CN117869288A - Axial plunger pump health state assessment and prediction method and intelligent terminal - Google Patents
Axial plunger pump health state assessment and prediction method and intelligent terminal Download PDFInfo
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Abstract
The application discloses an axial plunger pump health state assessment and prediction method and an intelligent terminal, which belong to the technical field of axial plunger pumps, wherein the axial plunger pump health state assessment and prediction method comprises the following steps: acquiring a pressure signal of an axial plunger pump; calculating the health factor of the axial plunger pump according to the pulsation characteristic of the pressure signal; and determining the health state of the axial plunger pump according to the health factor. According to the method, the health factor of the axial plunger pump is built according to the pulsation characteristic of the pressure signal, so that the health evaluation of the axial plunger pump is realized, and further, the serious property loss and the casualties caused by equipment faults can be prevented.
Description
Technical Field
The application belongs to the technical field of axial plunger pumps, and particularly relates to an axial plunger pump health state assessment and prediction method and an intelligent terminal.
Background
Axial plunger pumps are a type of pump device that is widely used in modern industrial systems, and their high efficiency and reliability make them the first choice for many industrial applications, especially where precise control of flow and pressure is required. A plurality of plungers are uniformly distributed in the circumferential direction of the axial plunger pump, and the plungers reciprocate to generate a cavity with periodically changing volume, so that the suction of low-pressure oil and the discharge of high-pressure oil are realized.
The axial plunger pump is used as a heart of a hydraulic system, and is important in ensuring the stable operation of important equipment. In order to prevent significant property loss and casualties caused by equipment failure, it is particularly important to perform health assessment on the axial plunger pump.
Disclosure of Invention
The purpose of the application is to provide an axial plunger pump health state evaluation prediction method and an intelligent terminal for performing health detection evaluation on the axial plunger pump.
According to a first aspect of embodiments of the present application, there is provided an axial plunger pump health status assessment prediction method, which may include: acquiring a pressure signal of an axial plunger pump; calculating the health factor of the axial plunger pump according to the pulsation characteristic of the pressure signal; and determining the health state of the axial plunger pump according to the health factor.
In some alternative embodiments of the present application, the pressure signal of the axial plunger pump is obtained, specifically: a high frequency sampling pressure signal at an outlet line of the axial plunger pump is acquired.
In some alternative embodiments of the present application, calculating the health factor of the axial plunger pump from the pulsatile characteristics of the pressure signal includes: dividing the pressure signal to obtain a plurality of sections of sampling signals; calculating health factors of each section of sampling signals in the plurality of sections of sampling signals; and calculating the health factor of the axial plunger pump according to the health factor of each section of sampling signals in the plurality of sections of sampling signals.
In some optional embodiments of the present application, the dividing the pressure signal to obtain a multi-segment sampling signal includes: acquiring sampling points of one circle of rotation of the axial plunger pump; the pressure signal is divided according to the sampling points, so that the sampling points of each section of sampling signals in the multi-section sampling signals are the same as the sampling points of the axial plunger pump rotating for one circle.
In some alternative embodiments of the present application, calculating the health factor for each of the plurality of segments of sampled signals includes: converting each section of sampling signal into a pressure pulsation signal frequency; calculating the sum of the pulse fundamental wave and the energy of each order of harmonic waves of the pressure signal and the total energy of the frequency domain of the pressure signal; and determining health factors of each section of sampling signals in the multi-section sampling signals according to the sum of the pulse fundamental wave of the pressure signals and the energy of each harmonic wave and the total energy of the frequency domain of the pressure signals.
In some optional embodiments of the present application, the health factor of the axial plunger pump is calculated according to the health factor of each section of sampling signal in the plurality of sections of sampling signals, specifically: and calculating the average value of the health factors of each section of sampling signals in the plurality of sections of sampling signals to obtain the health factors of the axial plunger pump.
In some optional embodiments of the present application, after determining the health status of the axial plunger pump according to the health factor, the axial plunger pump health status assessment prediction method further comprises: and predicting the service life of the axial plunger pump according to the health factor by using a trained machine learning model.
According to a second aspect of embodiments of the present application, there is provided an intelligent terminal, which may include:
the acquisition module is used for acquiring an outlet pressure signal of the axial plunger pump;
the calculation module is used for calculating the health factor of the axial plunger pump according to the pulsation characteristic of the pressure signal;
and the calculation module is also used for determining the health state of the axial plunger pump according to the health factor.
According to a third aspect of embodiments of the present application, there is provided an electronic device, which may include:
a processor;
a memory for storing processor-executable instructions;
wherein the processor is configured to execute instructions to implement the axial plunger pump health assessment prediction method as shown in any one of the embodiments of the first aspect.
According to a fourth aspect of embodiments of the present application, there is provided a storage medium, which when executed by a processor of an information processing apparatus or a server, causes the information processing apparatus or the server to implement the axial plunger pump health status assessment prediction method as shown in any one of the embodiments of the first aspect.
The technical scheme of the application has the following beneficial technical effects:
according to the method, the pressure signal of the axial plunger pump is obtained, the health factor of the axial plunger pump is calculated according to the pulsation characteristic of the pressure signal, and finally the health state of the axial plunger pump is determined according to the health factor. According to the method, the health factor of the axial plunger pump is built according to the pulsation characteristic of the pressure signal, so that the health evaluation of the axial plunger pump is realized, and further, the serious property loss and the casualties caused by equipment faults can be prevented.
Drawings
FIG. 1 is a flow chart of a method of predicting health status assessment of an axial piston pump in an exemplary embodiment of the present application;
fig. 2 is a schematic structural diagram of an intelligent terminal according to an exemplary embodiment of the present application;
FIG. 3 is a schematic diagram of an intelligent terminal according to an embodiment of the present application
FIG. 4 is a schematic diagram of an electronic device in an exemplary embodiment of the present application;
fig. 5 is a schematic diagram of a hardware structure of an electronic device in an exemplary embodiment of the present application.
Detailed Description
For the purposes of making the objects, technical solutions and advantages of the present application more apparent, the present application will be further described in detail below with reference to the accompanying drawings. It should be understood that the description is only exemplary and is not intended to limit the scope of the present application. In addition, in the following description, descriptions of well-known structures and techniques are omitted so as not to unnecessarily obscure the concepts of the present application.
A layer structure schematic diagram according to an embodiment of the present application is shown in the drawings. The figures are not drawn to scale, wherein certain details may be exaggerated and some details may be omitted for clarity. The shapes of the various regions, layers and relative sizes, positional relationships between them shown in the drawings are merely exemplary, may in practice deviate due to manufacturing tolerances or technical limitations, and one skilled in the art may additionally design regions/layers having different shapes, sizes, relative positions as actually required.
It will be apparent that the embodiments described are some, but not all, of the embodiments of the present application. All other embodiments, which can be made by one of ordinary skill in the art without undue burden from the present disclosure, are within the scope of the present disclosure.
In the description of the present application, it should be noted that the terms "first," "second," and "third" are used for descriptive purposes only and are not to be construed as indicating or implying relative importance.
In addition, the technical features described below in the different embodiments of the present application may be combined with each other as long as they do not collide with each other.
The health of an axial plunger pump is typically characterized by physical information, such as pressure signals, generated during its operation. The collected signals are analyzed and processed, and health factors are built in a reasonable mode, so that the health condition of the axial plunger pump can be estimated. The plunger pump outlet pressure generates periodic pulsation due to the discontinuous oil discharge of the plunger and the compressibility of oil, the time domain waveform of the plunger pump outlet pressure is similar to a sine signal, but the plunger pump outlet pressure signal is not a standard sine signal, namely, the plunger pump outlet pressure signal has higher harmonic components in a healthy state, and whether the pressure signal is abnormal can not be judged through a standard harmonic distortion rate formula; but it has been found that a new health factor can be built based on the pulsatile nature of the pressure signal.
The method for estimating and predicting the health state of the axial plunger pump and the intelligent terminal provided by the embodiment of the application are described in detail through specific embodiments and application scenes thereof with reference to the accompanying drawings.
As shown in fig. 1, in a first aspect of the embodiments of the present application, there is provided an axial plunger pump health status evaluation prediction method, where the detection method may include:
s110: acquiring an outlet pressure signal of an axial plunger pump;
s120: calculating the health factor of the axial plunger pump according to the pulsation characteristic of the pressure signal;
s130: and determining the health state of the axial plunger pump according to the health factor.
According to the method, the health factor of the axial plunger pump is built according to the pulsation characteristic of the pressure signal, so that the health evaluation of the axial plunger pump is realized, and further, the serious property loss and casualties caused by equipment faults can be prevented.
In some embodiments, a pressure signal of the axial plunger pump is obtained, in particular:
a high frequency sampling pressure signal at an outlet line of the axial plunger pump is acquired.
In order to construct the axial health factor, the embodiment needs high-frequency sampling to acquire the pressure pulsation signal of the plunger pump outlet so as to cover the higher harmonic component information in the pressure pulsation signal and ensure the accuracy of the health evaluation result. The frequency range of the pressure pulsation harmonic wave of the axial plunger pump is found to be 50 Hz-4000 Hz. And the collected axial plunger pump outlet pressure signal needs to contain at least ten harmonics. Therefore, the plunger pump outlet pressure sensor is more preferable to meet the requirement that the natural frequency is more than or equal to 30kHz and the sampling frequency of the pressure sensor is more than or equal to 15kHz. The pressure sensor is arranged close to the pump outlet pipeline of the axial plunger pump, so that the pressure pulsation condition can be accurately monitored. Specifically, a sampling frequency of 15 kHz-50 kHz can be selected. In the sampling process, a segmented acquisition strategy can be adopted, and a section of high-frequency signal with a fixed length can be acquired at fixed intervals.
In some embodiments, calculating the health factor of the axial plunger pump from the pulsatile characteristics of the pressure signal includes:
dividing the pressure signal to obtain a plurality of sections of sampling signals;
calculating health factors of each section of sampling signals in the plurality of sections of sampling signals;
and calculating the health factor of the axial plunger pump according to the health factor of each section of sampling signals in the plurality of sections of sampling signals.
In this embodiment, the number of sampling points of each segment of the segmented signal is the same as the number of sampling points corresponding to one rotation of the plunger pump, that is, the segmented signal just contains one complete rotation period of the plunger pump, and the data can be corresponding to each rotation period of the pump, so that the calculation of the axial health factor is better performed.
In some embodiments, the dividing the pressure signal to obtain a plurality of segments of sampled signals includes:
acquiring sampling points of one circle of rotation of the axial plunger pump;
the pressure signal is divided according to the sampling points, so that the sampling points of each section of sampling signals in the multi-section sampling signals are the same as the sampling points of the axial plunger pump rotating for one circle.
In some embodiments, calculating the health factor for each of the plurality of segments of sampled signals includes:
converting each section of sampling signal into a pressure pulsation signal frequency;
calculating the sum of the pulse fundamental wave and the energy of each order of harmonic waves of the pressure signal and the total energy of the frequency domain of the pressure signal;
and determining health factors of each section of sampling signals in the multi-section sampling signals according to the sum of the pulse fundamental wave of the pressure signals and the energy of each harmonic wave and the total energy of the frequency domain of the pressure signals.
Specifically, the health factor is calculated by the following formula:
f 0 =plunger pump rotation frequency f×plunger number n
E 0 ＝|X(f 0 )| 2 +|X(2f 0 )| 2 +|X(3f 0 )| 2 +...+|X(nf 0 )| 2
Wherein f 0 Is the fundamental frequency of the normal pressure signal. f (f) 0 、2f 0 、3f 0 、…、nf 0 For each order of harmonic frequency; x is the amplitude of the pressure signal; e (E) 0 Is the pulse of the pressure signalThe sum of the energy of the dynamic fundamental wave and the harmonic wave of each order; e is the total energy of the pressure signal frequency domain; p (P) n Is the amplitude of the nth frequency component in the spectrum; HI is a health factor.
In some embodiments, the health factor of the axial plunger pump is calculated according to the health factor of each section of sampling signals in the plurality of sections of sampling signals, specifically:
and calculating the average value of the health factors of each section of sampling signals in the plurality of sections of sampling signals to obtain the health factors of the axial plunger pump.
In some embodiments, after determining the health status of the axial plunger pump based on the health factor, the axial plunger pump health status assessment prediction method further comprises:
and predicting the service life of the axial plunger pump according to the health factor by using a trained machine learning model.
In the running process of the axial plunger pump, historical health indexes can be calculated and accumulated. After selecting a suitable network architecture (e.g., CNN, RNN, LSTM, etc.), setting up super parameters, selecting a loss function, optimizing, etc., the model will learn the relationship between the health index and the remaining life in cooperation with the data set.
Specifically, the trained machine learning model is obtained through the following training:
and (3) data acquisition: outlet pressure signal data of the plunger pump under different health states are collected, and the data should comprise signals from health to fault.
And (3) data marking: each data sample is annotated with a remaining life (RUL), i.e. the time required from the current state to the final fault state. The specific values may be determined by historical fault data or expert knowledge.
Data screening: and abnormal values and noise are removed, and the quality of data is ensured.
Feature extraction and selection: according to the health factor construction method, the health factor of each sample is calculated when the plunger pump is operated. Other relevant features, such as statistical features, time-frequency features, etc. of the signal may be extracted if desired. The most advantageous features for predicting RUL are selected by correlation analysis, principal Component Analysis (PCA), etc.
Model selection: a variety of deep learning models may be considered. For time series data, convolutional Neural Networks (CNNs), recurrent Neural Networks (RNNs), and variants thereof such as long short-term memory networks (LSTM) and gated loop units (GRUs) may be more suitable.
Training data set partitioning: the data is divided into a training set, a validation set and a test set. The training set is used for training the model, the verification set is used for parameter tuning, and the test set is used for finally evaluating the performance of the model.
Model training: the machine learning model is trained using a training set. Model parameters are adjusted according to performance on the validation set to optimize the effect. Manual adjustment may be used, or automated methods such as grid searching, random searching, bayesian optimization, etc. may be used. The model performance can then be evaluated.
Model deployment: the trained machine learning model is deployed into a production environment for predicting the remaining life of the plunger pump in real time or periodically.
Model iteration: the predictive performance of the model is continuously monitored and the model is periodically updated based on the newly collected data to accommodate possible environmental and equipment changes.
It should be noted that, in the method for estimating and predicting the health state of the axial plunger pump provided in the embodiments of the present application, the execution subject may be an axial plunger pump health state estimating and predicting device, or a control module in the axial plunger pump health state estimating and predicting device for executing the method for estimating and predicting the health state of the axial plunger pump. In the embodiment of the application, an example is taken as an axial plunger pump health state evaluation and prediction method executed by the axial plunger pump health state evaluation and prediction device, and the axial plunger pump health state evaluation and prediction device provided by the embodiment of the application is described.
As shown in fig. 2, in a second aspect of the embodiments of the present application, there is provided an intelligent terminal, which may include:
the acquisition module is used for acquiring an outlet pressure signal of the axial plunger pump;
the calculation module is used for calculating the health factor of the axial plunger pump according to the pulsation characteristic of the pressure signal;
and the calculation module is also used for determining the health state of the axial plunger pump according to the health factor.
The axial plunger pump health assessment requires the selection of an appropriate data acquisition system to measure and process the pressure pulsation amplitude and phase. The health assessment process is typically completed by uploading data to the host computer. However, when the plunger pump outlet pressure signal is sampled and collected at a high frequency, the uploading of a large amount of data to the host computer will cause data processing and response delays, which is particularly the case in the case of long-time sampling or unstable network connection. In addition, the processing mode based on the upper computer generally requires higher maintenance cost, increases the number of devices, and is unfavorable for shortening the link length of data acquisition, transmission and analysis.
As shown in fig. 3, in a specific embodiment, an intelligent terminal is provided, including:
the acquisition module 1 is fixed at the bottom of the aluminum alloy shell 3;
the computing module 2 is responsible for analyzing, computing, analyzing and uploading data and performing artificial intelligent edge computing;
the acquisition module 1 and the calculation module 2 are fixed on the top of the shell 3 through copper columns 4 and the like;
a sensor interface 5 for connecting sensors arranged on the industrial installation;
the power interface 6 uses a 24V direct current power supply to supply power for the acquisition module 1 and the calculation module 2;
the video output interface 7 is used for connecting the computing module 2 with the display equipment, so that debugging is convenient;
a USB interface 8 for connecting the computing module 2 and a USB device;
the acquisition module network port 9 and the calculation module network port 10 are used for transmitting acquisition data from the acquisition module 1 to the calculation module 2;
a network antenna interface 11 for providing wireless network connection functionality to the computing module 2.
In order to better utilize the artificial intelligence deep learning technology, an artificial intelligence module is adopted as a computing module 2 of the intelligent perception terminal, a graphic processing unit GPU specially optimized for AI and machine learning workload is provided, and a complex deep learning algorithm can be executed at the edge end. Residual life (Remaining Useful Life, RUL) predictions can be made by deploying different deep learning models in the artificial intelligence module with the signals or pre-processed features acquired in real time by the acquisition module 1 as model inputs.
The deep learning-based residual life prediction may include data acquisition, health factor (HI) construction, historical HI data accumulation and model training, RUL prediction, and the like. The HI of axial piston pumps typically exhibit varying degrees of degradation as the degree of failure progresses. The performance degradation process of the axial plunger pump comprises a health stage, a degradation stage and a critical stage. The RUL of the plunger pump may be obtained by predicting the time interval in which HI reaches the failure threshold.
HI prediction may be accomplished through deep neural network models, such as convolutional neural networks, recurrent neural networks, long-term memory networks, and the like. In the running process of the axial plunger pump, historical HI data can be continuously calculated and accumulated and used for training a deep neural network model, and the trained model can be used for predicting the residual life of equipment. Because cloud servers typically have higher performance and greater storage space, training of models can be done at the cloud servers. The computing module 2 may download the latest deep learning model locally for model reasoning via SCP (Secure Copy Protocol) instructions.
The data processing of the intelligent terminal in the embodiment occurs in a place closer to a data source, namely the computing module 2, rather than an upper computer or a remote server, so that the data transmission time is reduced, the real-time analysis capability of high-frequency pressure signals is realized, the dependence on a central server or cloud resources is reduced, the accuracy and the real-time performance of the health evaluation prediction of the axial plunger pump are improved, and the reliability of the data acquisition terminal in an unstable industrial environment of a network is improved. In addition, the computing module 2 can execute a complex deep learning algorithm at the edge end, can respond more quickly to the change of the state of the industrial system, reduces network load and reduces the number of devices. The marginalization and the intellectualization of the data processing are realized, so that the monitoring and predictive maintenance of the industrial equipment are more efficient and advanced.
In this embodiment, the computing module 2 is connected with the collecting module 1 through a TCP protocol, and uses the collecting module 1 as a server, and the computing module 2 as a client, sends a collecting command to the collecting module 1 and receives collected data. The pressure signals acquired by the acquisition module 1 are stored in a data frame in time sequence, and the magnitude of numbers in the data frame represents the magnitude of pressure pulsation. By reading the data at a specific position of the data frame, an outlet pressure signal can be obtained when the axial plunger pump works. The computing module 2 is used as a core processing module, can replace a traditional upper computer, analyzes, calculates and analyzes received data in real time, and runs a deep learning reasoning model deployed on the module.
The acquisition module 1 and the calculation module 2 adopt a two-layer structure, and the whole structure of the device is simple; the overall size of the device can be reduced to 184.5mm multiplied by 176mm multiplied by 75mm, and the device is small in size. In addition, the whole body can be covered by an aluminum alloy shell with the thickness of 2.5mm, and the aluminum alloy shell has a better protection function on internal elements.
The calculation results of the health assessment and the remaining life are usually transmitted to the cloud end, so that relevant personnel can check the calculation results conveniently. The device can have a wireless network connection function, and the analyzed signals and calculation analysis results can be packaged into a JSON data format through a wireless network and transmitted to a cloud server based on an MQTT protocol. The content of the package and the frequency of uploading can be set according to the requirement. Typically, a segment of JSON data typically contains one time pressure signal sample data and health factor calculations. The prediction result of the remaining life may be packaged once with a plurality of pieces of JSON data.
The computing module 2 integrates the artificial intelligence reasoning function with low power consumption and high performance while having the function of a traditional upper computer. The collected data is not required to be uploaded to an upper computer, but is transmitted to the computing module 2 through a network port, and the computing module 2 analyzes, calculates, analyzes and uploads the data, so that the device has a wireless network function and can work in an environment lacking a wired network.
The intelligent terminal of the embodiment has the capabilities of high-frequency sampling and real-time processing analysis of the pressure pulsation signal of the axial plunger pump outlet, so that the collection of the high-frequency pressure signal of the plunger pump, the construction of health factors and the prediction of the residual life can be realized at the edge end.
The intelligent terminal in the embodiment of the application can be a device, and also can be a component, an integrated circuit or a chip in the terminal. The device may be a mobile electronic device or a non-mobile electronic device. By way of example, the mobile electronic device may be a cell phone, tablet computer, notebook computer, palm computer, vehicle-mounted electronic device, wearable device, ultra-mobile personal computer (ultra-mobile personal computer, UMPC), netbook or personal digital assistant (personal digital assistant, PDA), etc., and the non-mobile electronic device may be a server, network attached storage (Network Attached Storage, NAS), personal computer (personal computer, PC), television (TV), teller machine or self-service machine, etc., and the embodiments of the present application are not limited in particular.
The intelligent terminal in the embodiment of the application may be a device with an operating system. The operating system may be an Android operating system, an IOS operating system, or other possible operating systems, which is not specifically limited in the embodiments of the present application.
The intelligent terminal provided in this embodiment of the present application can implement each process implemented by the method embodiment of fig. 1, and in order to avoid repetition, a description is omitted here.
Optionally, as shown in fig. 4, the embodiment of the present application further provides an electronic device 400, including a processor 401, a memory 402, and a program or an instruction stored in the memory 402 and capable of running on the processor 401, where the program or the instruction implements each process of the embodiment of the method for estimating and predicting the health status of the axial plunger pump when executed by the processor 401, and the process can achieve the same technical effect, so that repetition is avoided and redundant description is omitted here.
The electronic device in the embodiment of the application includes the mobile electronic device and the non-mobile electronic device described above.
Fig. 5 is a schematic hardware structure of an electronic device implementing an embodiment of the present application.
The electronic device 500 includes, but is not limited to: radio frequency unit 501, network module 502, audio output unit 503, input unit 504, sensor 505, display unit 506, user input unit 507, interface unit 508, memory 509, and processor 510.
Those skilled in the art will appreciate that the electronic device 500 may further include a power source (e.g., a battery) for powering the various components, and that the power source may be logically coupled to the processor 510 via a power management system to perform functions such as managing charging, discharging, and power consumption via the power management system. The electronic device structure shown in fig. 5 does not constitute a limitation of the electronic device, and the electronic device may include more or less components than shown, or may combine certain components, or may be arranged in different components, which are not described in detail herein.
It should be appreciated that in embodiments of the present application, the input unit 504 may include a graphics processor (Graphics Processing Unit, GPU) 5041 and a microphone 5042, with the graphics processor 5041 processing image data of still pictures or video obtained by an image capturing device (e.g., a camera) in a video capturing mode or an image capturing mode. The display unit 506 may include a display panel 5061, and the display panel 5061 may be configured in the form of a liquid crystal display, an organic light emitting diode, or the like. The user input unit 507 includes a touch panel 5071 and other input devices 5072. Touch panel 5071, also referred to as a touch screen. Touch panel 5071 may include two parts, a touch detection device and a touch controller. Other input devices 5072 may include, but are not limited to, a physical keyboard, function keys (e.g., volume control keys, switch keys, etc.), a trackball, a mouse, a joystick, and so forth, which are not described in detail herein. The memory 509 may be used to store software programs as well as various data including, but not limited to, application programs and an operating system. Processor 510 may integrate an application processor that primarily handles operating systems, user interfaces, applications, etc., with a modem processor that primarily handles wireless communications. It will be appreciated that the modem processor described above may not be integrated into the processor 510.
The embodiment of the application further provides a readable storage medium, on which a program or an instruction is stored, where the program or the instruction implements each process of the above embodiment of the method for estimating and predicting the health state of the axial plunger pump when executed by a processor, and the same technical effects can be achieved, so that repetition is avoided, and no detailed description is given here.
Wherein the processor is a processor in the electronic device described in the above embodiment. The readable storage medium includes a computer readable storage medium such as a Read-Only Memory (ROM), a random access Memory (Random Access Memory, RAM), a magnetic disk or an optical disk, and the like.
The embodiment of the application further provides a chip, the chip includes a processor and a communication interface, the communication interface is coupled with the processor, the processor is used for running a program or an instruction, implementing each process of the embodiment of the method for estimating and predicting the health state of the axial plunger pump, and achieving the same technical effect, so as to avoid repetition, and no further description is provided here.
It should be understood that the chips referred to in the embodiments of the present application may also be referred to as system-on-chip chips, chip systems, or system-on-chip chips, etc.
It should be noted that, in this document, the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising one … …" does not exclude the presence of other like elements in a process, method, article, or apparatus that comprises the element. Furthermore, it should be noted that the scope of the methods and apparatus in the embodiments of the present application is not limited to performing the functions in the order shown or discussed, but may also include performing the functions in a substantially simultaneous manner or in an opposite order depending on the functions involved, e.g., the described methods may be performed in an order different from that described, and various steps may also be added, omitted, or combined. Additionally, features described with reference to certain examples may be combined in other examples.
From the above description of the embodiments, it will be clear to those skilled in the art that the above-described embodiment method may be implemented by means of software plus a necessary general hardware platform, but of course may also be implemented by means of hardware, but in many cases the former is a preferred embodiment. Based on such understanding, the technical solutions of the present application may be embodied essentially or in a part contributing to the prior art in the form of a computer software product stored in a storage medium (such as ROM/RAM, magnetic disk, optical disk), comprising several instructions for causing a terminal (which may be a mobile phone, a computer, a server, or a network device, etc.) to perform the methods described in the embodiments of the present application.
The embodiments of the present application have been described above with reference to the accompanying drawings, but the present application is not limited to the above-described embodiments, which are merely illustrative and not restrictive, and many forms may be made by those of ordinary skill in the art without departing from the spirit of the present application and the scope of the claims, which are also within the protection of the present application.
Claims (10)
1. The method for evaluating and predicting the health state of the axial plunger pump is characterized by comprising the following steps of:
acquiring a pressure signal of an axial plunger pump;
calculating a health factor of the axial plunger pump according to the pulsation characteristic of the pressure signal;
and determining the health state of the axial plunger pump according to the health factor.
2. The method for estimating and predicting the health status of an axial plunger pump according to claim 1, wherein the step of obtaining the pressure signal of the axial plunger pump specifically comprises:
a high frequency sampling pressure signal at an outlet line of the axial plunger pump is acquired.
3. The method of claim 1, wherein calculating a health factor of the axial plunger pump from a pulsation characteristic of the pressure signal comprises:
dividing the pressure signal to obtain a plurality of sections of sampling signals;
calculating health factors of each section of sampling signals in the plurality of sections of sampling signals;
and calculating the health factor of the axial plunger pump according to the health factor of each section of sampling signals in the plurality of sections of sampling signals.
4. The method for predicting health status of an axial piston pump according to claim 3, wherein the dividing the pressure signal to obtain a plurality of segments of sampling signals comprises:
obtaining sampling points of the axial plunger pump rotating for one circle;
and dividing the pressure signal according to the sampling points so that the sampling points of each section of sampling signals in the multi-section sampling signals are the same as the sampling points of the axial plunger pump rotating for one circle.
5. The method of claim 3, wherein said calculating a health factor for each of said plurality of segments of sampled signals comprises:
converting each section of sampling signal into a pressure pulsation signal frequency;
calculating the sum of the pulse fundamental wave and the energy of each order of harmonic waves of the pressure signal and the total energy of the frequency domain of the pressure signal;
and determining health factors of each section of sampling signals in the multi-section sampling signals according to the sum of the pulse fundamental wave and the energy of each harmonic wave and the total energy of the frequency domain of the pressure signals.
6. The method for estimating and predicting the health state of an axial plunger pump according to claim 3, wherein the calculating the health factor of the axial plunger pump according to the health factor of each segment of the sampled signals specifically includes:
and calculating the average value of the health factors of each section of sampling signals in the plurality of sections of sampling signals to obtain the health factors of the axial plunger pump.
7. The axial plunger pump health assessment prediction method according to claim 1, wherein after the determination of the health status of the axial plunger pump according to the health factor, the axial plunger pump health assessment prediction method further comprises:
and predicting the service life of the axial plunger pump according to the health factor by utilizing a trained machine learning model.
8. An intelligent terminal, comprising:
the acquisition module is used for acquiring an outlet pressure signal of the axial plunger pump;
the calculating module is used for calculating the health factor of the axial plunger pump according to the pressure signal;
the calculation module is further used for determining the health state of the axial plunger pump according to the health factor.
9. An electronic device, comprising: a processor, a memory and a program or instruction stored on the memory and executable on the processor, which when executed by the processor, implements the steps of the axial plunger pump health assessment prediction method of any one of claims 1 to 7.
10. A readable storage medium, wherein a program or instructions is stored on the readable storage medium, which when executed by a processor, implements the steps of the axial plunger pump health assessment prediction method according to any one of claims 1 to 7.
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