TWI626978B - Fluid filter abnormality detecting method and fluid filter abnormality detecting system - Google Patents

Abstract

The invention provides a fluid filter abnormality detecting method, which comprehensively judges the geometry of the fluid filter, the physical properties of the fluid, the porosity of the fluid filter, the impurity density, the measured fluid flow rate and the pressure Poorly, an operational model of the fluid filter is established, and the initial impurity accumulation in the fluid filter is measured through the operational model. Furthermore, the present invention further obtains the impurity accumulation amount of the impurity in the fluid filter at an estimated time through the Kalman estimator, and compares the accumulated amount of the impurity with a preset value, thereby judging the fluid filtration. Whether the device is operating abnormally. Thereby, the fluid filter abnormality detecting method of the present invention proves to have high accuracy through experiments.

Description

Fluid filter abnormality detecting method and fluid filter abnormality detecting system

The present invention relates to a fluid filter anomaly detection method and a fluid filter anomaly detection system; more particularly, the present invention relates to a pressure difference detection that simultaneously combines fluid flow detection and fluid filter. The fluid filter anomaly detection method and the fluid filter anomaly detection system capable of dynamically predicting the accumulation of impurities.

In general, fluid filters are used for filtering impurities, and can be roughly classified into liquid filters and gas filters depending on the type of use. In a vehicle vehicle, a plurality of fluid filters such as an air cleaner, an oil filter, a fuel filter, an automatic transmission oil filter, and an indoor air conditioner filter are usually provided. At present, based on environmental protection and energy saving demand, diesel vehicles are receiving more and more attention; while diesel engine driving engines use electronically controlled common rail diesel injection systems, which require high injection pressure and high atomization effect; therefore, for fuel filters The filtration accuracy and efficiency are highly valued and required, so the fuel filter plays an important role. The main purpose of the fuel filter is to filter the internal impurities of the fuel system, while the fuel Impurities in the system may come from the quality purity of the gas station itself, the cleanliness of the tank at the gas station, and the dissolution of the vehicle's fuel tank due to long-term use.

In general, a fluid filter is filtered by its filter material (filter element). As the impurities will accumulate over time, the fluid filter will gradually block as the time of use increases, which will affect the oil flow and oil quality. Therefore, the car manufacturer recommends that the owner change the fluid filter regularly. However, due to differences in driving conditions, oil types, quality and usage environment of each vehicle, it is difficult to predict the timing of fluid filter blocking. Premature replacement will result in increased maintenance costs. This leads to an increased risk of failure.

The prior art technique determines the filter block pressure difference point by judging the change of the fluid filter fluid inlet and the fluid outlet pressure difference, and initiates an early warning mechanism to alert the user. The above method can obtain good results in a steady state of fluid flow. However, the vehicle vehicle travels dynamically, which causes an unstable change in fluid flow, and the pressure difference cannot be stabilized; the unstable pressure difference will make the timing of the early warning mechanism unable to be accurately determined.

Therefore, the fluid filter needs to be more accurate in judging the timing of replacement, promptly reminding the owner to replace the new product, and ensuring that the vehicle is driven under optimal efficiency and safety conditions.

Based on the above, the market expects to develop a method and system that can accurately determine whether the fluid filter is operating abnormally, and can immediately notify the user of new technology for replacing the new product timing, and its technical importance is gradually improved.

The invention provides a fluid filter anomaly detection method. according to According to the measured fluid flow and pressure difference, together with the geometry of the fluid filter, the physical properties of the fluid, the porosity of the fluid filter and the impurity density, the operational model of the fluid filter is established. The operating model can obtain the accumulation of impurities in the fluid filter and predict the change of impurity accumulation over time through the Kalman estimator. Thereby, the fluid filter abnormality detecting method of the present invention has high accuracy.

To achieve the above objective, in one embodiment, the present invention provides a fluid filter anomaly detection method, the method comprising: detecting a flow of a fluid in a fluid filter; detecting a fluid filter One pressure difference; establishes a working model of the fluid filter according to the geometry of the fluid filter, the physical properties of the fluid, the porosity of the fluid filter, an impurity density, the flow rate and the pressure difference; An initial impurity accumulation amount; through a Kalman estimator, estimating a state of accumulation of impurities in the fluid filter over time according to an initial impurity accumulation amount and an initial pressure difference; obtaining a fluid filter according to the change state The impurity inside is used to estimate the amount of impurity accumulation in one time, and the impurity accumulation amount is compared with a preset value to determine whether the fluid filter operates abnormally.

In the above fluid filter abnormality detecting method, the fluid may be a fuel oil, an oil or an automatic transmission oil. The pressure difference is a pressure difference formed by the fluid flowing through one of the fluid inlet of the fluid filter and one of the fluid outlets of the fluid filter, and the pressure difference includes a void pressure difference, a filter pressure difference, and an impurity pressure difference.

In the above fluid filter abnormality detecting method, the porosity of the fluid filter includes a void porosity, a filter porosity, and an impurity porosity. The geometry of the fluid filter includes a cross-sectional area of the fluid filter, a void thickness, a filter thickness, and an impurity thickness. The physical properties of the fluid include fluid A viscosity coefficient.

In the above fluid filter anomaly detection method, the operational model of the fluid filter can be expressed by the following relationship: Where m is the cumulative amount of impurities in the air filter, Δ P is the pressure difference, μ is the fluid viscosity coefficient, Q is the flow rate of the fluid, A is the cross-sectional area of the fluid filter, and κ _b is the porosity of the empty shell. κ _f is the filter core porosity, κ _p is the impurity porosity, ρ _p is the impurity density, L _b is the empty shell thickness, and L _f is the filter core thickness. The porosity of the shell κ _b can be obtained by quadratic linear regression of the values of the complex array flow.

In the above fluid filter abnormality detecting method, after the fluid filter is abnormally operated, a warning sound, a warning light or a warning message may be issued.

In another embodiment, the present invention provides a fluid filter anomaly detection system including a fluid filter, a flow detector, a differential pressure detector, an analyzer, and a Kalman estimation. And a processor. A fluid flows through the fluid filter. The flow detector detects the flow of one of the fluids; the differential pressure detector detects a pressure difference within the fluid filter. The analyzer establishes an operational model of the fluid filter based on the geometry of the fluid filter, the physical properties of the fluid, the porosity of the fluid filter, an impurity density, the flow rate, and the pressure difference, and obtains a model through the operational model. Initial impurity accumulation. The Kalman estimator estimates the state of accumulation of impurities in the fluid filter over time based on the initial impurity accumulation and an initial pressure difference. The processor obtains an impurity accumulation amount of the impurity in the fluid filter at an estimated time according to the change state, and compares the impurity accumulation amount with a preset value to determine whether the fluid filter operates abnormally.

In the above fluid filter abnormality detecting system, the fluid may be a fuel oil, an oil or an automatic transmission oil. The pressure difference includes a shell pressure difference, a filter pressure difference, and an impurity pressure difference. Additionally, the fluid filter can be disposed on a marine vehicle, a land vehicle, or a flying vehicle.

In the above fluid filter anomaly detection system, the operational model of the fluid filter can be expressed by the following relationship: Where m is the cumulative amount of impurities in the air filter, Δ P is the pressure difference, μ is the fluid viscosity coefficient, Q is the flow rate of the fluid, A is the cross-sectional area of the fluid filter, and κ _b is the porosity of the empty shell. κ _f is the filter core porosity, κ _p is the impurity porosity, ρ _p is the impurity density, L _b is the empty shell thickness, and L _f is the filter core thickness.

S101~S106‧‧‧Steps

100‧‧‧Fluid filter

110‧‧‧ filter

120‧‧‧ empty shell

130‧‧‧check valve

130a‧‧‧ fluid inlet

130b‧‧‧Fluid outlet

130c‧‧‧ annular passage

200‧‧‧Flow detector

300‧‧‧ Differential Pressure Detector

400‧‧‧Analyzer

500‧‧‧Kalman estimator

600‧‧‧ processor

F‧‧‧ fluid

1 is a flow chart showing a fluid filter abnormality detecting method according to an embodiment of the present invention; and FIG. 2 is a schematic view showing a fluid filter abnormality detecting system according to an embodiment of the present invention; FIG. 4 is a schematic diagram showing the operation state of the fluid filter in FIG. 2; FIG. 4 is a schematic diagram of estimating the accumulation of impurities in the fluid filter over time using a Kalman estimator. State diagram; Figure 5A is a plot of the predicted and actual pressure difference as a function of fluid flow in a fluid (oil D100); Figure 5B is shown in another fluid (oil B100), the predicted and actual pressure difference as a function of fluid flow; Figure 6 is shown in a fluid containing impurities (oil B20), prediction and Figure 5 shows the change of the actual pressure difference with the fluid flow rate; Figure 7 shows the change of the predicted and actual pressure difference with time when the simulated impurity accumulation is from 1g to 6g under the condition of fluid flow rate 2l/min; It is shown in the case of fluid flow rate 2l/min, when the simulated impurity accumulation amount is from 1g to 6g, the predicted and actual impurity accumulation amount changes with time; the ninth figure shows the simulated impurity accumulation amount under the condition of fluid flow rate 2l/min. From 1g to 6g, the predicted and actual impurity concentration changes with time; and Figure 10 is shown in the fluid flow rate of 2l / min, the simulated impurity accumulation from 1g to 6g, the predicted and actual impurity accumulation Error change graph.

Hereinafter, a plurality of embodiments of the present invention will be described with reference to the drawings. For the sake of clarity, many practical details will be explained in the following description. However, it should be understood that these practical details are not intended to limit the invention. That is, in some embodiments of the invention, these practical details are not necessary. In addition, some of the conventional structures and elements are shown in the drawings in a simplified schematic manner in order to simplify the drawings.

Please refer to FIG. 1 , which is a flow chart showing a method for detecting an abnormality of a fluid filter according to an embodiment of the invention. The fluid filter anomaly detection method proposed by the present invention generally comprises the following steps.

Step S101, detecting a flow rate of one of the fluids in the fluid filter.

Step S102, detecting a pressure difference in the fluid filter.

Step S103, establishing an operational model of the fluid filter according to the geometry of the fluid filter, the physical properties of the fluid, the porosity of the fluid filter, the impurity density, the flow rate, and the pressure difference.

In step S104, an initial impurity accumulation amount is obtained through the operation model.

Step S105, estimating, by a Kalman estimator, a state of change of impurities in the fluid filter over time according to an initial impurity accumulation amount and an initial pressure difference.

Step S106: Obtain an impurity accumulation amount of the impurity in the fluid filter at an estimated time according to the change state, and compare the impurity accumulation amount with a preset value to determine whether the fluid filter operates abnormally.

In one example, the present invention provides a fluid filter anomaly detection system that operates one of the fluid filter anomaly detection methods described above. Please refer to Figure 2 and Figure 3 together. 2 is a schematic view showing a fluid filter system according to an embodiment of the present invention; and FIG. 3 is a schematic view showing the operation of the fluid filter 100 in FIG. The fluid filter anomaly detection system generally includes a fluid filter 100, a differential pressure detector 200 and a flow detector 300, an analyzer 400, a Kalman estimator 500, and a processor 600.

Operation of Fluid Filter 100, generally, as shown in Figure 3, fluid F enters fluid inlet 130a of one-way valve 130 and is filtered by filter element 110. Based on this embodiment, the filter element 110 is cylindrically symmetrical, so the fluid F is also part The flow passes through the annular passage 130c into the filter element 110 to provide a complete filtering effect for the fluid F. After filtration, the fluid F flows out of the fluid outlet 130b of the one-way valve 130.

The flow detector 300 is used to measure the flow of the fluid F in the fluid filter 100. In general, if it is a vehicle carrier, the traffic detector 300 is assembled when the vehicle carrier is shipped from the factory, and no additional installation is required, which can save additional cost. In addition, the fluid filter 100 may be disposed in a marine carrier or a flight carrier in addition to a land vehicle such as a steam or a locomotive.

The differential pressure detector 200 is used to measure the pressure difference within the fluid filter 100. This pressure difference is the difference in pressure formed by the fluid F passing through the fluid inlet 130a and then flowing out of the fluid outlet 130b.

The analyzer 400 is configured to simultaneously receive the flow rate and the pressure difference of the fluid F, and additionally obtain the parameters of the geometry of the fluid filter 100, the physical characteristics of the fluid F, the porosity of the fluid filter 100, the impurity density, and the like to establish The operational model of the fluid filter 100. After the analyzer 400 establishes the operational model of the fluid filter 100, an initial amount of accumulated impurities can be obtained according to actual conditions.

The Kalman estimator 500 estimates a state in which the impurities in the fluid filter 100 accumulate over time based on the initial impurity accumulation amount and an initial pressure difference.

The processor 600 obtains an impurity accumulation amount of the impurity in the fluid filter 100 at an estimated time according to the change state, and compares the impurity accumulation amount with a preset value to determine whether the fluid filter 100 operates abnormally. . In one example, when the accumulated amount of impurities exceeds a preset value, it is determined that the fluid filter 100 is abnormally operated, and a warning sound, a warning light or a warning message is issued to remind the user.

The analyzer 400, the processor 600, and the like all refer to a computer device having a logical operation function, and has a non-transitory storage medium for storing software programs required for the operation. In other possible instances, analyzer 400 and processor 600 can be integrated into a multi-function computer device or an integrated chip to streamline the volume.

The fluid F described above may be a fuel oil, an oil or an automatic transmission fluid. In other embodiments, the remaining oil species that must be filtered via fluid filter 100 are also potentially applicable to the methods and systems of the present invention.

It should also be mentioned that the above pressure difference comprises a shell pressure difference, a filter element pressure difference and an impurity pressure difference. The porosity of the fluid filter comprises an empty shell porosity, a filter core porosity, and an impurity porosity. The geometry of the fluid filter includes a cross-sectional area of the fluid filter, a void thickness, a filter thickness, and an impurity thickness; and the physical properties of the fluid include a viscosity coefficient of the fluid.

The meaning of each of the above parameters will be described in detail below, and how to establish an operational model of the fluid filter. First, assuming that the fluid F flows through the fluid filter 100, the following relationship is satisfied: Δ P = Δ P _box + Δ P _filter + Δ P _P (1); Where m is the cumulative amount of impurities in the fluid filter, Δ P is the pressure difference, μ is the fluid viscosity coefficient, Q is the flow rate of the fluid, A is the cross-sectional area of the fluid filter, and κ _b is the porosity of the shell κ _f is the filter porosity, κ _p is the impurity porosity, ρ _p is the impurity density, L _b is the empty shell thickness, L _f is the filter thickness, L _P is the impurity thickness, Δ P _box is the empty shell pressure difference, △ P _filter filter pressure difference, △ P _P is the impurity pressure difference.

Combining the above formulas (1) to (5), we can obtain:

The above formula (6) is the operational model of the fluid filter 100, that is, the state of accumulation of impurities in the fluid filter 100 when the fluid filter 100 is operated by the impurity accumulation amount m in the fluid filter 100. . It should be noted that since the fluid F flows through the empty shell 120 of the fluid filter 100 and the filter element 110 to form impurities, it is necessary to consider different parameters of the empty shell state, the filter state, and the impurity accumulation state. Therefore, there is void porosity κ _b , filter porosity κ _f , impurity porosity κ _p , empty shell thickness L _b , filter thickness L _f , impurity thickness L _P , empty shell pressure difference Δ P _box , filter pressure difference △ P _filter , and a comprehensive consideration of the impurity pressure difference Δ P _P .

It should also be mentioned that since the opening or the pore communicating with the opening allows fluid to enter, the ratio of the volume occupied by the opening to the total volume of the material is defined as porosity. The higher the porosity, the higher the fluid content, so the porosity is an important parameter for evaluating fluid performance. The void porosity κ _{b is} generally provided by the fluid filter 100 supplier. If not provided, the present invention proposes a method for obtaining a Δ P _box by performing a quadratic linear regression on the value of the F-flow of the complex array fluid. = f ( Q ), and then the void porosity κ _{b is} obtained by the formula (2). That is, the change of the fluid F flow rate will affect the change of the empty shell pressure difference Δ P _box , and the change of the fluid F flow rate and the pressure difference conforms to the relationship of the quadratic linear equation. The filter core porosity κ _f and the impurity porosity κ _p can be obtained in a similar manner by combining the above formulas (3) and (4).

The above-mentioned transmission formula (6) can obtain the initial impurity accumulation amount. However, in order to estimate the amount of impurities accumulated at a certain estimated time, an estimation method needs to be introduced. Please continue to refer to Figure 4. 4 is a schematic diagram showing the state of change of impurities accumulated in a fluid filter over time using a Kalman estimator 500 in an embodiment of the present invention.

In Fig. 4, the initial impurity accumulation amount obtained by the equation (6) and the measured initial pressure difference are substituted into the Kalman estimator 500. Continuation, sequential: Calculate the Jacobian matrix, pre-estimate the state equation, pre-estimate the error covariance, calculate the Jacobian matrix, calculate the Kalman gain, update the pre-estimated state of the measured value, update the error covariance matrix, etc., and update The pre-estimated state and the updated error covariance matrix are substituted into the original pre-estimated state equation and the pre-estimated error covariance, and the repeating steps are performed. Thereby, the state in which the impurities in the fluid filter 100 are accumulated over time can be obtained.

Please continue to refer to Figures 5A and 5B. Figure 5A is shown in a fluid (oil D100), the predicted and actual pressure difference as a function of fluid flow; Figure 5B is shown in another fluid (oil B100), the predicted and actual pressure difference A graph of changes with fluid flow. 5B by the first and second FIG. 5A, the present invention can be seen that a plurality of sets of values of the secondary fluid flow F linear regression manner, the shell of the simulated differential pressure △ P _box with the fluid flow rate variation, and the measured The actual empty shell pressure difference Δ P _{box is} quite consistent with the change of fluid flow rate, and can be applied to different kinds of fluids, and has wide applicability.

Please continue to refer to Figure 6. Figure 6 is a graph showing the predicted and actual pressure difference as a function of fluid flow in a fluid containing impurities (oil B20). From Fig. 6, it can be seen that the method for quadratic linear regression of the value of the flow rate of the complex array fluid F to obtain the pressure difference is applicable to the presence of the filter element and The state of accumulation of impurities. As shown in Fig. 6, the pressure difference Δ P includes a comprehensive consideration of the pressure difference Δ P _box , the filter pressure difference Δ P _filter , and the impurity pressure difference Δ P _P . More specifically, the pressure difference Δ P is the sum of the empty case pressure difference Δ P _box , the filter element pressure difference Δ P _{filter ,} and the impurity pressure difference Δ P _P . In FIG. 6, it can be seen the difference between simulation and actual pressure △ P of the fluid flow rate variation curve with good agreement.

Please refer to Table 1 below. The series shows the error percentage of the simulated and actual pressure difference Δ P for different types of fluid F (oil) in different states (including accumulation of impurities or no accumulation of impurities) and different fluid F flow rates. It can be seen that the error is within 10%.

Please continue to refer to Figures 7 through 10. Fig. 7 is a graph showing the predicted and actual pressure difference with time when the simulated impurity is added from 1g to 6g under the condition of fluid flow rate of 2l/min; Fig. 8 is shown in the case of fluid flow rate of 2l/min, simulation When the impurity is added from 1g to 6g, the predicted and actual impurity accumulation amount changes with time; the ninth figure is shown in the fluid flow rate of 2l/min, when the simulated impurity is added from 1g to 6g, the predicted and actual impurity concentration The time change graph; Fig. 10 is a graph showing the error variation between the predicted and actual impurity accumulation amount when the simulated impurity is added from 1g to 6g under the condition of fluid flow rate of 2l/min.

With the operational model of the fluid filter 100 established by the present invention, combined with the application of the Kalman estimator 500, accurate estimation of the accumulation of impurities over time can be made. To illustrate the accuracy of the fluid filter anomaly detection method of the present invention, the amount of impurities accumulated from 1 g to 6 g was simulated by manually adding impurities of 1 g to 6 g. In Fig. 7, it can be seen that the pressure difference gradually increases with the amount of accumulated impurities, and finally converges when the accumulated amount of impurities is 6 g. In Fig. 8, showing the estimation by the Kalman estimator 500, the estimated change in the amount of impurities accumulated over time coincides with the change in the amount of actual impurities added over time. Finally, through the summary of FIG. 10, it can be found that the error of the cumulative amount of impurities predicted by the fluid filter abnormality detecting method of the present invention with time and the change of the actual impurity addition amount with time does not exceed 10%. Quite high accuracy.

In summary, in the fluid filter abnormality detecting method disclosed in the present invention, by measuring the fluid flow rate and the pressure difference, the operation model of the fluid filter is established, and the estimation of the Kalman estimator can be accurately obtained. The change in the amount of impurities accumulated over time, and the amount of impurities accumulated to determine whether a new fluid filter needs to be replaced. Thereby, the instability factor which is only known by measuring the pressure difference as a criterion for judgment is eliminated, and the judgment accuracy of the fluid filter abnormality detecting system of the present invention is effectively improved.

While the invention has been described above by way of example, the invention is not intended to be limited thereby, the scope of the invention is defined by the scope of the appended claims.

Claims (10)

A method for detecting an abnormality of a fluid filter, comprising: detecting a flow rate of a fluid in a fluid filter; detecting a pressure difference in the fluid filter; and determining a geometry of the fluid filter State, the physical properties of the fluid, the porosity of the fluid filter, an impurity density, the flow rate, and the pressure difference establish an operational model of the fluid filter; an initial impurity accumulation is obtained through the operational model; a Kalman estimator, estimating the state of accumulation of impurities in the fluid filter over time according to the initial impurity accumulation amount and an initial pressure difference; obtaining impurities in the fluid filter according to the change state Estimating the amount of impurities accumulated in one time, and comparing the accumulated amount of impurities with a preset value to determine whether the fluid filter operates abnormally; wherein the operational model of the fluid filter can have the following relationship Expression: Where m is the cumulative amount of impurities in the air filter, Δ P is the pressure difference, μ is the fluid viscosity coefficient, Q is the flow rate of the fluid, A is the cross-sectional area of the fluid filter, and κ _b is the porosity of the empty shell. κ _f is the filter core porosity, κ _p is the impurity porosity, ρ _p is the impurity density, L _b is the empty shell thickness, and L _f is the filter core thickness. The fluid filter abnormality detecting method according to claim 1, wherein the fluid is a fuel oil, an oil or an automatic transmission. oil. The fluid filter abnormality detecting method according to claim 1, wherein the pressure difference is formed by the fluid flowing through one of the fluid inlet of the fluid filter and one of the fluid outlets of the fluid filter. A pressure difference. The fluid filter abnormality detecting method according to claim 3, wherein the pressure difference comprises a shell pressure difference, a filter element pressure difference, and an impurity pressure difference. The method for detecting an abnormality of a fluid filter according to the first aspect of the invention, wherein after the fluid filter is abnormally operated, a warning sound, a warning light or a warning message is issued. The method for detecting an abnormality of a fluid filter according to claim 1, wherein the void porosity κ _b is obtained by performing a quadratic linear regression on the value of the complex array. A fluid filter anomaly detection system includes: a fluid filter in which a fluid flows; a flow detector that detects a flow of the fluid; and a differential pressure detector Detecting a pressure difference in the fluid filter; an analyzer depending on the geometry of the fluid filter, the physical properties of the fluid, the porosity of the fluid filter, an impurity density, The flow rate and the pressure difference establish an operational model of the fluid filter, and an initial impurity accumulation amount is obtained through the operation model; a Kalman estimator is estimated based on the initial impurity accumulation amount and an initial pressure difference Detecting a change state of impurities in the fluid filter over time; and a processor for obtaining an impurity accumulation amount of the impurity in the fluid filter at an estimated time according to the change state, and The accumulated amount of impurities is compared with a predetermined value to determine whether the fluid filter operates abnormally; wherein the operational model of the fluid filter can be expressed by the following relationship: Where m is the cumulative amount of impurities in the air filter, Δ P is the pressure difference, μ is the fluid viscosity coefficient, Q is the flow rate of the fluid, A is the cross-sectional area of the fluid filter, and κ _b is the porosity of the empty shell. κ _f is the filter core porosity, κ _p is the impurity porosity, ρ _p is the impurity density, L _b is the empty shell thickness, and L _f is the filter core thickness. The fluid filter anomaly detection system of claim 7, wherein the fluid is a fuel oil, an oil or an automatic transmission fluid. The fluid filter abnormality detecting system of claim 7, wherein the pressure difference comprises a void pressure difference, a filter element pressure difference, and an impurity pressure difference. The fluid filter anomaly detection system of claim 7, wherein the fluid filter is disposed on a marine vehicle, a land vehicle or a flight vehicle.