A self-optimizing controller for equipment of a plant provides a manipulated variable as an input to the plant and receives an output variable as feedback. The controller generates a performance variable model defining the performance variable as a function of the manipulated variable and an output variable model defining the output variable as a function of the manipulated variable. The controller uses the performance variable model to determine a gradient of the performance variable, uses the output variable model to determine a gradient of the output variable, and generates a self-optimizing variable based on the gradient of the performance variable model and the gradient of the output variable model. The controller operates the equipment of the plant to affect a variable state or condition of the building based on the value of the self-optimizing variable from the self-optimizing variable model.

A state identification device includes: a feature amount extraction unit that extracts a feature amount from physical data of a machine tool unit which shifts to a plurality of operation states; a feature amount storage unit in which a region is provided as a storage destination of the extracted feature amount for each of the plurality of operation states to which the machine tool unit shifts; a similarity calculation unit that calculates a similarity between the extracted feature amount and a feature amount stored in the feature amount storage unit; a state determination unit that determines an operation state of the machine tool unit based on the calculated similarity; and a storage destination determination unit that determines the region serving as the storage destination of the extracted feature amount based on a determination result of the state determination unit.

A self-optimizing controller for equipment of a plant provides a manipulated variable as an input to the plant and receives an output variable as feedback. The controller generates a performance variable model defining the performance variable as a function of the manipulated variable and an output variable model defining the output variable as a function of the manipulated variable. The controller uses the performance variable model to determine a gradient of the performance variable, uses the output variable model to determine a gradient of the output variable, and generates a self-optimizing variable based on the gradient of the performance variable model and the gradient of the output variable model. The controller operates the equipment of the plant to affect a variable state or condition of the building based on the value of the self-optimizing variable from the self-optimizing variable model.

A sensor management system includes a historical data repository, a building management system (BMS) controller, and a sensor diagnostics system. The historical data repository may store historical data by a plurality of sensors. The BMS controller may control one or more components of a building subsystem based on data provided by one or more sensors. The sensor diagnostic system may receive, from a sensor, sensor data. The sensor diagnostic system may determine at least one fault in the sensor data. The sensor diagnostic system may select, from the historical data repository, substitute sensor data for the sensor based on a comparison of one or more attributes of the sensor data and one or more attributes of the historical data in the data repository. The sensor diagnostic system may provide, in replacement of the sensor data from the sensor, the supplemental sensor data to the BMS controller.

A testing platform tests an electrical and mechanical system such as an HVAC unit according to an algorithm that reduces the total testing time of the components of the system, while ensuring the safety of the system during system-wide testing. The platform uses constraints that are checked both before and during the testing to ensure that HVAC operating conditions are acceptable for starting and maintaining component tests. Preferably, the platform uses finite-state machines for each device to organize the component tests, allowing for monitoring of constraints and starting, pausing, and stopping component tests. Preferably, total test execution time is reduced by running component tests in parallel, running component tests based on loads of the components, or combinations of both.

A machine tool controller capable of identifying the factors of a warning easily is provided. A controller of a machine tool includes a power supply monitoring unit, a power supply abnormality detecting unit, and an abnormality diagnosis unit. The power supply monitoring unit acquires measurements indicating an operating state of a machine tool and/or a peripheral device (for example, an AC power supply or a motor) of the machine tool. The power supply abnormality detecting unit detects an abnormality in the operating state of the machine tool and/or the peripheral device and outputs a signal indicating the abnormality and the measurements obtained when the abnormality was detected. The abnormality diagnosis unit automatically selects a measurement related to the abnormality among the measurements and automatically diagnoses factors of the abnormality.

A brushless DC motor with automatic record of abnormal operation includes an integrated circuit chip electrically connected to a drive circuit of the brushless DC motor. The integrated circuit chip includes a central processing unit, and a flash memory and a detection unit connected to the central processing unit. When the detection unit receives a detection signal from the drive circuit and the central processing unit determines that the brushless DC motor is in an abnormal operation state, the central processing unit generates an abnormal operation data including an error code so that the flash memory automatically stores the abnormal operation data. The present disclosure further includes a method of automatically recording abnormality for the brushless DC motor.

A testing platform tests an electrical and mechanical system such as an HVAC unit according to an algorithm that reduces the total testing time of the components of the system, while ensuring the safety of the system during system-wide testing. The platform uses constraints that are checked both before and during the testing to ensure that HVAC operating conditions are acceptable for starting and maintaining component tests. Preferably, the platform uses finite-state machines for each device to organize the component tests, allowing for monitoring of constraints and starting, pausing, and stopping component tests. Preferably, total test execution time is reduced by running component tests in parallel, running component tests based on loads of the components, or combinations of both.

A motor controller for an electric motor includes an enclosure and a processing device, memory device, and wireless transmitter disposed within the enclosure. The processing device is configured to control the electric motor and collect operating information from the electric motor during operation of the electric motor. The memory device is communicatively coupled to the processing device. The memory device is configured to receive and store the operating information collected during operation of the electric motor. The wireless transmitter is communicatively coupled to the processing device and the memory device. The wireless transmitter is configured to transmit the operating information stored on the memory device to a computing device disposed remotely from the electric motor.

A testing platform tests an electrical and mechanical system such as an HVAC unit according to an algorithm that reduces the total testing time of the components of the system, while ensuring the safety of the system during system-wide testing. The platform uses constraints that are checked both before and during the testing to ensure that HVAC operating conditions are acceptable for starting and maintaining component tests. Preferably, the platform uses finite-state machines for each device to organize the component tests, allowing for monitoring of constraints and starting, pausing, and stopping component tests. Preferably, total test execution time is reduced by running component tests in parallel, running component tests based on loads of the components, or combinations of both.