CN111365232A - Gear pump experiment platform and gear damage detection method - Google Patents
Gear pump experiment platform and gear damage detection method Download PDFInfo
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- CN111365232A CN111365232A CN202010231030.9A CN202010231030A CN111365232A CN 111365232 A CN111365232 A CN 111365232A CN 202010231030 A CN202010231030 A CN 202010231030A CN 111365232 A CN111365232 A CN 111365232A
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- F—MECHANICAL ENGINEERING; LIGHTING; HEATING; WEAPONS; BLASTING
- F04—POSITIVE - DISPLACEMENT MACHINES FOR LIQUIDS; PUMPS FOR LIQUIDS OR ELASTIC FLUIDS
- F04C—ROTARY-PISTON, OR OSCILLATING-PISTON, POSITIVE-DISPLACEMENT MACHINES FOR LIQUIDS; ROTARY-PISTON, OR OSCILLATING-PISTON, POSITIVE-DISPLACEMENT PUMPS
- F04C14/00—Control of, monitoring of, or safety arrangements for, machines, pumps or pumping installations
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- G01M13/00—Testing of machine parts
- G01M13/02—Gearings; Transmission mechanisms
- G01M13/021—Gearings
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01M—TESTING STATIC OR DYNAMIC BALANCE OF MACHINES OR STRUCTURES; TESTING OF STRUCTURES OR APPARATUS, NOT OTHERWISE PROVIDED FOR
- G01M13/00—Testing of machine parts
- G01M13/02—Gearings; Transmission mechanisms
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- G01—MEASURING; TESTING
- G01M—TESTING STATIC OR DYNAMIC BALANCE OF MACHINES OR STRUCTURES; TESTING OF STRUCTURES OR APPARATUS, NOT OTHERWISE PROVIDED FOR
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- F—MECHANICAL ENGINEERING; LIGHTING; HEATING; WEAPONS; BLASTING
- F04—POSITIVE - DISPLACEMENT MACHINES FOR LIQUIDS; PUMPS FOR LIQUIDS OR ELASTIC FLUIDS
- F04C—ROTARY-PISTON, OR OSCILLATING-PISTON, POSITIVE-DISPLACEMENT MACHINES FOR LIQUIDS; ROTARY-PISTON, OR OSCILLATING-PISTON, POSITIVE-DISPLACEMENT PUMPS
- F04C2270/00—Control; Monitoring or safety arrangements
- F04C2270/16—Wear
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Abstract
The invention relates to a gear pump experimental platform and a gear damage detection method, wherein the gear pump experimental platform comprises an oil tank, a flow divider, a gear pump, a pressure gauge, a ball valve, vibration sensors and an oil monitor, the oil tank is divided into four temporary storage chambers by partition plates, the flow divider is a four-flow divider, the four temporary storage chambers are respectively connected with the flow divider through hoses, the output ends of the flow dividers are connected with the input end of the gear pump, the number of the vibration sensors is three, the three vibration sensors are respectively arranged on three mutually vertical straight lines positioned on a shell of the gear pump, and the vibration sensors measure vibration signals of the gear pump; the liquid outlet port of the gear pump is connected with a pressure gauge and a ball valve, and the liquid outlet port of the ball valve is connected with the liquid inlet port of the oil tank; the oil monitor is used for measuring the oil granularity of the gear pump outlet port and the oil granularity in the four temporary storage chambers. The invention solves the problem that the detection and diagnosis result is inaccurate due to insufficient consideration of the working condition change of the gear pump in the existing method.
Description
Technical Field
The invention relates to the technical field of mechanical fault diagnosis, in particular to a gear pump experiment platform and a gear damage detection method.
Background
Gear pumps are rotary pumps that deliver or pressurize fluid by virtue of the change in working volume and movement created between a pump cylinder and a meshing gear. Two gears, pump body and front and back covers form two closed spaces, when the gears rotate, the space volume of the gear disengagement side is changed from small to large to form vacuum, liquid is sucked in, and the space volume of the gear engagement side is changed from large to small to squeeze the liquid into the pipeline. The suction chamber and the discharge chamber are separated by a meshing line of two gears. The discharge port pressure of the gear pump is completely dependent on the amount of resistance at the pump outlet.
Current gear pump is after using for a long time, and its inside part takes place wearing and tearing, and the inside particulate matter of gear pump increases, finally leads to the gear pump to destroy and can't use, then leads to station part stop work gently, then leads to the whole assembly line to scrap heavily. The gear pump volume is less, and life cycle is longer, carries out gear damage through the mode of dismouting and detects the shortcoming that has the cycle length, inefficiency, can not real-time detection. The existing equipment can not estimate the internal loss of the gear pump and can not predict, diagnose and early warn the trend of the fault.
Disclosure of Invention
Technical problem to be solved
In order to solve the problems in the prior art, the invention provides a gear pump experiment platform and a gear damage detection method, and designs a digital twin-based gear pump multi-wear working condition lubricating oil on-line monitoring circulation simulation experiment platform, so that the problems that the detection and diagnosis results are inaccurate due to insufficient consideration of working condition changes of a gear pump and the problems that the gear pump is worn inside and potential accidents cannot be pre-warned in the prior art are solved. The gear pump experimental platform and the gear damage detection method provided by the invention can synchronously and faithfully map the gear damage condition of the gear pump in real time and reflect the gear damage condition of the gear pump with high fidelity, when the operating condition of the gear pump changes, the virtual model can be updated in real time through interactive feedback between the virtual model and a physical entity, and the operating state of the gear pump is judged and analyzed through the model updated in real time, so that accurate damage detection and qualitative diagnosis results are provided.
The digital twin is a technical means which integrates multiple physical, multi-scale and multi-disciplinary attributes, has the characteristics of real-time synchronization, faithful mapping and high fidelity, and can realize the interaction and fusion of the physical world and the information world. The main idea of digital twinning is: establishing an initial high-fidelity virtual model of a physical entity; performing real-time interactive feedback on the virtual model calculation result and actual operation data of the physical entity, and performing fusion analysis; and the feedback analysis result is utilized to carry out iterative update on the virtual model, so that the virtual model can have more accurate judgment and prediction capability.
(II) technical scheme
In order to achieve the purpose, the invention adopts the main technical scheme that:
a gear pump experiment platform is designed, and comprises an oil tank, a flow divider, a gear pump, a pressure gauge, a ball valve, vibration sensors and an oil monitor, wherein the oil tank is divided into four temporary storage chambers by partition plates, the flow divider is a four-branch flow divider, the four temporary storage chambers are respectively connected with the flow divider through hoses, the output end of the flow divider is connected with the input end of the gear pump, the number of the vibration sensors is three, the three vibration sensors are respectively arranged on three mutually vertical straight lines positioned on a shell of the gear pump, and the vibration sensors measure gear vibration signals of the gear pump; the liquid outlet port of the gear pump is connected with a pressure gauge and a ball valve, and the liquid outlet port of the ball valve is connected with the liquid inlet port of the oil tank; the oil monitor is used for measuring the oil granularity of the gear pump liquid outlet port and the oil granularity in the four temporary storage chambers.
In the above scheme, connect through PVC steel wire hose, clamp and internal thread pagoda adapter between oil tank and the shunt, connect through PVC steel wire hose, clamp and flange pagoda adapter between shunt and the gear pump.
In the above scheme, one side electric connection inverter motor of gear pump, inverter motor is used for carrying out the speed governing to the gear pump, the gear pump passes through foot steel and rag bolt is fixed in ground.
The invention also provides a gear damage detection method, which applies the gear pump experiment platform and comprises the following steps:
step 1), a gear pump gear digital twinning model is adopted to perform simulation calculation on vibration response signals of gears in different running states, a gear pump gear normal running digital twinning database and a gear pump gear fault digital twinning database are constructed by utilizing a characteristic vector extracted from a simulation signal and oil monitoring information of a liquid outlet port of the gear pump, wherein the gear pump gear fault digital twinning database comprises digital twinning databases in three fault states of gear tooth breakage, tooth surface abrasion and bearing abrasion;
step 2), establishing a self-organizing mapping neural network by using each feature vector in the gear pump gear normal operation digital twin database obtained in the step 1) as a single neuron; setting an alarm threshold interval by using a 3 sigma principle aiming at different eigenvectors represented by different neurons, finally comparing the eigenvectors extracted from vibration signals actually measured by a gear pump experiment platform subjected to noise reduction treatment with the eigenvectors under the same working condition, and judging that the gear is damaged if the eigenvectors exceed the alarm threshold interval;
step 3), establishing a joint dictionary expressed by sparse codes and aiming at different damage states of the gear by utilizing the difference of characteristic vectors in the gear pump gear fault digital twin database obtained in the step 1); establishing a one-to-one mapping relation between a damage state and sparse coding through a joint dictionary; matching sparse codes with the minimum residual error of the characteristic vectors extracted from the actually measured vibration signals of the gear pump experiment platform subjected to noise reduction treatment by using a residual error minimum principle; and finally, obtaining the gear damage category and the damage degree through a one-to-one mapping relation between sparse coding and the damage state.
Preferably, in the step 1), a gear pump gear digital twin database is established, and the method comprises the following steps:
step 1.1), establishing a gear digital twin model of a gear pump;
step 1.2), gear pump gear vibration response signals under different running states are predicted by using the gear pump gear digital twin model obtained in the step 1.1), and feature vectors of the gear pump gear vibration response signals are extracted from the prediction results;
and step 1.3), establishing a gear pump gear normal operation digital twin database and a gear pump gear fault digital twin database by utilizing the characteristic vectors extracted in the step 1.2), and providing data support for damage detection and qualitative diagnosis.
Preferably, in the step 1), a gear digital twin database of the gear pump is established, and the method specifically comprises the following steps:
step 1.1), measuring the shape of the gear pump and geometric structure parameters of the gear, inquiring material characteristic parameters, and sensing initial working conditions/environmental parameters;
step 1.2), establishing a gear of the gear pump and a matched digital twin submodel according to the parameters and the physical action relation measured, inquired and sensed in the step 1.1);
step 1.3), combining the coordination relationship and interface cooperation among different submodels, establishing a multi-physical-field integrated simulation platform containing a plurality of submodels by using software, and fusing the submodels into a unified physical model;
step 1.4), monitoring real-time vibration signals, oil signals and working condition/environmental parameters of a gear of the gear pump in an actual operation process of the gear pump in a gear pump experiment platform;
step 1.5), real-time inputting working condition/environment parameters into a unified physical model;
step 1.6), carrying out simulation calculation on the gear real-time vibration signal and the oil liquid parameter of the gear pump by using the unified physical model obtained in the step 1.3);
step 1.7), carrying out noise reduction treatment on the actually measured vibration signal obtained in the step 1.4);
step 1.8), comparing the simulation calculation result of the unified physical model in the step 1.6) with the actual measurement result subjected to noise reduction processing in the step 1.7), and calculating the deviation of the simulation calculation result and the actual measurement result;
step 1.9), adjusting and correcting internal parameters of the unified physical model by using an extended Kalman filtering algorithm according to the deviation value calculated in the step 1.8), and obtaining a real-time synchronous gear pump gear digital twin model.
Preferably, in the step 1.1), the shape of the gear pump and the geometric parameters of the gear are obtained from a drawing file of the gear pump; the material characteristics at least comprise the grade and the mechanical property of the material used by the gear; the working condition/environment parameters comprise the working rotating speed, the inlet and outlet pressure and the flow of the gear pump.
Preferably, in the step 1.2), the physical action relationship at least comprises a contact force and moment of a gear pump driving gear and a driven gear, a coupling action relationship of a flow field and force and a relationship between an acting force and strain; the digital twin submodel at least comprises a structure dynamics model, a fluid force coupling model, a stress analysis model and a damage evolution model.
Preferably, the step 2) specifically comprises the following steps:
step 2.1), establishing a self-organizing mapping neural network on the basis of the gear pump gear digital twin database obtained in the step 1), wherein each feature vector in the digital twin database for normal operation of the gear pump gear is used as a single independent neuron in the self-organizing mapping neural network, and the self-organizing neural network is used as the basis for detecting gear damage of the gear pump;
step 2.2), aiming at different characteristic vectors represented by different neurons in the self-organizing mapping neural network, setting an alarm threshold interval of the characteristic vector by using a 3 sigma principle;
and 2.3) carrying out noise reduction treatment on actually measured vibration signals of the gear pump in the gear pump experimental platform, extracting a characteristic vector, comparing the characteristic vector with the characteristic vector under the same working condition in the self-organizing mapping neural network, and judging that the gear is damaged if the alarm threshold interval of the characteristic vector in the step 2.2) is exceeded.
Preferably, the step 3) specifically comprises the following steps:
step 3.1), analyzing the differences of gear pump gears in different damage states on the characteristic vectors by using the gear pump gear fault digital twin database obtained in the step 1) through a sparse coding method;
step 3.2), constructing a joint dictionary for different damage states of the gear of the damaged gear pump expressed by sparse codes by utilizing the difference obtained in the step 3.1), and establishing a one-to-one mapping relation between the sparse codes and the damage states;
step 3.3), extracting a characteristic vector after carrying out noise reduction treatment on an oil monitoring signal and a vibration signal which are actually measured by the gear pump in the gear pump experimental platform, and matching sparse codes with the minimum residual error of the characteristic vector extracted from the vibration signal which is actually measured by the gear pump experimental platform and is subjected to the noise reduction treatment by using a residual error minimum principle and the combined dictionary established in the step 3.2);
and 3.4) obtaining the gear damage type and the damage degree by adopting the sparse codes obtained in the step 3.3) and according to the one-to-one mapping relation between the sparse codes obtained in the step 3.2) and the damage state.
Preferably, in the step 3.3), a sparse code with the minimum residual error of the feature vector extracted from the vibration signal actually measured by the gear pump experiment platform after the noise reduction treatment is obtained, and a specific calculation formula is
In the formula, t is an actually measured vibration signal characteristic vector; d is a joint dictionary; y isiSparse coding for the ith damage state; i is 1, 2.. k, k is the number of lesion states.
(III) advantageous effects
1. According to the method, the reflection of the particulate matters in the lubricating oil transported by the gear pump and the vibration parameters of the pump body on the running state of the pump is researched, the damage detection and qualitative diagnosis are carried out by utilizing a digital twin model of the gear pump, the vibration data of the gear pump during working is obtained through the vibration sensor, and the time sequence of the working parameters of the hydraulic oil is obtained through oil monitoring, so that the defects that the model is too single and the working condition change is not considered enough in the conventional gear pump gear fault diagnosis method are overcome; the gear pump gear digital twin model comprises a plurality of submodels considering different physical effects, and internal parameters of the digital twin submodels are continuously adjusted and corrected through real-time comparison between a calculation result of the digital twin model and an actually measured vibration signal and an oil liquid signal of the gear pump gear, so that the working condition change of the gear pump gear can be tracked in real time.
2. When the gear pump gear is subjected to damage detection and qualitative diagnosis, a gear pump gear normal operation digital twin database and a gear pump gear fault digital twin database are established, the damage detection and qualitative diagnosis work of the gear is completed through the combination of the databases and an intelligent algorithm, so that the real-time performance of the gear damage detection and qualitative diagnosis is guaranteed, and the generation of major accidents is avoided through timely damage discovery.
Drawings
Fig. 1 is a schematic structural diagram of a gear pump experimental platform according to an embodiment of the present invention;
fig. 2 is a schematic flow chart of a gear damage detection method according to an embodiment of the present invention;
fig. 3 is a schematic diagram of a specific application of the gear damage detection method according to the embodiment of the present invention.
In the figure: the device comprises an oil tank 1, a flow divider 2, a gear pump 3, a pressure gauge 4, a ball valve 5 and a vibration sensor 6.
Detailed Description
For the purpose of better explaining the present invention and to facilitate understanding, the present invention will be described in detail by way of specific embodiments with reference to the accompanying drawings.
As shown in figure 1, the invention provides a gear pump experimental platform, which comprises an oil tank 1, a flow divider 2, a gear pump 3, a pressure gauge 4, a ball valve 5, vibration sensors 6 and an oil monitor, wherein the oil tank 1 is divided into four temporary storage chambers by partition plates, the flow divider 2 is a one-to-four flow divider, the four temporary storage chambers are connected with the flow divider 2 through four hoses, the output end of the flow divider 2 is connected with the input end of the gear pump 3, the number of the vibration sensors 6 is three, the three vibration sensors 6 are respectively arranged on three mutually vertical straight lines of a shell of the gear pump 3 (the three vibration sensors 6 are respectively positioned in the radial vertical direction, the radial horizontal direction and the axial direction), a liquid outlet port of the gear pump 3 is connected with the pressure gauge 4 and the ball valve 5, a liquid outlet port of the ball valve 5, the fluid particle degree and four temporary storage chamber interior fluid particle degrees of 3 liquid outlet ports of gear pump are measured to the fluid monitor, through PVC steel wire hose between oil tank 1 and the shunt 2, clamp and internal thread pagoda adapter are connected, through PVC steel wire hose between shunt 2 and the gear pump 3, clamp and flange pagoda adapter are connected, one side electric connection inverter motor of gear pump 3 for carry out the speed governing to gear pump 3, gear pump 3 is fixed in ground through foot steel and rag bolt.
The gear pump experimental platform disclosed by the invention has the following working principle: detect four interior fluid granularities of temporary storage chamber in the oil tank 1 through the fluid monitor, flow into in the oil tank 1 behind shunt 2, gear pump 3, manometer 4 and ball valve 5 with the minimum fluid of granularity, fluid circulation flows, treats that circulation system operation stable vibration sensor 6 measures gear pump gear vibration signal, and the fluid monitor measures fluid data.
In the embodiment of the present invention, the volume of each temporary storage chamber of the fuel tank 1 is 60L, and 240L is total. Considering that oil with different properties is needed to circulate so as to observe the working states of the gear pump 3 under different states, the inside of the oil tank 1 is arranged in a grid mode, and each temporary storage chamber is provided with a separate inlet and outlet.
In the embodiment of the invention, the gear pump 3 is a 2CG-2 gear pump, and mainly comprises a pump body, a pump cover, a gear, a shaft, a bearing, a seal and the like. The pump body is made of iron casting or steel casting, and the gear shaft is made of special steel. The shaft end is sealed by adopting a mechanical seal, so that the pump has good self-absorption performance, and a moving part in the pump realizes lubrication by utilizing conveyed liquid; the detailed parameters of the 2CG-2 gear pump are shown in Table 1 below:
TABLE 12 CG-2 Gear Pump parameters
| Gear pump type | 2CG-2/0.6 |
| Flow rate L/min | 33.3 |
| Discharge pressure MPa | 0.33 |
| Height m of |
3 |
| Bore mm | 25 |
| Rated speed r/min | 1420 |
| Motor model | YVF2-90L-4 |
| Motor power KW | 1.5 |
In the embodiment of the invention, the model of the variable frequency motor is YVF2-90L-4, and the parameters of the variable frequency motor are as follows:
TABLE 2 YVF2-90L-4 inverter Motor parameters
| Model number | YVF2-90L-4 |
| Nominal power KW | 1.5 |
| Nominal current A | 3.8 |
| Rated torque n.m | 9.5 |
| Weight Kg | 27 |
The invention also provides a gear damage detection method, which applies the gear pump experiment platform and comprises the following steps as shown in figures 2-3:
step 1), a gear pump gear digital twinning model is adopted to perform simulation calculation on vibration response signals of gears in different running states, a gear pump gear normal running digital twinning database and a gear pump gear fault digital twinning database are constructed by utilizing a characteristic vector extracted from a simulation signal and oil monitoring information of a liquid outlet port of the gear pump, wherein the gear pump gear fault digital twinning database comprises digital twinning databases in three fault states of gear tooth breakage, tooth surface abrasion and bearing abrasion;
step 2), establishing a self-organizing mapping neural network by using each feature vector in the gear pump gear normal operation digital twin database obtained in the step 1) as a single neuron; setting an alarm threshold interval by using a 3 sigma principle aiming at different eigenvectors represented by different neurons, finally comparing the eigenvectors extracted from vibration signals actually measured by a gear pump experiment platform subjected to noise reduction treatment with the eigenvectors under the same working condition, and judging that the gear is damaged if the eigenvectors exceed the alarm threshold interval;
step 3), establishing a joint dictionary expressed by sparse codes and aiming at different damage states of the gear by utilizing the difference of characteristic vectors in the gear pump gear fault digital twin database obtained in the step 1); establishing a one-to-one mapping relation between a damage state and sparse coding through a joint dictionary; matching sparse codes with the minimum residual error of the characteristic vectors extracted from the actually measured vibration signals of the gear pump experiment platform subjected to noise reduction treatment by using a residual error minimum principle; and finally, obtaining the gear damage category and the damage degree through a one-to-one mapping relation between sparse coding and the damage state.
In the above method for detecting gear damage, the step 1) is a specific step of establishing a gear digital twinning database of a gear pump, and the step 1) specifically includes the following steps:
step 1.1), establishing a gear digital twin model of a gear pump;
step 1.2), measuring geometric structure parameters of the gear pump and the gear thereof, inquiring material characteristic parameters, and sensing initial working condition/environment parameters. The geometric structure parameters of the gear pump and the gears thereof can be obtained from a drawing file of the gear pump; the material characteristics at least comprise the grade and the mechanical property of the material used by the gear of the gear pump; the working condition/environment parameters comprise the working rotating speed, the inlet and outlet pressure and the flow of the gear pump;
step 1.3), establishing a digital twin submodel of the gear pump according to the parameters measured, inquired and sensed in the step 1.1) and the physical action relation. The physical action relationship at least comprises the contact force and moment of a gear pump driving gear and a driven gear, the coupling action relationship between a flow field and force and the relationship between acting force and strain; the digital twin submodel at least comprises a structure dynamics model, a fluid force coupling model, a stress analysis model and a damage evolution model. Considering coordination relation and interface cooperation among different submodels, establishing a multi-physical-field integrated simulation platform containing a plurality of submodels by using software, and fusing the submodels into a unified physical model; the coordination relation and the interface coordination mean that different software and different languages are used when different digital twin submodels are established, so that different data types are generated, and when the submodels are fused, the different data types are coordinated and can be mutually converted; the process of fusing the sub-models into a unified physical model can adopt, but is not limited to adopt, the following methods: utilizing Isight software, calling Ansys or Abaqus to establish a fluid force coupling model and a stress analysis model of the gear, calculating stress field distribution of the gear pump, bringing the calculated stress field distribution result into a structural dynamics model embedded with a damage evolution model, solving, and finally simulating and calculating a gear vibration signal and an oil signal of the gear pump;
step 1.4), monitoring real-time vibration signals, oil signals and working conditions/environmental parameters of the gear pump in the actual operation process, inputting the signals into a unified physical model in real time, and performing simulation calculation on the real-time vibration signals and the oil signals of the gear pump by using the obtained unified physical model; after the noise reduction processing is carried out on the actually measured vibration signal and the oil liquid signal, the simulation calculation result of the unified physical model is compared with the noise reduction processing result, the deviation of the unified physical model and the oil liquid signal is calculated, and the internal parameters of the unified physical model are adjusted and corrected by utilizing an extended Kalman filtering algorithm according to the deviation value, so that a gear pump gear digital twin model capable of being synchronized in real time is obtained;
and step 1.5), simulating gear vibration response signals and oil signals of the gear pump in different states by using the gear digital twin model of the gear pump obtained in the previous step, and extracting characteristic vectors of the gear vibration response signals and the oil signals from simulation results. And establishing a gear pump gear normal operation digital twin database and a gear pump gear fault digital twin database by using the extracted characteristic vectors, and providing data support for damage detection and qualitative diagnosis.
In the above method for detecting gear damage, the step 2) is a specific step of gear damage detection of a gear pump, and the step 2) specifically includes the following steps:
step 2.1), establishing a self-organizing mapping neural network on the basis of the gear pump gear digital twin database obtained in the step 1), wherein each feature vector in the digital twin database for normal operation of the gear pump gear is used as a single independent neuron in the self-organizing mapping neural network, and the self-organizing neural network is used as the basis for detecting gear damage of the gear pump;
step 2.2), aiming at different characteristic vectors represented by different neurons in the self-organizing mapping neural network, setting an alarm threshold interval of the characteristic vector by using a 3 sigma principle;
and 2.3) carrying out noise reduction treatment on actually measured vibration signals of the gear pump in the gear pump experimental platform, extracting a characteristic vector, comparing the characteristic vector with the characteristic vector under the same working condition in the self-organizing mapping neural network, and judging that the gear is damaged if the alarm threshold interval of the characteristic vector in the step 2.2) is exceeded.
In the above method for detecting gear damage, the step 3) is a specific step of qualitative diagnosis of gear damage of the gear pump, and the step 3) specifically includes the following steps:
step 3.1), analyzing the differences of gear pump gears in different damage states on the characteristic vectors by using the gear pump gear fault digital twin database obtained in the step 1) through a sparse coding method;
step 3.2), constructing a joint dictionary for different damage states of the gear of the damaged gear pump expressed by sparse codes by utilizing the difference obtained in the step 3.1), and establishing a one-to-one mapping relation between the sparse codes and the damage states;
and 3.3) carrying out noise reduction on oil monitoring signals and vibration signals actually measured by the gear pump in the gear pump experimental platform, extracting a characteristic vector, and matching sparse codes with the minimum residual error of the characteristic vector extracted from the vibration signals actually measured by the gear pump experimental platform subjected to the noise reduction treatment by using a residual error minimum principle and the joint dictionary established in the step 3.2). In the step 3.3), sparse codes with minimum residual errors of the characteristic vectors extracted from the actually measured vibration signals of the gear pump experiment platform subjected to noise reduction treatment are obtained, and a specific calculation formula is
In the formula, t is an actually measured vibration signal characteristic vector; d is a joint dictionary; y isiSparse coding for the ith damage state; i is 1, 2.. k, k is the number of lesion states.
And 3.4) obtaining the gear damage type and the damage degree by adopting the sparse codes obtained in the step 3.3) and according to the one-to-one mapping relation between the sparse codes obtained in the step 3.2) and the damage state.
While the present invention has been described with reference to the particular embodiments illustrated in the drawings, which are meant to be illustrative only and not limiting, it will be apparent to those of ordinary skill in the art in light of the teachings of the present invention that numerous modifications can be made without departing from the spirit and scope of the invention as defined by the appended claims.
Claims (10)
1. A gear pump experiment platform comprises an oil tank (1), a flow divider (2), a gear pump (3), a pressure gauge (4), a ball valve (5), a vibration sensor (6) and an oil liquid monitor, and is characterized in that the oil tank (1) is internally divided into four temporary storage chambers by partition plates, the flow divider (2) is a one-to-four flow divider, the four temporary storage chambers are respectively connected with the flow divider (2) through hoses, the output end of the flow divider (2) is connected with the input end of the gear pump (3), the number of the vibration sensors (6) is three, the three vibration sensors (6) are respectively arranged on three mutually vertical straight lines positioned on the shell of the gear pump (3), and the vibration sensors (6) measure gear vibration signals of the gear pump (3); a liquid outlet port of the gear pump (3) is connected with a pressure gauge (4) and a ball valve (5), and a liquid outlet port of the ball valve (5) is connected with a liquid inlet port of the oil tank (1); the oil monitor is used for measuring the oil granularity of the liquid outlet port of the gear pump (3) and the oil granularity in the four temporary storage chambers.
2. Gear pump experimental platform according to claim 1, characterized in that, connect through PVC steel wire hose, clamp and internal thread pagoda adapter between oil tank (1) and shunt (2), connect through PVC steel wire hose, clamp and flange pagoda adapter between shunt (2) and gear pump (3).
3. A gear damage detection method is characterized in that the gear pump experiment platform of claim 1 or 2 is applied, and the detection method comprises the following steps:
step 1), a gear pump gear digital twinning model is adopted to perform simulation calculation on vibration response signals of gears in different running states, a gear pump gear normal running digital twinning database and a gear pump gear fault digital twinning database are constructed by utilizing a characteristic vector extracted from a simulation signal and oil monitoring information of a liquid outlet port of the gear pump, wherein the gear pump gear fault digital twinning database comprises digital twinning databases in three fault states of gear tooth breakage, tooth surface abrasion and bearing abrasion;
step 2), establishing a self-organizing mapping neural network by using each feature vector in the gear pump gear normal operation digital twin database obtained in the step 1) as a single neuron; setting an alarm threshold interval by using a 3 sigma principle aiming at different eigenvectors represented by different neurons, finally comparing the eigenvectors extracted from vibration signals actually measured by a gear pump experiment platform subjected to noise reduction treatment with the eigenvectors under the same working condition, and judging that the gear is damaged if the eigenvectors exceed the alarm threshold interval;
step 3), establishing a joint dictionary expressed by sparse codes and aiming at different damage states of the gear by utilizing the difference of characteristic vectors in the gear pump gear fault digital twin database obtained in the step 1); establishing a one-to-one mapping relation between a damage state and sparse coding through a joint dictionary; matching sparse codes with the minimum residual error of the characteristic vectors extracted from the actually measured vibration signals of the gear pump experiment platform subjected to noise reduction treatment by using a residual error minimum principle; and finally, obtaining the gear damage category and the damage degree through a one-to-one mapping relation between sparse coding and the damage state.
4. A gear damage detection method according to claim 3, wherein in step 1), establishing a gear digital twinning database of the gear pump comprises the following steps:
step 1.1), establishing a gear digital twin model of a gear pump;
step 1.2), gear pump gear vibration response signals under different running states are predicted by using the gear pump gear digital twin model obtained in the step 1.1), and feature vectors of the gear pump gear vibration response signals are extracted from the prediction results;
and step 1.3), establishing a gear pump gear normal operation digital twin database and a gear pump gear fault digital twin database by utilizing the characteristic vectors extracted in the step 1.2), and providing data support for damage detection and qualitative diagnosis.
5. The method for detecting gear damage according to claim 4, wherein in the step 1), a gear digital twinning database of the gear pump is established, and the method specifically comprises the following steps:
step 1.1), measuring the shape of the gear pump and geometric structure parameters of the gear pump, inquiring material characteristic parameters, and sensing initial working conditions/environmental parameters;
step 1.2), establishing a gear of the gear pump and a matched digital twin submodel according to the parameters and the physical action relation measured, inquired and sensed in the step 1.1);
step 1.3), combining the coordination relationship and interface cooperation among different submodels, establishing a multi-physical-field integrated simulation platform containing a plurality of submodels by using software, and fusing the submodels into a unified physical model;
step 1.4), monitoring real-time vibration signals, oil signals and working conditions/environmental parameters of a gear pump gear in a gear pump experiment platform in the actual operation process;
step 1.5), real-time inputting working condition/environment parameters into a unified physical model;
step 1.6), carrying out simulation calculation on the gear real-time vibration signal and the oil liquid parameter of the gear pump by using the unified physical model obtained in the step 1.3);
step 1.7), carrying out noise reduction treatment on the actually measured vibration signal obtained in the step 1.4);
step 1.8), comparing the simulation calculation result of the unified physical model in the step 1.6) with the actual measurement result subjected to noise reduction processing in the step 1.7), and calculating the deviation of the simulation calculation result and the actual measurement result;
step 1.9), adjusting and correcting internal parameters of the unified physical model by using an extended Kalman filtering algorithm according to the deviation value calculated in the step 1.8), and obtaining a real-time synchronous gear pump gear digital twin model.
6. The method for detecting the gear damage as claimed in claim 5, wherein in the step 1.1), the shape of the gear pump and the geometric parameters of the gear thereof are obtained from a drawing file of the gear pump; the material characteristics at least comprise the grade and the mechanical property of the material used by the gear; the working condition/environment parameters comprise the working rotating speed, the inlet and outlet pressure and the flow of the gear pump.
7. The method for detecting gear damage according to claim 5, wherein in the step 1.2), the physical action relationship at least comprises a contact force and moment between a driving gear and a driven gear of the gear pump, a coupling action relationship between a flow field and force, and a relationship between an acting force and strain; the digital twin submodel at least comprises a structure dynamics model, a fluid force coupling model, a stress analysis model and a damage evolution model.
8. The gear damage detection method according to claim 3, wherein the step 2) specifically comprises the steps of:
step 2.1), establishing a self-organizing mapping neural network on the basis of the gear pump gear digital twin database obtained in the step 1), wherein each feature vector in the digital twin database for normal operation of the gear pump gear is used as a single independent neuron in the self-organizing mapping neural network, and the self-organizing neural network is used as the basis for detecting gear damage of the gear pump;
step 2.2), aiming at different characteristic vectors represented by different neurons in the self-organizing mapping neural network, setting an alarm threshold interval of the characteristic vector by using a 3 sigma principle;
and 2.3) carrying out noise reduction treatment on actually measured vibration signals of the gear pump in the gear pump experimental platform, extracting a characteristic vector, comparing the characteristic vector with the characteristic vector under the same working condition in the self-organizing mapping neural network, and judging that the gear is damaged if the alarm threshold interval of the characteristic vector in the step 2.2) is exceeded.
9. The gear damage detection method according to claim 3, wherein the step 3) specifically comprises the steps of:
step 3.1), analyzing the differences of gear pump gears in different damage states on the characteristic vectors by using the gear pump gear fault digital twin database obtained in the step 1) through a sparse coding method;
step 3.2), constructing a joint dictionary for different damage states of the gear of the damaged gear pump expressed by sparse codes by utilizing the difference obtained in the step 3.1), and establishing a one-to-one mapping relation between the sparse codes and the damage states;
step 3.3), extracting a characteristic vector after carrying out noise reduction treatment on the oil monitoring signal and the vibration signal which are actually measured in the gear pump experiment platform, and matching sparse codes with the minimum residual error of the characteristic vector extracted from the vibration signal which is actually measured in the gear pump experiment platform and is subjected to the noise reduction treatment by utilizing a residual error minimum principle and the joint dictionary established in the step 3.2);
and 3.4) obtaining the gear damage type and the damage degree by adopting the sparse codes obtained in the step 3.3) and according to the one-to-one mapping relation between the sparse codes obtained in the step 3.2) and the damage state.
10. The method for detecting gear damage according to claim 9, wherein in step 3.3), the sparse code with the minimum residual error of the eigenvector extracted from the vibration signal actually measured from the gear pump experiment platform subjected to noise reduction treatment is obtained, and the specific calculation formula is
In the formula, t is an actually measured vibration signal characteristic vector; d is a joint dictionary; y isiSparse coding for the ith damage state; i is 1, 2.. k, k is the number of lesion states.
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