A method includes obtaining an actual value of a condition affected by operating the building equipment, operating the building equipment using a setpoint for the condition, calculating a stability index based on a timeseries of errors between the actual value and the setpoint, comparing the stability index to a criterion, in response to the stability index satisfying the criterion, executing an action relating to the building equipment, and in response to the stability index not satisfying the criterion, not executing the action.

There is provided a monitoring method and system for an aircraft engine. At an engine controller, a current value of an ageing parameter of at least one component of the aircraft engine is obtained, the at least one component having a life limit associated therewith. Based on the current value of the ageing parameter, it is determined, at the engine controller, whether the life limit of the at least one component has been reached. A start of the aircraft engine is inhibited, at the engine controller, when the life limit of the at least one component has been reached.

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.

Methods, systems, and non-transitory computer readable medium are described for sensor metrology data integration. A method includes receiving sets of sensor data and sets of metrology data. Each set of sensor data includes corresponding sensor values associated with producing corresponding product by manufacturing equipment and a corresponding sensor data identifier. Each set of metrology data includes corresponding metrology values associated with the corresponding product manufactured by the manufacturing equipment and a corresponding metrology data identifier. The method further includes determining common portions between each corresponding sensor data identifier and each corresponding metrology data identifier. The method further includes, for each of the sensor-metrology matches, generating a corresponding set of aggregated sensor-metrology data and storing the sets of aggregated sensor-metrology data to train a machine learning model. The trained machine learning model is capable of generating one or more outputs for performing a corrective action associated with the manufacturing equipment.

A system includes a computer processor located within a cloud system. The system receives data from a sensor associated with an industrial or home automation control application. The sensor is configured to monitor a first condition of a device. The computer processor analyzes the data, and transmits a software update or software download from the cloud system to the industrial or home automation control application based on the data analysis.

The present invention relates to a dual controller system for analyzing a control signal received from two dual controllers, both of which operate in an active state, to check whether an error occurs in the controllers and to perform operation with a controller in a normal state. A dual controller system according to the present invention includes a plurality of lower-layer modules performing respective functions, and first and second controllers for controlling each of the plurality of lower-layer modules, wherein the first and second controllers transmit control signals to the plurality of lower-layer modules, and the lower-layer modules determine whether an error occurs in the two received control signals, remove an erroneous control signal and perform a function according to a normal control signal.

Aspects of the disclosure relate to identifying and responding to problem conditions for a fleet of aerial vehicles. This may include receiving at one or more processors of one or more server computing devices sensor feedback from an AV of the fleet. A problem condition may be identified using the sensor feedback. A mitigation response for the problem condition relating to a mission assigned to the aerial vehicle may be determined. The mitigation response may be sent to the AV in order to cause the aerial vehicle to maneuver according to the mitigation response and thereby automatically respond to the problem condition.

A non-transitory computer-readable storage medium stores instructions, which when executed by a processing device of a diagnostic server, cause the processing device to perform certain operations. The operations include receiving, from a processing chamber, (i) measurement values of a combined signal that is based on an injection of an alternating signal wave onto a first output signal of a controller of the processing chamber, and (ii) measurement values of a second output signal of the controller that incorporates feedback from the processing chamber. The operations further include generating, based on the measurement values of the combined signal and the measurement values of the second output signal of the controller, a baseline bode fingerprint pertaining to a state associated with the processing chamber. The operations further include storing, in computer storage, the baseline bode fingerprint to be used in performing diagnostics of the processing chamber.

Internal control devices (41A to 41D) included respectively in air conditioners (10A to 10D) in an air conditioning system (1) are communicably connected one by one correspondingly to external control devices (42A to 42D). Each of the external control devices (42A to 42D) is configured to execute command generating operation of generating an operation command to a corresponding one of the air conditioners (10A to 10D). The external control devices (42A to 42D) constitute an external control system (50). In the external control system (50), one of the external control devices (42A) functions as a master control device configured to execute the command generating operation whereas remaining ones of the external control devices (42A to 42D) other than the external control device (42A) functioning as the master control device function as sub control devices configured not to execute the command generating operation.

A machine learning apparatus that learns an alarm factor in a motor drive device includes a state observation unit that obtains a feature amount as a state variable from the motor drive device and an alarm factor as label data, the alarm factor corresponding to the feature amount, and a learning unit that generates a learning model for inferring a new alarm factor corresponding to a new feature amount, from a dataset created on a basis of a combination of the state variable and the label data. The feature amount includes at least one of a detected current value detected from the motor, a speed command value specifying a rotational speed of the motor, an output voltage value output to the motor, an estimated speed value of the motor, and a detected speed value of the motor.