** **

For the elliptical gear fuel consumption sensor, when the pressure is not high (pressure value is less than 6.4 MPa), the pressure has little effect on the accuracy, and no pressure correction is required [4]. This article focuses on the effects of viscosity and temperature on the accuracy of the flowmeter and the method of correction.

The correction of the metering accuracy is actually a correction of the meter factor K. The meter factor is mainly used for unit conversion. The output pulse of the elliptical gear oil consumption measuring sensor is converted into an engineering unit. Its physical meaning is the number of pulses emitted by the fluid flowing through the unit volume of the flow meter. The corrected meter coefficient K' is the median value of the E2 user medium deviation in the formula K'=K[1 E2-E1-T(t2-t1)](2). Medium temperature, Â°Ct1 calibration medium temperature, Â°CT flowmeter housing expansion coefficient, /Â°C In the case of measuring pulse frequency F, the corrected meter coefficient K' is substituted into equation (1) to obtain the measured medium. Volume flow rate QV=FK[1 E2-E1-T(t2-t1)] (3) Measurement model of elliptical gear oil consumption under the condition of the above, when the volume flow of diesel and the density under working conditions are known The mass flow rate of diesel is Qm=QV(4) where =0/[1 (t2-t1)] Qm diesel mass flow, kg/s working temperature t2 diesel density 0 temperature t1 diesel density diesel volume Expansion coefficient, 1/Â°C Substituting equation (3) into equation (4), the measurement model of elliptical gear oil consumption measurement sensor under working condition Qm=F0K[1 E2-E1-T(t2-t1)][1 (t2 -t1)](5)2 Diesel engine oil consumption model intelligent optimization correction In order to further improve the accuracy of the diesel engine oil consumption measurement model and prevent changes in environmental conditions, the characteristics of the measurement model change. The model parameter â€œdriftâ€ phenomenon during the measurement of diesel engine oil consumption is used, and the diesel engine oil consumption measurement model is online corrected by the function chain neural network [5~7] technology.

Assume that the corrected diesel engine oil consumption intelligent measurement output value can be described by the power level n-degree polynomial (generally taking n=3). X(xi)=c0 c1xi c2x2i c3x3i(6) where xi diesel engine oil consumption is the i-th measurement output The value gives a schematic diagram of the functional connection, which clearly shows that in this mathematical conceptual model of the network suitable for parallel distributed processing, once a node (such as node k) is excited, there will be many additional function functions. It is also motivated to obtain not only xk but also g0(xk), g1(xk),...,gn(xk). In principle, as long as the function chain method is used, supervised learning can always be achieved with a single layer network.

Shown is a functional chain neural network. In the figure, Wj (j=0, 1, 2, 3) is the connection weight of the network. The number of connection weights is the same as the order of the inverse nonlinear polynomial. Assuming that the neurons of the neural network are linear, the input values â€‹â€‹of the function chain neural network are: 1, xi, x2i, x3i.

In general, the weights W0, W1 are of the same order of magnitude, W2 is at least an order of magnitude lower than W1, and W3 is orders of magnitude lower than W2. The low order of magnitude is determined by the degree of nonlinearity of the sensor's nonlinear characteristics. When the optimal solutions W0, W1, W2, and W3 are obtained, there are c0=W0, c1=W1, c2=W2, and c3=W3, and the obtained undetermined coefficients c0, c1, c2, and c3 are stored in the memory.

The fuel consumption measurement model uses an elliptical gear fuel consumption sensor with a pulse number of 4 per revolution, a gear accuracy of 5, and a meter factor K of 1.553 pulses/m3. Other basic parameters are shown. The 495 diesel engine test rig has two upper and lower fuel tanks containing No. 0 light diesel oil with a height difference of 3.5 m. The oil pump is used to pump the oil from the lower tank to the upper tank. The oil flows through the filter and the elliptical gear fuel consumption sensor to the lower tank, and a Coriolis mass flow meter is installed on the oil line, and the flow rate is controlled by a throttle valve for comparison test. Based on the mass flow rate of diesel elliptical gear fuel consumption sensor measured by Coriolis mass flowmeter under different volume flow QV, the trend of relative error before and after calibration of diesel engine elliptical gear oil consumption measurement model is shown.

The volumetric flow rate QV is between 20 and 70 L/h. The relative error of the diesel engine elliptical gear oil consumption measurement model corrected by the functional chain neural network is about 0.85, while the diesel engine elliptical gear fuel consumption measurement model without functional chain neural network correction is relative. The error is around 0.975, but when the volumetric flow rate QV is less than 20L/h, the relative error of the diesel engine elliptical gear oil consumption measurement model increases sharply, which may be due to the large proportion of leakage at small flow. It can be seen that after the correction by the function chain neural network, the diesel elliptical gear oil consumption measurement model has high precision, and the oil consumption of the elliptical gear of the diesel engine can be measured online.

Conclusion (1) Combining the measurement mechanism of the elliptical gear oil consumption measurement sensor with the function chain neural network correction, the diesel engine oil consumption measurement model is established, which makes the diesel fuel consumption difficult to measure online become a parameter that can monitor its change and work for the diesel engine. Process intelligence control provides assurance. (2) The elliptical gear oil consumption measurement model is corrected by the function chain neural network, and its relative error is reduced by 0.125 on average. It has high precision and can realize on-line measurement of diesel engine elliptical gear oil consumption.

For the elliptical gear fuel consumption sensor, when the pressure is not high (pressure value is less than 6.4 MPa), the pressure has little effect on the accuracy, and no pressure correction is required [4]. This article focuses on the effects of viscosity and temperature on the accuracy of the flowmeter and the method of correction.

The correction of the metering accuracy is actually a correction of the meter factor K. The meter factor is mainly used for unit conversion. The output pulse of the elliptical gear oil consumption measuring sensor is converted into an engineering unit. Its physical meaning is the number of pulses emitted by the fluid flowing through the unit volume of the flow meter. The corrected meter coefficient K' is the median value of the E2 user medium deviation in the formula K'=K[1 E2-E1-T(t2-t1)](2). Medium temperature, Â°Ct1 calibration medium temperature, Â°CT flowmeter housing expansion coefficient, /Â°C In the case of measuring pulse frequency F, the corrected meter coefficient K' is substituted into equation (1) to obtain the measured medium. Volume flow rate QV=FK[1 E2-E1-T(t2-t1)] (3) Measurement model of elliptical gear oil consumption under the condition of the above, when the volume flow of diesel and the density under working conditions are known The mass flow rate of diesel is Qm=QV(4) where =0/[1 (t2-t1)] Qm diesel mass flow, kg/s working temperature t2 diesel density 0 temperature t1 diesel density diesel volume Expansion coefficient, 1/Â°C Substituting equation (3) into equation (4), the measurement model of elliptical gear oil consumption measurement sensor under working condition Qm=F0K[1 E2-E1-T(t2-t1)][1 (t2 -t1)](5)2 Diesel engine oil consumption model intelligent optimization correction In order to further improve the accuracy of the diesel engine oil consumption measurement model and prevent changes in environmental conditions, the characteristics of the measurement model change. The model parameter â€œdriftâ€ phenomenon during the measurement of diesel engine oil consumption is used, and the diesel engine oil consumption measurement model is online corrected by the function chain neural network [5~7] technology.

Assume that the corrected diesel engine oil consumption intelligent measurement output value can be described by the power level n-degree polynomial (generally taking n=3). X(xi)=c0 c1xi c2x2i c3x3i(6) where xi diesel engine oil consumption is the i-th measurement output The value gives a schematic diagram of the functional connection, which clearly shows that in this mathematical conceptual model of the network suitable for parallel distributed processing, once a node (such as node k) is excited, there will be many additional function functions. It is also motivated to obtain not only xk but also g0(xk), g1(xk),...,gn(xk). In principle, as long as the function chain method is used, supervised learning can always be achieved with a single layer network.

Shown is a functional chain neural network. In the figure, Wj (j=0, 1, 2, 3) is the connection weight of the network. The number of connection weights is the same as the order of the inverse nonlinear polynomial. Assuming that the neurons of the neural network are linear, the input values â€‹â€‹of the function chain neural network are: 1, xi, x2i, x3i.

In general, the weights W0, W1 are of the same order of magnitude, W2 is at least an order of magnitude lower than W1, and W3 is orders of magnitude lower than W2. The low order of magnitude is determined by the degree of nonlinearity of the sensor's nonlinear characteristics. When the optimal solutions W0, W1, W2, and W3 are obtained, there are c0=W0, c1=W1, c2=W2, and c3=W3, and the obtained undetermined coefficients c0, c1, c2, and c3 are stored in the memory.

The fuel consumption measurement model uses an elliptical gear fuel consumption sensor with a pulse number of 4 per revolution, a gear accuracy of 5, and a meter factor K of 1.553 pulses/m3. Other basic parameters are shown. The 495 diesel engine test rig has two upper and lower fuel tanks containing No. 0 light diesel oil with a height difference of 3.5 m. The oil pump is used to pump the oil from the lower tank to the upper tank. The oil flows through the filter and the elliptical gear fuel consumption sensor to the lower tank, and a Coriolis mass flow meter is installed on the oil line, and the flow rate is controlled by a throttle valve for comparison test. Based on the mass flow rate of diesel elliptical gear fuel consumption sensor measured by Coriolis mass flowmeter under different volume flow QV, the trend of relative error before and after calibration of diesel engine elliptical gear oil consumption measurement model is shown.

The volumetric flow rate QV is between 20 and 70 L/h. The relative error of the diesel engine elliptical gear oil consumption measurement model corrected by the functional chain neural network is about 0.85, while the diesel engine elliptical gear fuel consumption measurement model without functional chain neural network correction is relative. The error is around 0.975, but when the volumetric flow rate QV is less than 20L/h, the relative error of the diesel engine elliptical gear oil consumption measurement model increases sharply, which may be due to the large proportion of leakage at small flow. It can be seen that after the correction by the function chain neural network, the diesel elliptical gear oil consumption measurement model has high precision, and the oil consumption of the elliptical gear of the diesel engine can be measured online.

Conclusion (1) Combining the measurement mechanism of the elliptical gear oil consumption measurement sensor with the function chain neural network correction, the diesel engine oil consumption measurement model is established, which makes the diesel fuel consumption difficult to measure online become a parameter that can monitor its change and work for the diesel engine. Process intelligence control provides assurance. (2) The elliptical gear oil consumption measurement model is corrected by the function chain neural network, and its relative error is reduced by 0.125 on average. It has high precision and can realize on-line measurement of diesel engine elliptical gear oil consumption.

JTY Titanium can process Titanium Product according to customers' drawings. If no drawings are available, JTY can also design drawings and execute production based on requirements.

Service: CNC Machining,Turning and Milling, CNC Turning, OEM Parts

Material :Titanium GR1,GR2,GR5, GR7,GR12etc

Main Equipment : CNC Machining center(Milling), CNC Lathe, Grinding machine,

Cylindrical grinder machine, Drilling machine, Laser Cutting Machine,etc.

Drawing: format STEP,STP,GIS,CAD,PDF,DWG,DXF etc or samples.

Tolerance: +/-0.01mm ~ +/-0.05mm

Surface Roughness: Ra 0.1~3.2

Inspection:

Complete inspection lab with Micrometer, Optical Comparator, Caliper Vernier,CMM

Depth Caliper Vernier, Universal Protractor, Clock Gauge, Internal Centigrade Gauge

Capacity:

CNC turning work range: Ï†0.5mm-Ï†150mm*300mm

CNC milling work range: 510mm*1020mm*500mm

Oem For Drawing,Oem&Odm Titanium Maching Parts,Titanium Tubes Forged As Per Drawings,High Strength Titanium Parts

Baoji Jintaoyue New Material Technology Co.,Ltd , https://www.jtytitanium.com