Patent classifications
G01L27/02
ABNORMALITY DETERMINATION DEVICE AND ABNORMALITY DETERMINATION METHOD
An abnormality determination device according to one aspect of the present disclosure is an abnormality determination device that determines an abnormality of an inducer used for a pump, the abnormality determination device including a stress-response acquisition unit that acquires a stress response indicating a temporal change in stress applied to the inducer, an accumulated-fatigue-damage-degree calculation unit that calculates an accumulated fatigue-damage degree of the inducer based on the stress response, a lifetime-consumption-rate calculation unit that calculates a lifetime consumption rate that is a changing rate of the accumulated fatigue-damage degree with respect to time, and a determination unit that determines an abnormality of the inducer based on the accumulated fatigue-damage degree and the lifetime consumption rate, in which the inducer is used only for a predetermined use time per operation of the pump.
ABNORMALITY DETERMINATION DEVICE AND ABNORMALITY DETERMINATION METHOD
An abnormality determination device according to one aspect of the present disclosure is an abnormality determination device that determines an abnormality of an inducer used for a pump, the abnormality determination device including a stress-response acquisition unit that acquires a stress response indicating a temporal change in stress applied to the inducer, an accumulated-fatigue-damage-degree calculation unit that calculates an accumulated fatigue-damage degree of the inducer based on the stress response, a lifetime-consumption-rate calculation unit that calculates a lifetime consumption rate that is a changing rate of the accumulated fatigue-damage degree with respect to time, and a determination unit that determines an abnormality of the inducer based on the accumulated fatigue-damage degree and the lifetime consumption rate, in which the inducer is used only for a predetermined use time per operation of the pump.
PRESSURE SENSOR
Aiming to more easily perform calibration of a sensor output from a pressure sensor, the calibration being necessitated due to the occurrence of sedimentation, a resonance point measurement unit (122) measures a resonance point of a diaphragm (112) on the basis of the result obtained by performing measurement of a constant pressure using the pressure sensor while a power supply frequency is changed, a characteristic calculation unit (123) calculates, on the basis of the measured resonance point, an elastic modulus of the diaphragm (112) at the time of the measurement of the resonance point, and a correction unit (124) calculates a corrected sensor sensitivity resulting from correcting a sensor sensitivity of a sensor chip (101) on the basis of the elastic modulus calculated by the characteristic calculation unit (123).
VIRTUAL SENSING METHOD AND SYSTEM FOR VARIABLE INLET GUIDE VANE CONTROL FLUID DEVICE OPERATING FREQUENCY BASED ON METAMODEL
There are a method and a system for virtual sensing, which predict a current operating frequency of a variable IGV control fluid device (for example, a pump, a blower) based on a metamodel. A virtual sensing method for sensing a variable IGV control fluid device operating frequency based on a metamodel according to an embodiment includes: collecting, by a communication unit, input characteristic data from a fluid device system; and predicting, by a processor, output characteristic data by applying the input characteristic data to a metamodel which is a machine learning model, and the input characteristic data is two or more of a fluid pressure (P), a fluid flow rate (Q), and an IGV angle (), and the output characteristic data is an operating frequency (N) of the fluid device.
VIRTUAL SENSING METHOD AND SYSTEM FOR VARIABLE INLET GUIDE VANE CONTROL FLUID DEVICE OPERATING FREQUENCY BASED ON METAMODEL
There are a method and a system for virtual sensing, which predict a current operating frequency of a variable IGV control fluid device (for example, a pump, a blower) based on a metamodel. A virtual sensing method for sensing a variable IGV control fluid device operating frequency based on a metamodel according to an embodiment includes: collecting, by a communication unit, input characteristic data from a fluid device system; and predicting, by a processor, output characteristic data by applying the input characteristic data to a metamodel which is a machine learning model, and the input characteristic data is two or more of a fluid pressure (P), a fluid flow rate (Q), and an IGV angle (), and the output characteristic data is an operating frequency (N) of the fluid device.
Method for operating a pressure measuring cell of a capacitive pressure sensor
A method for operating a pressure measuring cell of a capacitive pressure sensor. The pressure measuring cell includes a pressure-dependent measuring capacitor and a reference capacitor, with an internal alternating square wave excitation voltage applied. The pressure measured value is obtained from capacitance values of the measuring capacitor and the reference capacitor. The measurement signal is an alternating square-wave signal supplied to an evaluation unit. The alternating square-wave signal is supplied to an amplifier unit where signal amplification is performed by amplitude adjustment for the internal excitation voltage and offset compensation is performed by a further square-wave signal and gain correction is performed by multiplicative influencing of the quotient of the capacitance values of the reference capacitor and the measuring capacitor, and the offset correction is performed by virtue of the square-wave signal being supplied to the amplifier unit and thus being added to the square-wave signal.
Method for operating a group of pressure sensors
Method for operating a group 1 of pressure sensors which are arranged in such a manner that they can measure the pressure in a common measurement volume 2, wherein the group of pressure sensors comprises at least a first pressure sensor 1 with a first pressure measurement range and a second pressure sensor 1 with a second pressure measurement range, wherein the first and second pressure measurement ranges overlap in an overlap pressure measurement range, wherein the first and second pressure sensors are each based on an indirect pressure measurement principle and are configured to output a measurement signal calibrated to a reference gas, and wherein the method comprises the steps of: a) providing calibration data specific to the type of gas for the first measurement signal and for the second measurement signal, which calibration data describe a dependence of the first and second measurement signals on the effective pressure and on a list of types of gas, respectively; b) recording a first and a second measured value of the first and second measurement signals, respectively; c) determining a resultant type of gas which best matches the combination of the recorded first measured value and the recorded second measured value, taking into account the first and second calibration data. In one variant, a resultant pressure which is independent of the type of gas is additionally determined. The invention is also directed to an apparatus for earring out the method and to a computer program product.
TRIVIAL TRANSMITTER MODEL
A trivial transmitter model enables processes to be automated and/or networks to be managed efficiently and reliably. The trivial transmitter model includes one or more interneuron units that receive a quantity of transmitters including at least a portion of a first quantity of a source transmitter released by a source unit, determine a second quantity of an interneuron transmitter based on the quantity of received transmitters including at least the portion of the first quantity of the source transmitter, and release the second quantity of the interneuron transmitter, wherein the second quantity of the interneuron transmitter is configured to perform an action upon satisfying a predetermined threshold.
TRIVIAL TRANSMITTER MODEL
A trivial transmitter model enables processes to be automated and/or networks to be managed efficiently and reliably. The trivial transmitter model includes one or more interneuron units that receive a quantity of transmitters including at least a portion of a first quantity of a source transmitter released by a source unit, determine a second quantity of an interneuron transmitter based on the quantity of received transmitters including at least the portion of the first quantity of the source transmitter, and release the second quantity of the interneuron transmitter, wherein the second quantity of the interneuron transmitter is configured to perform an action upon satisfying a predetermined threshold.