configured to monitor the PGS, and/or the like. The fault identification may be the result of a failure of a component of the PGS, a future failure of a component of the PGS, a routine maintenance of the PGS, and/or the like. The fault is converted to a notification on a user interface using a mapping of faults, root-causes, notification rules, and/or the like. The conversion may use one or more lookup tables and/or formulas for determining the impact of the fault on the PGS, and/or the like.

Provided is a system and method for the distributed networked test of electric vehicles based on a cloud platform, comprising a cloud computing platform and a plurality of remote test benches, wherein the remote test benches, being provided at least two, to form a remote distributed networked structure and transmit test data to the cloud computing platform; the cloud computing platform, being in real-time bidirectional data communication with the remote test bench and configured to receive each test data in real-time, perform a data cleaning, a data classing washing, a data fusion and a data mining, extract useful data information from the test data, build corresponding a data model and a mechanism model on the cloud computing platform according to historical data and a mechanism of control object, control for different test benches, and complete a fault diagnosis and an early warning.

A diagnostics system, for mass flow controller calibration, comprising a controller communicable coupled to a sensor(s) and a valve. The controller controls the valve based on a predetermined set point value and communication from the at least one sensor. The controller determines a number of set point value adjustments and compares results of a calibration operation and a set point value plus a tolerance value. The controller generates a notification message indicating at least one of the predetermined number of set point value adjustments and results of the comparing. The controller calibrates the mass flow controller based on, at least in part, one of a predetermined number of set point value adjustments, results of the comparison, and user input. The notification message can comprise temperature values, valve drive values, sensor flow rate values, gas flow hours, and a remaining number of set point adjustments based on a total fluid hours.

For improved provision of an alarm relating to anomaly scores assigned to input data, a method includes receiving input data relating to at least one device. The input data includes incoming data batches X relating to at least N separable classes. Respective anomaly scores are determined for the respective incoming data batch X relating to the at least N separable classes using N anomaly detection models. The anomaly detection models are applied to the input data to generate output data. A difference is determined, for the respective incoming data batch X, between the determined respective anomaly scores for the at least N separable classes and given respective anomaly scores of the N anomaly detection models. When the respective determined difference is greater than a difference threshold, an alarm relating to the determined difference is provided to a user, the respective device, and/or an IT system connected to the respective device.

The control apparatus 1 includes a control unit 21 that controls execution of a workflow including a handling execution process when an alarm indicating a failure is occurred, and an instruction unit that instructs the control unit 21 to stand by execution of the handling execution process when the handling execution process is capable of being executed before a monitoring period started in response to occurrence of an alarm indicating the failure or a recovery expires, and when the monitoring period expires, execute the handling execution process in a case where an alarm that is occurred most recently indicates the failure, and cancel the execution of the handling execution process in a case where the alarm that is occurred most recently indicates the recovery.

A data processing device capable of performing problem diagnosis in a production system with a plurality of robots includes: a first time series obtaining part for obtaining historical event data used for determining some historical alarm indicator in time series and storing the historical event data as first time series data; a historic alarm indicator calculation part for calculating a series of historic alarm indicators using statistic characteristics of the first time series data; a threshold definition part for defining at least one threshold value based on a statistical distribution of the historical alarm indicators; a second time series obtaining part for obtaining operational event data during operation of the robots used for determining some operational alarm indicator in time series and storing the operational event data as second time series data; and an operational alarm indicator calculation part for calculating a series of operational alarm indicators.

A data transmission system and method for an engine of an aircraft. Engine fault data indicative of at least one fault condition of the engine is obtained at a computing device provided on-board the aircraft. Sensor data associated with the at least one fault condition is retrieved based on the engine fault data. The engine fault data and the sensor data are transmitted, through a wireless connection, to an electronic device external to the aircraft.

A method of identifying anomalous data obtained by at least one sensor of a plurality of sensors located within an environment. The method includes identifying, based on sensor data obtained from the plurality of sensors, at least one instance of a sequence of events that occurred within the environment. A probability of the sequence of events occurring within the environment under non-anomalous conditions is obtained. A frequency characteristic dependent on a frequency at which the sequence of events occurred within the environment is determined. A likelihood of the sequence of events occurring within the environment at the frequency is determined, based on a combination of the probability and the frequency characteristic. It is identified, based on the likelihood, that at least a portion of the sensor data is anomalous.