CN117191387B

CN117191387B - Oil seal detection method and system

Info

Publication number: CN117191387B
Application number: CN202311164247.2A
Authority: CN
Inventors: 诸立亚; 袁丽芬
Original assignee: KUNSHAN KENBO SEALING SCIENCE AND TECHNOLOGY CO LTD
Current assignee: KUNSHAN KENBO SEALING SCIENCE AND TECHNOLOGY CO LTD
Priority date: 2023-09-11
Filing date: 2023-09-11
Publication date: 2024-03-08
Anticipated expiration: 2043-09-11
Also published as: CN117191387A

Abstract

Embodiments of the present disclosure provide an oil seal detection system and method, the system including at least a test cavity, a test shaft, an oil seal installation tool, a pressing device, and a processor configured to: sending an installation instruction to the oil seal installation tool and the pressing device, wherein the installation instruction indicates that the oil seal piece is installed at a designated position of the test cavity; in response to detecting that the oil seal is installed at the specified location, determining an oil seal detection scheme based on structural features and material features of the oil seal; detecting an oil sealing piece based on an oil sealing detection scheme, and collecting at least one of wind power data and sound data in the detection process to determine a first detection result; and evaluating the factory quality of the oil sealing piece based on the first detection result.

Description

Oil seal detection method and system

Technical Field

The present disclosure relates to the field of mechanical technologies, and in particular, to an oil seal detection method and system.

Background

Oil-tight seals are mechanical components commonly used in transmission systems that prevent leakage of liquid material from the transmission system from the gap and also prevent foreign objects from entering the transmission system. Oil seals for mechanical devices typically fail at high temperatures and/or pressures exceeding their withstand thresholds; when the oil sealing piece leaves the factory to be detected, the service life of the oil sealing piece can be reasonably evaluated through high temperature and high pressure resistance tests, fatigue tests and the like.

In order to solve the problems, CN111323223B provides an accelerated life testing device and a testing method for an oil seal for a new energy automobile, wherein the oil seal to be tested is arranged outside a bearing and fixedly arranged on the side wall of an oil seal test box; measuring data of the motor rotation speed changing along with time in an actual use environment, editing the data into a rotation speed-time curve of the motor changing along with time and inputting the curve into a control unit so as to control the rotation speed of the motor; the data display and storage unit are used to display and store life time, cycle times and related environmental information. However, the testing method is long and complicated, and the labor cost and the time cost are high.

Therefore, it is desirable to provide an oil seal detection system and method to realize detection of sealing elements under different simulated working conditions, and improve test efficiency.

Disclosure of Invention

One or more embodiments of the present disclosure provide an oil seal detection system. The system comprises: test cavity, test axle, oil blanket installation frock, biasing means and treater, the treater is configured to: sending an installation instruction to the oil seal installation tool and the pressing device, wherein the installation instruction indicates that an oil seal piece is installed at a designated position of the test cavity; in response to detecting that the oil seal is mounted in the specified position, determining an oil seal detection scheme based on structural features and material features of the oil seal, the oil seal detection scheme including a first detection instance including a tightness detection of the oil seal in a first specific state including the test shaft at a specific rotational speed within a first preset time period; detecting the oil sealing piece based on the oil sealing detection scheme, and collecting at least one of wind power data and sound data in the detection process to determine a first detection result; and evaluating the delivery quality of the oil sealing piece based on the first detection result.

One or more embodiments of the present disclosure provide an oil seal detection method. The method comprises the following steps: sending an installation instruction to the oil seal installation tool and the pressing device, wherein the installation instruction indicates that the oil seal piece is installed at a designated position of the test cavity; in response to detecting that the oil seal is mounted in the specified position, determining an oil seal detection scheme based on structural features and material features of the oil seal, the oil seal detection scheme including a first detection instance including a tightness detection of the oil seal in a first specific state including a test shaft at a specific rotational speed for a first preset period of time; detecting the oil sealing piece based on the oil sealing detection scheme, and collecting at least one of wind power data and sound data in the detection process to determine a first detection result; and evaluating the delivery quality of the oil sealing piece based on the first detection result.

Drawings

The present specification will be further elucidated by way of example embodiments, which will be described in detail by means of the accompanying drawings. The embodiments are not limiting, in which like numerals represent like structures, wherein:

FIG. 1 is a schematic diagram of an oil seal detection system according to some embodiments of the present disclosure;

FIG. 2 is an exemplary flow chart of an oil seal detection method according to some embodiments of the present description;

FIG. 3 is an exemplary flow chart for evaluating the shipping quality of an oil seal according to some embodiments of the present description;

FIG. 4 is a model schematic diagram of a life prediction model shown in accordance with some embodiments of the present description.

Detailed Description

In order to more clearly illustrate the technical solutions of the embodiments of the present specification, the drawings that are required to be used in the description of the embodiments will be briefly described below. It is apparent that the drawings in the following description are only some examples or embodiments of the present specification, and it is possible for those of ordinary skill in the art to apply the present specification to other similar situations according to the drawings without inventive effort. Unless otherwise apparent from the context of the language or otherwise specified, like reference numerals in the figures refer to like structures or operations.

It will be appreciated that "system," "apparatus," "unit" and/or "module" as used herein is one method for distinguishing between different components, elements, parts, portions or assemblies at different levels. However, if other words can achieve the same purpose, the words can be replaced by other expressions.

As used in this specification and the claims, the terms "a," "an," "the," and/or "the" are not specific to a singular, but may include a plurality, unless the context clearly dictates otherwise. In general, the terms "comprises" and "comprising" merely indicate that the steps and elements are explicitly identified, and they do not constitute an exclusive list, as other steps or elements may be included in a method or apparatus.

A flowchart is used in this specification to describe the operations performed by the system according to embodiments of the present specification. It should be appreciated that the preceding or following operations are not necessarily performed in order precisely. Rather, the steps may be processed in reverse order or simultaneously. Also, other operations may be added to or removed from these processes.

The existing oil seal detection technology is difficult to simulate the loss of an oil seal in an actual environment and ensure the detection efficiency. CN111323223B simulates the actual use environment only by simulating the motor rotation speed, the detection process is long and complicated, and the application is mainly used for detecting the oil seal of the transmission system of the new energy automobile, and the application range is narrow.

In view of this, in some embodiments of the present disclosure, it is desirable to provide an oil seal detection system and method, which can preset different detection schemes according to structural features and material features of different oil seals, implement automatic fatigue detection of the seals under different simulated working conditions, and perform life prediction through a life prediction model, so as to shorten test time and improve test efficiency.

Fig. 1 is a schematic diagram of an oil seal detection system according to some embodiments of the present disclosure.

As shown in fig. 1, the oil seal detection system 100 may include a test chamber 110, a test shaft 111, an oil seal installation tool 120, a pressing device 130, a processor 140, and the like.

The test chamber 110 refers to a chamber for performing an oil seal detection test. The material of the test cavity can be steel or cast iron. The test cavity can be used for simulating a cavity for storing oil when the oil sealing piece is actually used.

The test shaft 111 refers to a rotatable shaft mounted in the test chamber. The test shaft can be made of carbon structural steel, cast iron and the like. The test shaft may be used to simulate a rotating shaft that is contacted by an oil seal in actual use.

The oil seal installation tool 120 refers to a tool for installing an oil seal, and may include, for example, a screw, a bolt, a guide rod, etc.

The pressing means 130 refers to a pressing means for assisting in the installation of the oil seal. For example, it may include a platen, a press, etc.

Processor 140 may obtain data and/or information (e.g., oil temperature, oil pressure data, etc.) from other devices of the oil seal detection system (e.g., interactive screens, temperature sensors, pressure sensors, etc.). The processor may control the drive motor, the temperature control unit, the pressure control unit, etc. to execute program instructions based on the data, information, and/or processing results (e.g., oil seal detection scheme, etc.) to perform the oil seal detection method.

In some embodiments, the processor 140 may send an installation instruction to the oil seal installation tool and the pressing device; determining an oil seal detection scheme; determining a detection result; and evaluating the delivery quality of the oil seal. For more on the above embodiments reference is made to fig. 2 and the related description.

An oil seal is a device for sealing liquid or gas, and may be used for sealing a rotating shaft or a rotating device. The oil seal mainly comprises a sealing lip, a spring and a metal shell.

The sealing lip is the main sealing part of the oil seal, is usually made of soft rubber or elastic material, and has good sealing performance and abrasion resistance. The seal lip is attached to the rotary shaft, and a sealing effect is produced by contact with the shaft surface.

The back of the oil seal may be provided with a spring for providing pressure against the sealing lip, ensuring that the sealing lip maintains good contact with the rotating shaft. The choice and design of the spring can be determined according to the actual application requirements.

The support structure of the oil seal may be a metal housing, may be made of a metal or alloy material, and provides strength and rigidity support to the oil seal. The metal housing is generally sized and shaped to match the seal bore of the device.

The structure and working principle of the oil seal can be changed and different to some extent, depending on the practical application requirements and the required sealing performance and durability. Different types of oil seals are suitable for different application fields and working conditions, such as automobiles, industrial machinery, hydraulic transmission and the like.

In some embodiments, the oil seal detection system 100 may further include a temperature sensor 150-1, a pressure sensor 150-2, a temperature control unit 150-3, and a pressure control unit 150-4.

The temperature sensor 150-1 refers to a sensor for monitoring the oil temperature of the oil sealing member. In some embodiments, the temperature sensor may include a thermistor thermometer, an infrared heat detector.

The pressure sensor 150-2 refers to a sensor that monitors the oil pressure of the oil seal. In some embodiments, the pressure sensor may include a piezoresistive pressure sensor, a piezoelectric pressure sensor, or the like.

The temperature control unit 150-3 and the pressure control unit 150-4 may be used to control the oil temperature and the oil pressure of the oil seal, respectively.

In some embodiments, the oil seal detection system 100 may also include a dust and sand spray component 160-1, a water vapor spray component 160-2. The dust and sand spraying component and the water vapor spraying component are respectively used for spraying dust and sand and water vapor so as to simulate the use environment of the oil sealing piece in reality.

In some embodiments, the processor 140 may control the dust and sand spraying part and the water vapor spraying part to spray the oil seal.

In some embodiments, the oil seal detection system 100 may also include wind sensors, acoustic sensors, protective boxes, drive motors, interactive screens, and the like.

Wind sensors are sensors used to collect wind force data during the detection process.

The sound sensor is a sensor for collecting sound data during detection.

A protective enclosure refers to equipment used to protect personnel, equipment, and the environment.

The drive motor refers to a motor for driving the test equipment, and may be used to drive the test shaft to rotate, for example.

The interactive screen refers to a screen for interacting with the oil seal detection system 100 and displaying related contents, and for example, may be used to input a preset scheme for starting the driving motor, and the preset scheme may include power, rotation speed, etc. of the motor.

In some embodiments of the present disclosure, based on the oil seal detection system 100, the processor may determine different detection schemes according to different oil seals, detect by simulating an actual environment, evaluate the service life based on the detection result, and may perform life detection on the oil seals efficiently and with pertinence.

It should be noted that the above description of the oil seal detection system 100 and its modules is for convenience only, and is not intended to limit the present disclosure to the scope of the illustrated embodiments. It will be appreciated by those skilled in the art that, given the principles of the system, various modules may be combined arbitrarily or a subsystem may be constructed in connection with other modules without departing from such principles. In some embodiments, the test chamber 110, the test shaft 111, the oil seal installation tool 120, and the pressing device 130 disclosed in fig. 1 may be different modules in one system, or may be one module to implement the functions of two or more modules. For example, each module may share one memory module, or each module may have a respective memory module. Such variations are within the scope of the present description.

Fig. 2 is an exemplary flow chart of an oil seal detection method according to some embodiments of the present description. As shown in fig. 2, the process 200 includes the following steps. In some embodiments, the process 200 may be performed by a processor.

Step 210, sending an installation instruction to the oil seal installation tool and the pressing device, wherein the installation instruction indicates that the oil seal is installed at a designated position of the test cavity.

The installation instruction refers to an operation instruction issued by the processor for installing the oil seal. For example, the installation instructions may include an installation step, an installation location, and the like. In some embodiments, the installation instruction may be determined by querying a preset installation table according to a model of the oil seal, where the preset installation table includes the model of the oil seal and the corresponding installation instruction.

The specified position refers to a position where the oil seal is normally installed in the installation instruction. For example, the designated location may be at the interface of the test shaft and the test cavity.

In some embodiments, the oil seal installation tool may preliminarily fix the oil seal at the specified position based on the installation instruction, and then the pressing device may press the oil seal based on the installation instruction, so that the oil seal is installed in place.

In response to detecting that the oil seal is installed in the designated location, an oil seal detection scheme is determined based on structural and material characteristics of the oil seal, step 220.

Detecting that the oil seal is mounted at the specified position means detecting that the oil seal is mounted at the specified position by a sensor or a camera or the like acquisition device.

The oil seal detection scheme refers to a detection scheme for detecting the tightness of an oil seal. The oil seal detection scheme may include one or more first detection instances.

The first test case is a test case in which the pointer seals an oil seal under certain environmental parameters. The environmental parameter may include at least one of oil temperature, oil pressure, and rotational speed.

In some embodiments, the first detection instance may include a tightness detection of the oil seal in a first specific state.

The first specific state refers to the state of the apparatus in the test associated with the oil seal. The first specific state includes at least: the test shaft is at a specific rotational speed for a first preset period of time.

The first preset time period refers to a duration of the first detection instance.

In some embodiments, the first particular state may further include: the test shaft is at a preset rotating speed, and the oil temperature and the oil pressure of the oil sealing piece meet preset requirements.

The preset rotational speed refers to a preset test shaft rotational speed. For example, the preset rotational speed may be 3000r/min.

The preset requirement refers to a preset condition of oil temperature and oil pressure. For example, the preset requirement may be that the oil temperature is at 50 ℃ and the oil pressure is at 200 kPa.

By way of example only, the first detection example may be in particular the detection of the tightness of the oil seal under the following environmental parameters: the test shaft is at a rotating speed of 3000 r/min; the oil temperature was 50℃and the oil pressure was 200kPa for 10 hours.

In some embodiments of the present disclosure, the tightness of the oil seal in a specific environment may be tested by setting a preset rotation speed and a preset requirement, so as to obtain a detection result that is close to real.

In some embodiments, the oil seal detection scheme may further include one or more second detection instances.

The second detection example is a detection example of the sealing property of the oil seal performed for the case where the environment changes with time. In some embodiments, the second detection instance may include a tightness detection of the oil seal in a second specific state.

The second specific state refers to a state in which the device state changes with time. For example, the oil temperature may be gradually increased with time.

In some embodiments, the second specific state may include the test shaft being at a preset rotational speed sequence for a second preset time period and/or the oil temperature and oil pressure of the oil seal meeting preset sequence requirements for the second preset time period.

In some embodiments, the processor may determine the second detection instance based on vector similarity matching the detection target vector with the historical detection vectors. Wherein each historical detection vector has corresponding experimental conditions.

And the processor performs similarity matching on the detection target vector and the historical detection vector, and when the similarity is within a similarity threshold range, the experimental condition corresponding to the historical detection vector is selected as the experimental condition of the second detection example. The vector similarity may be cosine similarity or other various calculation methods, and the similarity threshold range may be set based on experience. Specific embodiments regarding vector matching may be similar to those described in connection with fig. 3.

The preset rotational speed sequence refers to a time-varying rotational speed sequence of the preset test shaft rotational speed. For example, the rotational speed is a rotational speed sequence in which the rotational speed is gradually changed with time in the order of 1000r/min, 2000r/min, 3000 r/min.

The second preset time period refers to a duration of the second detection instance.

The preset sequence requirement refers to a preset sequence of conditions of oil temperature and oil pressure. For example, the oil temperature is in a state in which the oil temperature sequence of 20 ℃, 25 ℃, 50 ℃ is gradually changed with time.

By way of example only, the second detection example may be in particular the detection of the tightness of the oil seal under the following environmental parameters: the rotation speed is evenly increased at a speed of 1000r/min every one hour, such as 0r/min,1000r/min and 2000r/min …; and/or the oil temperature is raised at a constant rate of 50% every one hour from 20 ℃, such as 20 ℃, 30 ℃, 45 ℃, …; this state lasts for 5h.

In some embodiments of the present disclosure, through presetting different rotation speed sequences and sequence requirements for oil temperature and oil pressure, conditions such as metal fatigue, thermal expansion and cold contraction, etc. of the oil seal member, which may be caused by frequent temperature or pressure changes, may be tested, so as to more comprehensively evaluate the quality of the oil seal member.

In some embodiments, the processor may determine the oil seal detection scheme based on structural and material characteristics of the oil seal in a variety of ways.

In some embodiments, the processor may determine the oil seal detection scheme by means of vector matching in the database based on the structural features and material features of the oil seal. In some embodiments, the processor may construct the first vector to be matched for the oil seal based on structural features, material features of the oil seal. The database comprises a plurality of first reference vectors, and each first reference vector has a corresponding oil seal detection scheme. The first reference vector is constructed based on the structural characteristics and the material characteristics of the oil seal with the tightness detection completed. In some embodiments, the processor may calculate the distance between the first reference vector and the first vector to be matched, respectively, and determine the spraying amount and the spraying frequency corresponding to the first vector to be matched. For more relevant embodiments of vector matching reference may be made to the corresponding description of fig. 3.

And 230, detecting the oil sealing piece based on the oil sealing detection scheme, and collecting at least one of wind power data and sound data in the detection process to determine a first detection result.

Wind data refers to data related to the wind of the environment during the detection process, such as wind power, wind direction, etc. In some embodiments, the wind data may be acquired by a wind sensor.

Sound data refers to data related to the sound of the environment during detection, such as loudness, frequency, etc. In some embodiments, the sound data may be acquired by a sound sensor.

The first detection result refers to the detection result obtained in the first detection example of the oil seal. For example, the first detection result may include: whether the oil seal is deformed or not, whether oil leakage exists or not, and the like.

In some embodiments, the processor may determine the first detection result by monitoring the oil temperature and the oil pressure in combination with wind data or sound data. For example, when the rotation speed of the test shaft is unchanged, the sound data detected by the sound sensor is changed to be greater than a certain sound change threshold (for example, when the sound loudness is changed to be greater than the loudness change threshold), the first detection result (for example, when the oil temperature and the oil pressure are respectively greater than the oil temperature and the oil pressure change threshold) can be obtained by combining the oil temperature and the oil pressure change judgment (for example, when the oil temperature and the oil pressure change are respectively greater than the oil temperature and the oil pressure change threshold), and the wind power data is the same.

Step 240, evaluating the factory quality of the oil seal based on the first detection result.

The factory quality refers to the quality index of the newly produced oil seal. In some embodiments, the factory quality may include factory life, etc.

In some embodiments, the processor may evaluate the factory quality of the oil seal based on the first detection result in a variety of ways. For example, if the average failure probability of the obtained first detection result reaches the preset failure threshold after the same type of oil seal produced in the same batch is subjected to multiple first detection examples, the type of oil seal produced in the batch is evaluated as a failed oil seal. Wherein the failure probability refers to the ratio of the number of failed oil seals to the total number of detected oil seals in the first detection example.

In some embodiments, the processor may evaluate the factory quality based on the first detection result and the second detection result, see the relevant description of fig. 3 for details. In some embodiments, the processor may further evaluate the factory quality in combination with the first detection result and the life prediction model, see the relevant description of fig. 4 for details.

In some embodiments of the present disclosure, through the above-mentioned oil seal detection method flowchart 200, the processor may implement making a corresponding detection scheme based on the characteristics of the oil seal, and detect the detection results of the oil seal in different states through adjustment of different parameters, thereby evaluating the factory quality based on the detection results, and improving the efficiency and accuracy of oil seal detection.

It should be noted that the above description of the process 200 is for illustration and description only, and is not intended to limit the scope of applicability of the present disclosure. Various modifications and changes to flow 200 will be apparent to those skilled in the art in light of the present description. However, such modifications and variations are still within the scope of the present description.

Fig. 3 is an exemplary flow chart for evaluating the shipping quality of an oil seal according to some embodiments of the present description. As shown in fig. 3, the process 300 includes the following steps. In some embodiments, the process 300 may be performed by a processor.

In step 310, when the oil seal is detected based on the first detection example and/or the second detection example, the spraying amount and the spraying frequency of the dust and sand spraying component and the water vapor spraying component are determined based on the monitored oil temperature sequence and the oil pressure sequence.

For more on the first detection instance, the second detection instance, see fig. 2 and the related description thereof.

The oil temperature sequence refers to a sequence of a plurality of oil temperature data of the oil seal over time.

The oil pressure sequence refers to a sequence of a plurality of oil pressure data of the oil seal which changes with time.

The dust and sand spraying component may be used to simulate an actual dust and sand environment. Such as a sandblasting machine, etc.

The vapor spray component may be used to simulate an actual vapor environment. Such as a shower or the like.

The spraying amount means an amount of the dust sprayed by the dust spraying means and an amount of the water vapor sprayed by the water vapor spraying means per unit time.

The spraying rate means a rate at which the dust and sand spraying means sprays dust and a rate at which the water vapor spraying means sprays water vapor.

In some embodiments, the processor may determine the amount and frequency of the spray of the dust and sand spray component and the water vapor spray component by way of vector matching.

In some embodiments, the processor may construct a feature vector for the oil seal based on the monitored oil temperature sequence and the oil pressure sequence. There are a variety of ways to construct the feature vector based on the oil temperature sequence and the oil pressure sequence. For example, a feature vector p is constructed based on the oil temperature sequence and the oil pressure sequence (x, y), where x, y may represent the oil temperature sequence and the oil pressure sequence.

The database contains a plurality of second reference vectors, and each second reference vector has a corresponding spray amount and spray frequency. The second reference vector is constructed based on the oil temperature sequence and the oil pressure sequence of the oil sealing piece with the tightness detected, and the reference spraying quantity and the spraying frequency corresponding to the second reference vector are the spraying quantity and the spraying frequency configured by the oil sealing piece with the tightness detected. The second vector to be matched is constructed based on the oil temperature sequence of the oil seal to be subjected to tightness detection and the oil pressure sequence. The construction of the second reference vector and the second vector to be matched is similar to that described above with reference to the construction of the feature vector.

In some embodiments, the processor may calculate a distance between the second reference vector and the second vector to be matched, respectively, and determine a spraying amount and a spraying frequency corresponding to the second vector to be matched. For example, a second reference vector whose distance from the second vector to be matched satisfies a preset condition is taken as a target vector, and the spraying amount and the spraying frequency corresponding to the target vector are taken as the spraying amount and the spraying frequency corresponding to the second vector to be matched. The preset conditions may be set according to circumstances. For example, the preset condition may be that the vector distance is minimum or that the vector distance is less than a distance threshold, or the like.

In some embodiments, the amount of spray, the frequency of spray, are also related to the targeted application scenario characteristics of the oil seal. The processor can also determine one or more related oil sealing elements according to the structural characteristics, the material characteristics and the target application scene characteristics of the oil sealing elements; one or more sets of spray amounts and spray frequencies are determined based on one or more actual environmental characteristics within a preset time period before each associated oil seal fails.

The structural features of the oil seal may be used to characterize the structural features of the oil seal. For example, the spring of the oil seal is located on the back of the oil seal to provide pressure against the seal lip.

The material characteristics of the oil seal may be used to characterize the material characteristics that make up the oil seal. The sealing lip of the oil seal is made of rubber, for example.

In some embodiments, the structural and material characteristics of the oil seal may be obtained by querying a process specification of the oil seal.

The target application scenario feature may be used to characterize the scenario in which the oil seal is applied. Such as high temperature environments, corrosive environments, etc.

In some embodiments, the target application scenario feature may be determined based on the use of the oil seal.

The associated oil seal is one that has failed and/or been serviced and is similar to the oil seal to be tested. The similarity to the oil seal to be detected refers to similarity to one or more of structural features, material features and target application scene features of the oil seal to be detected (i.e. similarity is greater than a preset threshold).

In some embodiments, the processor may determine one or more associated oil seals by way of vector matching based on the database.

In some embodiments, the processor may construct the feature vector corresponding to the oil seal based on the structural features, the material features, and the target application scenario features of the oil seal.

The database comprises a plurality of reference vectors, and each reference vector in the plurality of reference vectors has a corresponding associated oil seal. The reference vector is constructed based on structural features, material features, and target application scenario features of the associated oil seal.

In some embodiments, the processor may calculate the similarity between the reference vector and the vector to be matched, respectively, and determine the associated oil seal corresponding to the vector to be matched. For example, a reference vector with a similarity greater than a preset similarity threshold is used as a target vector, and an associated oil seal corresponding to the target vector is used as an associated oil seal corresponding to the vector to be matched. The preset similarity threshold may be set according to circumstances. For more relevant embodiments of vector matching reference may be made to the corresponding description of fig. 3.

The actual environmental characteristics may be used to characterize the actual environment for a period of time before the associated oil seal fails.

In some embodiments, the actual environmental characteristics may include environmental moisture content, environmental sand content, environmental moisture content per unit time, environmental sand content per unit time, and the like. The environment water content and the environment sand content refer to the total amount of water vapor and the total amount of sand and dust in a period of time before the related oil sealing element fails, and the environment water content and the environment sand content in unit time refer to the water vapor and the sand and dust in unit time.

In some embodiments, the environment sensed by the oil seal is similar to the actual environment, and the processor may determine the amount and rate of spray required for the seal tightness detection based on the actual environment characteristics. The oil seal detection device is characterized in that the oil seal detection device is similar to the environment water content and the environment sand content of an actual environment.

In some embodiments, the processor may determine the amount of spray from the dust and sand spray component and the moisture spray component based on the amount of ambient moisture per unit time and the amount of ambient moisture per unit time before failure of the associated oil seal.

In some embodiments, the processor may determine the spray frequency of the dust and sand spray component and the water vapor spray component based on the ambient moisture content, the ambient sand content, the ambient moisture content per unit time, the ambient sand content per unit time before failure of the associated oil seal. For example, the result of the operation of dividing the environmental sand content by the environmental sand content per unit time may be determined as the spraying frequency of the dust and sand spraying means, that is, how often the dust and sand is sprayed.

In some embodiments of the present disclosure, the processor may determine the spraying amount and the spraying frequency based on the actual environmental characteristics within a preset period of time before the associated oil seal fails, so that a more suitable spraying amount and spraying frequency may be determined, and a more accurate second detection result may be obtained.

At step 320, the dust and sand spraying means and the water vapor spraying means are controlled to spray the oil seal at respective spraying amounts and spraying frequencies.

In some embodiments, the processor may be in communication with the dust and sand spraying component and the water vapor spraying component, the processor sending control instructions to the dust and sand spraying component and the water vapor spraying component to control the dust and sand spraying component and the water vapor spraying component to spray the oil seal. Wherein, the control instruction at least comprises the respective spraying quantity and the spraying frequency of the dust and sand spraying component and the water vapor spraying component.

And 330, collecting at least one of wind power data and sound data in the spraying process to determine a second detection result.

For a description of wind data and sound data, reference may be made to the relevant description of fig. 2.

The second detection result is a tightness detection result of the oil sealing piece in a water spraying and sand blasting environment.

In some embodiments, the processor may determine the second detection result in a variety of ways. For example, the processor may determine the second detection result by means of a weighted summation based on the wind data and the sound data. Wherein the weights of the wind data and the sound data may be obtained based on a priori knowledge or historical data.

In some embodiments, the processor may predict an equivalent parameter corresponding to the oil seal based on the detection results of the first detection instance and/or the second detection instance after completing at least a preset number of the first detection instance and/or the second detection instance, and a physical state sequence of the oil seal during each of the first detection instance and/or the second detection instance, convert each detection instance of the first detection instance and/or the second detection instance that has not been performed in the oil seal detection scheme into an equivalent detection instance based on the equivalent parameter, detect the oil seal based on the equivalent detection instance, and determine the first detection result and/or the second detection result.

The sequence of physical states may be used to characterize the physical state of the oil seal. In some embodiments, the physical state sequence may include a test shaft temperature sequence, an oil pressure sequence of the oil seal, and an oil temperature sequence of the oil seal.

The equivalence parameter refers to a coefficient matrix required to equivalently convert a detection instance that takes longer into an equivalent detection instance that takes shorter. In some embodiments, the equivalent parameters may include a test time reduction factor and other parameter transformation factors. Other parameters include rotational speed, oil temperature, oil pressure, etc.

The test time reduction factor is data that characterizes how much the test time is reduced. For example, the test time reduction factor may be a 10 second reduction.

The other parameter conversion factor is data representing the conversion size of other parameters (rotation speed, oil temperature, oil pressure). For example, the other parameter transformation factor may be an increase in oil temperature of 10 ℃.

The equivalent test case refers to a test case in which the test duration is shorter than that of the first test case and/or the second test case, and the test requirement is stronger than that of the first test case and/or the second test case. The test requirements refer to oil temperature, oil pressure and the like, and the higher the test requirements are, the higher the oil temperature and the oil pressure are.

In some embodiments, the processor may multiply a first detection instance or a second detection instance by the equivalent parameter to obtain an equivalent detection instance.

In some embodiments, the predetermined number may be related to the degree of dispersion in the predicted lifetime of the finished oil seals, which are all of the same type and batch of oil seals.

In some embodiments, because the test time of the equivalent test case is short, the reliability thereof will generally be lower than that of the first test case and the second test case, so that the greater the degree of dispersion of the predicted lifetime, the more regular test cases are required, and the fewer the equivalent test cases, i.e. the greater the preset number is required. The degree of dispersion of the predicted lifetime may include, among others, a variance of the predicted lifetime, an average difference of the predicted lifetime, and the like. For more on the equivalent detection examples see the following description. For more on predicted lifetime see fig. 4 and its associated description.

In some embodiments of the present disclosure, the preset number may be related to the degree of dispersion of the predicted lifetime of the oil seal that has been completely detected in the same batch of the same type, so that a more reasonable preset number may be obtained, and thus, a suitable number of equivalent detection examples may be obtained.

In some embodiments, the equivalent parameters may be determined in a variety of ways. For example, the processor may determine the equivalent parameters by querying a first preset table. The first preset table may be determined based on a priori knowledge or historical data, and includes at least one detection result of the completed first detection instance and/or the second detection instance, and equivalent parameters corresponding to each detection result. The processor may query the first preset table based on the detection result of the first detection instance and/or the second detection instance that are currently completed, and determine the equivalent parameter.

In some embodiments, the processor may predict the equivalent parameter through the equivalent parameter prediction model based on the characteristics of the oil seal, the results of the detection of the completed first detection instance and/or the second detection instance, and the physical state sequence of the oil seal during each of the completed first detection instance and/or the second detection instance. The features of the oil seal may include material features, type features, and structural features of the oil seal, among others.

The type feature of the oil seal may be used to characterize the seal type of the oil seal. In some embodiments, the type characteristics of the oil seal may be obtained by querying a process specification of the oil seal. For more details regarding the material characteristics, structural features of the oil seal, see fig. 3 and its associated description.

An equivalent parameter prediction model may refer to a model for predicting equivalent parameters, which in some embodiments may be a machine learning model. For example, the equivalent parametric prediction model may include any one or combination of a convolutional Neural network (Convolutional Neural Networks, CNN) model, a Neural Networks (NN) model, or other custom model structures, etc.

In some embodiments, the processor may train the equivalent parameter predictive model based on a number of first training samples with first labels. The first training sample may include sample characteristics of a number of sample oil seals, a detection result of each sample oil seal at one or more moments during the historical test, and a sample physical state sequence of the oil seal during each completed first detection instance and/or second detection instance, and the first label may be an equivalent test instance. In some embodiments, the first training sample may be obtained based on historical data.

In some embodiments, the first label may be determined based on historical data for the same type of oil seal. For example, two test cases of "20 seconds at 60 ℃ for oil temperature" and "10 seconds at 120 ℃ for oil temperature" exist, and hundreds of thousands (i.e., a sufficiently large number) of similar oil seals are tested by the two test cases, and in response to the same oil seal passing through the two test cases, the two test cases can be considered as equivalent test cases. Wherein, the test result can be qualified or unqualified.

The same type of oil seal refers to a group of oil seals having the same or similar characteristics (materials, types, structural characteristics, etc.), test cases (parameters of the first/second test cases), etc. For example, if the oil seals a and B have the same/similar oil seal characteristics (the similarity satisfies the threshold requirement), and the oil seal A, B in the history data completes similar detection examples at the history time t1 and the history time t2, respectively, the oil seal a at the history time t1 and the oil seal B at the history time t2 may be determined as the same kind of oil seal.

In some embodiments, the equivalent parametric prediction model may be trained from a plurality of first training samples having first labels. For example, a plurality of first training samples with first labels may be input into an initial equivalent parameter prediction model, a loss function may be constructed from the labels and the results of the initial equivalent parameter prediction model, and parameters of the initial equivalent parameter prediction model may be iteratively updated by gradient descent or other methods based on the loss function. And when the preset training conditions are met, model training is completed, and a trained equivalent parameter prediction model is obtained. The preset training condition may be that the loss function converges, the number of iterations reaches a threshold value, and the like.

In some embodiments, the processor may further determine the first label of the equivalent parametric prediction model by: the processor determines a target equivalent detection instance set based on a group of similar oil sealing elements of the input equivalent parameter prediction model, wherein the target equivalent detection instance set is the largest (the data volume is largest, for example, the maximum equivalent detection instance is contained, so that the fitting accuracy is highest), and any two detection instances in the set are equivalent; fitting the target equivalent detection example set by a least square method and the like to obtain a fitting result, taking the coefficient of the fitting result as an equivalent parameter, and taking the equivalent parameter as a first label of an equivalent parameter prediction model.

The target equivalent detection instance set refers to a set of equivalent detection instances of some type of oil seal, and any two detection instances in the set are equivalent. The set of target equivalent detection examples may reflect equivalent characteristics of the oil seal. The equivalent characteristic means that the oil sealing member has consistent characteristics and presents consistent detection results after detection based on the same detection example.

In some embodiments, the processor may fit the set of target equivalent detection instances by a variety of fitting means. For example, the processor may consider each instance in the set of target equivalent detection instances as a point in a multidimensional space, and fit the set of target equivalent detection instances by a linear fit or a nonlinear fit to obtain a fitting result.

In some embodiments, the processor may output coefficients of the fitting result as equivalent parameters. For example, the processor may output the slope and intercept of the linear fit result as equivalent parameters.

In some embodiments of the present disclosure, the processor predicts the equivalent parameters based on the equivalent parameter prediction model, so that more reasonable equivalent parameters can be obtained, and the detection instance can be further converted into an equivalent detection instance which meets the requirements more.

In some embodiments, the processor may predict an equivalent parameter corresponding to the oil seal, convert the detection instance into an equivalent detection instance based on the equivalent parameter, detect the oil seal based on the equivalent detection instance, and determine the first detection result and/or the second detection result, thereby improving the detection efficiency of the oil seal, and completing the detection of a large number of oil seals more quickly.

Step 340, evaluating the factory quality based on the first detection result and the second detection result.

In some embodiments, the processor may evaluate the field quality by querying a second preset table. The second preset table may include at least one first detection result, at least one second detection result, and factory quality corresponding to each detection result. The processor may query the second preset table based on the first detection result and the second detection result, and evaluate the factory quality.

In some embodiments of the present disclosure, the processor determines the spraying amount and the spraying frequency of the dust and sand spraying component and the water vapor spraying component based on the monitored oil temperature sensing data sequence and the oil pressure sensing data sequence, so as to obtain more accurate spraying amount and spraying frequency, thereby obtaining a more satisfactory test environment; based on one or more first detection results and second detection results, the factory quality of the oil seal is evaluated, and the factory quality of the oil seal can be evaluated more reasonably.

In some embodiments, the processor may predict the life of the oil seal through life prediction model 430 and evaluate the factory quality based on the life of the oil seal.

The life prediction model 430 may refer to a model for predicting the life of an oil seal, and in some embodiments, the life prediction model may be a machine learning model. For example, the life prediction model may include any one or combination of a convolutional neural network model, a neural network model, or other custom model structure, etc.

In some embodiments, the input of the life prediction model 430 may include the first detection result 410 and the output may include the life 440 of the oil seal. For more details on the first detection result, how to determine the first detection result, see fig. 2 and the related description thereof.

In some embodiments, the input to the life prediction model 430 also includes a second detection result 420. In some embodiments, the second detection result may include a tightness detection result of the first detection example, the second detection example, and the equivalent detection example in a water-spraying and sand-blasting environment. Wherein, the spraying process is to spray the oil sealing piece by the dust and sand spraying component and the water vapor spraying component. For more details on the second detection result, how to determine the second detection result, see fig. 3 and the related description thereof.

In some embodiments of the present disclosure, the input of the life prediction model further includes a second detection result, so that a more accurate life of the oil seal can be obtained, and the factory quality can be estimated more reasonably.

In some embodiments, the life prediction model may be trained based on a plurality of second training samples with second labels. The second training sample may be a first detection result of the sample oil seal, and the second label of the second training sample may be a subsequent actual service life of the sample oil seal. In some embodiments, the second training sample may be obtained based on historical data and the second label may be determined based on manual annotation.

In some embodiments, the second training sample may further comprise a second test result of the sample oil seal.

During training, a plurality of second training samples with second labels can be input into the initial life prediction model, a loss function is constructed through the labels and the results of the initial life prediction model, and parameters of the initial life prediction model are iteratively updated through gradient descent or other methods based on the loss function. And when the preset training conditions are met, model training is completed, and a trained life prediction model is obtained. The preset training condition may be that the loss function converges, the number of iterations reaches a threshold value, and the like.

In some embodiments, the factory quality of the oil seal may be assessed in a number of ways. For example, the processor may determine the factory quality of the oil seal by querying a third preset table based on the oil seal lifetime. The third preset table may include at least one oil seal lifetime, and factory quality corresponding to each oil seal lifetime.

In some embodiments of the present disclosure, the processor predicts the lifetime of the oil seal through the lifetime prediction model, and evaluates the factory quality based on the lifetime of the oil seal, so that the factory quality can be evaluated more reasonably and comprehensively.

While the basic concepts have been described above, it will be apparent to those skilled in the art that the foregoing detailed disclosure is by way of example only and is not intended to be limiting. Although not explicitly described herein, various modifications, improvements, and adaptations to the present disclosure may occur to one skilled in the art. Such modifications, improvements, and modifications are intended to be suggested within this specification, and therefore, such modifications, improvements, and modifications are intended to be included within the spirit and scope of the exemplary embodiments of the present invention.

Meanwhile, the specification uses specific words to describe the embodiments of the specification. Reference to "one embodiment," "an embodiment," and/or "some embodiments" means that a particular feature, structure, or characteristic is associated with at least one embodiment of the present description. Thus, it should be emphasized and should be appreciated that two or more references to "an embodiment" or "one embodiment" or "an alternative embodiment" in various positions in this specification are not necessarily referring to the same embodiment. Furthermore, certain features, structures, or characteristics of one or more embodiments of the present description may be combined as suitable.

Furthermore, the order in which the elements and sequences are processed, the use of numerical letters, or other designations in the description are not intended to limit the order in which the processes and methods of the description are performed unless explicitly recited in the claims. While certain presently useful inventive embodiments have been discussed in the foregoing disclosure, by way of various examples, it is to be understood that such details are merely illustrative and that the appended claims are not limited to the disclosed embodiments, but, on the contrary, are intended to cover all modifications and equivalent arrangements included within the spirit and scope of the embodiments of the present disclosure. For example, while the system components described above may be implemented by hardware devices, they may also be implemented solely by software solutions, such as installing the described system on an existing server or mobile device.

Likewise, it should be noted that in order to simplify the presentation disclosed in this specification and thereby aid in understanding one or more inventive embodiments, various features are sometimes grouped together in a single embodiment, figure, or description thereof. This method of disclosure, however, is not intended to imply that more features than are presented in the claims are required for the present description. Indeed, less than all of the features of a single embodiment disclosed above.

In some embodiments, numbers describing the components, number of attributes are used, it being understood that such numbers being used in the description of embodiments are modified in some examples by the modifier "about," approximately, "or" substantially. Unless otherwise indicated, "about," "approximately," or "substantially" indicate that the number allows for a 20% variation. Accordingly, in some embodiments, numerical parameters set forth in the specification and claims are approximations that may vary depending upon the desired properties sought to be obtained by the individual embodiments. In some embodiments, the numerical parameters should take into account the specified significant digits and employ a method for preserving the general number of digits. Although the numerical ranges and parameters set forth herein are approximations that may be employed in some embodiments to confirm the breadth of the range, in particular embodiments, the setting of such numerical values is as precise as possible.

Each patent, patent application publication, and other material, such as articles, books, specifications, publications, documents, etc., referred to in this specification is incorporated herein by reference in its entirety. Except for application history documents that are inconsistent or conflicting with the content of this specification, documents that are currently or later attached to this specification in which the broadest scope of the claims to this specification is limited are also. It is noted that, if the description, definition, and/or use of a term in an attached material in this specification does not conform to or conflict with what is described in this specification, the description, definition, and/or use of the term in this specification controls.

Finally, it should be understood that the embodiments described in this specification are merely illustrative of the principles of the embodiments of this specification. Other variations are possible within the scope of this description. Thus, by way of example, and not limitation, alternative configurations of embodiments of the present specification may be considered as consistent with the teachings of the present specification. Accordingly, the embodiments of the present specification are not limited to only the embodiments explicitly described and depicted in the present specification.

Claims

1. The oil seal detection system comprises a test cavity, a test shaft, an oil seal installation tool, a pressing device, a temperature control unit, a pressure control unit, a temperature sensor, a pressure sensor and a processor;

The temperature sensor is configured to monitor an oil temperature of an oil seal, the pressure sensor is configured to monitor an oil pressure of the oil seal, and the processor is configured to:

sending an installation instruction to the oil seal installation tool and the pressing device, wherein the installation instruction indicates that the oil seal is installed at a designated position of the test cavity;

in response to detecting that the oil seal is mounted at the specified location, determining an oil seal detection scheme based on structural and material characteristics of the oil seal;

the oil seal detection scheme comprises a first detection example, wherein the first detection example comprises tightness detection of the oil seal in a first specific state, the first specific state comprises that the test shaft is in a specific rotating speed in a first preset time period, the first specific state further comprises that the test shaft is in a preset rotating speed, and the oil temperature and the oil pressure of the oil seal meet preset requirements; the oil seal detection scheme further comprises a second detection example, wherein the second detection example comprises tightness detection of the oil seal in a second specific state, the second specific state comprises that the test shaft is in a preset rotating speed sequence in a second preset time period and/or the oil temperature and the oil pressure of the oil seal meet the requirement of a preset sequence in the second preset time period;

Detecting the oil sealing piece based on the oil sealing detection scheme, and collecting at least one of wind power data and sound data in the detection process to determine a first detection result;

evaluating the factory quality of the oil sealing piece based on the first detection result;

the oil seal detection system further includes a dust and sand spray component and a water vapor spray component, the processor being further configured to:

determining the spraying amount and the spraying frequency of the dust and sand spraying component and the water vapor spraying component based on the detected oil temperature sequence and the oil pressure sequence when the oil sealing element is detected based on the first detection example and/or the second detection example;

controlling the dust and sand spraying component and the water vapor spraying component to spray the oil sealing piece according to the respective spraying quantity and the spraying frequency, and collecting at least one of wind power data and sound data in the spraying process to determine a second detection result;

and evaluating the factory quality based on the first detection result and the second detection result.

2. The oil seal detection system of claim 1, the processor further configured to:

predicting the service life of the oil sealing piece through a service life prediction model based on the first detection result, wherein the service life prediction model is a machine learning model;

And evaluating the factory quality based on the lifetime.

3. An oil seal detection method employing the oil seal detection system according to claim 1 or 2, the oil seal detection method being executed by a processor, comprising:

sending an installation instruction to the oil seal installation tool and the pressing device, wherein the installation instruction indicates that the oil seal piece is installed at a designated position of the test cavity;

in response to detecting that the oil seal is mounted in the specified position, determining an oil seal detection scheme based on structural features and material features of the oil seal, the oil seal detection scheme including a first detection instance including a tightness detection of the oil seal in a first specific state including a test shaft at a specific rotational speed for a first preset period of time;

and evaluating the delivery quality of the oil sealing piece based on the first detection result.

4. The oil seal detection method according to claim 3, the first specific state further comprising: the test shaft is at a preset rotating speed, and the oil temperature and the oil pressure of the oil sealing piece meet preset requirements.

5. The oil seal inspection method according to claim 4, the oil seal inspection scheme further comprising a second inspection instance including a tightness inspection of the oil seal in a second specific state including: the test shaft is in a preset rotating speed sequence in a second preset time period and/or the oil temperature and the oil pressure of the oil sealing piece meet the requirement of a preset sequence in the second preset time period.

6. The oil seal detection method according to claim 4, the method further comprising:

7. The oil seal inspection method according to claim 3, wherein the evaluating the factory quality of the oil seal based on the first inspection result further comprises:

and evaluating the factory quality based on the lifetime.