Axial piston pump health status assessment prediction method and intelligent terminal

Abstract

The present application discloses a method for evaluating and predicting the health status of an axial piston pump and an intelligent terminal, which belongs to the technical field of axial piston pumps. The method for evaluating and predicting the health status of an axial piston pump includes: obtaining a pressure signal of the axial piston pump; calculating a health factor of the axial piston pump according to the pulsation characteristics of the pressure signal; and determining the health status of the axial piston pump according to the health factor. The method constructs a health factor of the axial piston pump according to the pulsation characteristics of the pressure signal, realizes the health evaluation of the axial piston pump, and thus can prevent major property losses and casualties caused by equipment failure.

Description

Technical Field

[0001] The present application belongs to the technical field of axial piston pumps, and specifically relates to a method for evaluating and predicting the health status of an axial piston pump and an intelligent terminal. Background Art

[0002] Axial piston pumps are a type of pump equipment widely used in modern industrial systems. Their high efficiency and reliability make them the first choice in many industrial applications, especially in situations where precise control of flow and pressure is required. Axial piston pumps have multiple pistons evenly distributed in the circumferential direction. The reciprocating motion of the pistons creates a cavity with a periodic volume change, thereby achieving the suction of low-pressure oil and the discharge of high-pressure oil.

[0003] As the "heart" of the hydraulic system, the axial piston pump is crucial in ensuring the stable operation of major equipment. In order to prevent major property losses and casualties caused by equipment failure, it is particularly important to conduct a health assessment of the axial piston pump. Summary of the invention

[0004] The purpose of this application is to provide an axial piston pump health status assessment and prediction method and an intelligent terminal to perform health detection and assessment on the axial piston pump.

[0005] According to the first aspect of an embodiment of the present application, a method for evaluating and predicting the health status of an axial piston pump is provided. The detection method may include: obtaining a pressure signal of the axial piston pump; calculating a health factor of the axial piston pump based on the pulsation characteristics of the pressure signal; and determining the health status of the axial piston pump based on the health factor.

[0006] In some optional embodiments of the present application, obtaining the pressure signal of the axial piston pump specifically involves obtaining a high-frequency sampling pressure signal at the outlet pipeline of the axial piston pump.

[0007] In some optional embodiments of the present application, the health factor of the axial piston pump is calculated based on the pulsation characteristics of the pressure signal, including: segmenting the pressure signal to obtain multiple sampling signals; calculating the health factor of each sampling signal in the multiple sampling signals; and calculating the health factor of the axial piston pump based on the health factor of each sampling signal in the multiple sampling signals.

[0008] In some optional embodiments of the present application, the pressure signal is segmented and processed to obtain multiple sampling signals, including: obtaining the number of sampling points in one rotation of the axial piston pump; segmenting the pressure signal according to the number of sampling points so that the number of sampling points of each sampling signal in the multiple sampling signals is the same as the number of sampling points in one rotation of the axial piston pump.

[0009] In some optional embodiments of the present application, the health factor of each sampling signal in a plurality of sampling signals is calculated, including: converting each sampling signal into a pressure pulsation signal frequency; calculating the sum of the energy of the fundamental wave of the pressure signal pulsation and each order harmonic wave and the total energy of the pressure signal in the frequency domain; determining the health factor of each sampling signal in the plurality of sampling signals according to the sum of the energy of the fundamental wave of the pressure signal pulsation and each order harmonic wave and the total energy of the pressure signal in the frequency domain.

[0010] In some optional embodiments of the present application, the health factor of the axial piston pump is calculated based on the health factor of each sampling signal in the multiple sampling signals, specifically: the average value of the health factor of each sampling signal in the multiple sampling signals is calculated to obtain the health factor of the axial piston pump.

[0011] In some optional embodiments of the present application, after determining the health status of the axial piston pump based on the health factor, the axial piston pump health status assessment and prediction method further includes: using a trained machine learning model to predict the life of the axial piston pump based on the health factor.

[0012] According to a second aspect of an embodiment of the present application, there is provided an intelligent terminal, which may include:

[0013] An acquisition module, used for acquiring an outlet pressure signal of an axial piston pump;

[0014] A calculation module, used for calculating the health factor of the axial piston pump according to the pulsation characteristics of the pressure signal;

[0015] The calculation module is also used to determine the health status of the axial piston pump according to the health factor.

[0016] According to a third aspect of an embodiment of the present application, an electronic device is provided, which may include:

[0017] processor;

[0018] a memory for storing processor-executable instructions;

[0019] The processor is configured to execute instructions to implement the axial piston pump health status assessment and prediction method as shown in any one of the embodiments of the first aspect.

[0020] According to the fourth aspect of the embodiments of the present application, a storage medium is provided. When the instructions in the storage medium are executed by a processor of an information processing device or a server, the information processing device or the server implements the axial piston pump health status assessment and prediction method as shown in any one of the embodiments of the first aspect.

[0021] The above technical solution of the present application has the following beneficial technical effects:

[0022] The method of the embodiment of the present application obtains the pressure signal of the axial piston pump, calculates the health factor of the axial piston pump according to the pulsation characteristics of the pressure signal, and finally determines the health status of the axial piston pump according to the health factor. The method constructs the health factor of the axial piston pump according to the pulsation characteristics of the pressure signal, realizes the health assessment of the axial piston pump, and thus can prevent major property losses and casualties caused by equipment failure. BRIEF DESCRIPTION OF THE DRAWINGS

[0023] Figure 1 It is a flow chart of a method for evaluating and predicting the health status of an axial piston pump in an exemplary embodiment of the present application;

[0024] Figure 2 is a schematic diagram of the structure of an intelligent terminal in an exemplary embodiment of the present application;

[0025] Figure 3 This is a schematic diagram of the structure of an intelligent terminal in a specific embodiment of the present application.

[0026] Figure 4 is a schematic structural diagram of an electronic device in an exemplary embodiment of the present application;

[0027] Figure 5 It is a schematic diagram of the hardware structure of an electronic device in an exemplary embodiment of the present application. DETAILED DESCRIPTION

[0028] In order to make the purpose, technical solutions and advantages of the present application clearer, the present application is further described in detail below in conjunction with specific implementations and with reference to the accompanying drawings. It should be understood that these descriptions are only exemplary and are not intended to limit the scope of the present application. In addition, in the following description, the description of known structures and technologies is omitted to avoid unnecessary confusion of the concepts of the present application.

[0029] The accompanying drawings show schematic diagrams of layer structures according to embodiments of the present application. These figures are not drawn to scale, and some details are magnified and some details may be omitted for the purpose of clarity. The shapes of various regions and layers shown in the figures and the relative sizes and positional relationships therebetween are only exemplary, and may deviate in practice due to manufacturing tolerances or technical limitations, and those skilled in the art may additionally design regions / layers with different shapes, sizes, and relative positions according to actual needs.

[0030] Obviously, the described embodiments are only part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in the field without creative work are within the scope of protection of the present application.

[0031] In the description of the present application, it should be noted that the terms "first", "second" and "third" are only used for descriptive purposes and cannot be understood as indicating or implying relative importance.

[0032] In addition, the technical features involved in the different embodiments of the present application described below can be combined with each other as long as they do not conflict with each other.

[0033] The health status of an axial piston pump is usually characterized by the physical information generated during its operation, such as pressure signals. The health status of an axial piston pump can be evaluated by analyzing and processing the collected signals and constructing a health factor in a reasonable way. The outlet pressure of the piston pump produces periodic pulsations due to the non-continuous oil discharge of the piston and the compressibility of the oil. Its time domain waveform is similar to a sinusoidal signal, but the outlet pressure signal of the piston pump is not a standard sinusoidal signal, that is, the outlet pressure signal of the piston pump has high-order harmonic components in a healthy state, and it is impossible to determine whether the pressure signal is abnormal by the standard harmonic distortion rate formula; however, research has found that a new health factor can be constructed based on the pulsation characteristics of the pressure signal.

[0034] The axial piston pump health status assessment and prediction method and intelligent terminal provided in the embodiment of the present application are described in detail below with reference to the accompanying drawings through specific embodiments and their application scenarios.

[0035] like Figure 1 As shown, in a first aspect of an embodiment of the present application, a method for evaluating and predicting the health status of an axial piston pump is provided. The detection method may include:

[0036] S110: Acquire an outlet pressure signal of the axial piston pump;

[0037] S120: Calculating the health factor of the axial piston pump according to the pulsation characteristics of the pressure signal;

[0038] S130: Determine the health status of the axial piston pump according to the health factor.

[0039] The method of this embodiment constructs a health factor of the axial piston pump according to the pulsation characteristics of the pressure signal, thereby realizing a health assessment of the axial piston pump, thereby preventing major property losses and casualties caused by equipment failure.

[0040] In some embodiments, the pressure signal of the axial piston pump is obtained, specifically:

[0041] Acquire a high-frequency sampled pressure signal at the outlet line of an axial piston pump.

[0042] In order to construct the axial health factor, this embodiment requires high-frequency sampling to obtain the plunger pump outlet pressure pulsation signal to cover the high-order harmonic component information in the pressure pulsation signal and ensure the accuracy of the health assessment results. Studies have found that the frequency range of the axial piston pump pressure pulsation harmonics is 50Hz to 4000Hz. The collected axial piston pump outlet pressure signal needs to contain at least ten harmonics. Therefore, it is more preferred that the plunger pump outlet pressure sensor should meet the natural frequency ≥ 30kHz, and the sampling frequency of the pressure sensor should be ≥ 15kHz. The pressure sensor is installed close to the axial piston pump outlet pipeline so that the pressure pulsation can be accurately monitored. Specifically, a sampling frequency of 15kHz to 50kHz can be selected. During the sampling process, a segmented acquisition strategy can be adopted to collect a fixed length of high-frequency signal at regular intervals.

[0043] In some embodiments, calculating the health factor of the axial piston pump according to the pulsation characteristics of the pressure signal includes:

[0044] The pressure signal is segmented and processed to obtain multiple sampling signals;

[0045] Calculate the health factor of each sampling signal in the multiple sampling signals;

[0046] The health factor of the axial piston pump is calculated according to the health factor of each sampling signal in the multiple sampling signals.

[0047] In this embodiment, the number of sampling points of each 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 a complete rotation cycle of the plunger pump. The data can be corresponded to each rotation cycle of the pump, so as to better calculate the axial health factor.

[0048] In some embodiments, the pressure signal is segmented to obtain multiple sampling signals, including:

[0049] Get the number of sampling points of the axial piston pump during one rotation;

[0050] The pressure signal is segmented according to the number of sampling points, so that the number of sampling points of each sampling signal in the multiple sampling signals is the same as the number of sampling points of one rotation of the axial piston pump.

[0051] In some embodiments, calculating the health factor of each of the plurality of sampling signals includes:

[0052] Convert each sampling signal into pressure pulsation signal frequency;

[0053] Calculate the sum of the fundamental wave of the pressure signal and each order of harmonic energy and the total energy of the pressure signal in the frequency domain;

[0054] The health factor of each sampling signal in the multiple sampling signals is determined according to the sum of the energy of the fundamental wave and each order harmonic of the pressure signal pulsation and the total energy of the pressure signal in the frequency domain.

[0055] Specifically, the health factor is calculated by the following formula:

[0056] f0 = plunger pump rotation frequency f × number of plungers n

[0057] E0=|X(f0)| 2 +|X(2f0)| 2 +|X(3f0)| 2 +...+|X(nf0)| 2

[0058]

[0059]

[0060] Where f0 is the fundamental frequency of the normal pressure signal. f0, 2f0, 3f0, ..., nf0 are the frequencies of each order of harmonics; X is the amplitude of the pressure signal; E0 is the sum of the energy of the fundamental wave of the pressure signal and each order of harmonics; E is the total energy of the pressure signal in the frequency domain; P n is the amplitude of the nth frequency component in the spectrum; HI is the health factor.

[0061] In some embodiments, the health factor of the axial piston pump is calculated according to the health factor of each sampling signal in the multiple sampling signals, specifically:

[0062] The average value of the health factor of each sampling signal in the multiple sampling signals is calculated to obtain the health factor of the axial piston pump.

[0063] In some embodiments, after determining the health status of the axial piston pump according to the health factor, the method for evaluating and predicting the health status of the axial piston pump further includes:

[0064] Use the trained machine learning model to predict the life of an axial piston pump based on health factors.

[0065] In the operation process of the axial piston pump of this embodiment, historical health indicators can be calculated and accumulated. After selecting a suitable network architecture (such as CNN, RNN, LSTM, etc.), setting hyperparameters, selecting loss functions and optimizers, etc., and matching the data set, the model will learn the relationship between health indicators and remaining life.

[0066] Specifically, the trained machine learning model is obtained through the following training:

[0067] Data collection: Collect the outlet pressure signal data of the plunger pump in different health states. These data should include signals from health to failure.

[0068] Data labeling: Label each data sample with the remaining useful life (RUL), which is the time required from the current state to the final failure state. The specific value can be determined by historical failure data or expert knowledge.

[0069] Data screening: remove outliers and noise to ensure data quality.

[0070] Feature extraction and selection: According to the aforementioned health factor construction method, the health factor of each sample when the plunger pump is running is calculated. If necessary, other relevant features can be extracted, such as statistical features of the signal, time-frequency features, etc. The most favorable features for predicting RUL are selected using methods such as correlation analysis and principal component analysis (PCA).

[0071] Model selection: There are many deep learning models that can be considered. For time series data, convolutional neural networks (CNN), recurrent neural networks (RNN) and their variants such as long short-term memory networks (LSTM) and gated recurrent units (GRU) may be more appropriate.

[0072] Training data set division: Divide the data into training set, validation set and test set. The training set is used to train the model, the validation set is used to adjust the parameters, and the test set is used to finally evaluate the model performance.

[0073] Model training: Use the training set to train the machine learning model. Adjust the model parameters to optimize the results based on the performance on the validation set. This can be done manually or using automated methods such as grid search, random search, Bayesian optimization, etc. The model performance can then be evaluated.

[0074] Model deployment: Deploy the trained machine learning model to the production environment for real-time or periodic prediction of the remaining life of the plunger pump.

[0075] Model iteration: Continuously monitor the model’s predictive performance and regularly update the model based on newly collected data to adapt to possible environmental and device changes.

[0076] It should be noted that the axial piston pump health status assessment and prediction method provided in the embodiment of the present application can be executed by an axial piston pump health status assessment and prediction device, or a control module in the axial piston pump health status assessment and prediction device for executing the axial piston pump health status assessment and prediction method. In the embodiment of the present application, the axial piston pump health status assessment and prediction method executed by the axial piston pump health status assessment and prediction device is taken as an example to illustrate the axial piston pump health status assessment and prediction device provided in the embodiment of the present application.

[0077] like Figure 2 As shown, in a second aspect of an embodiment of the present application, an intelligent terminal is provided, and the intelligent terminal may include:

[0078] An acquisition module, used for acquiring an outlet pressure signal of an axial piston pump;

[0079] A calculation module, used for calculating the health factor of the axial piston pump according to the pulsation characteristics of the pressure signal;

[0080] The calculation module is also used to determine the health status of the axial piston pump according to the health factor.

[0081] The health assessment of axial piston pumps requires the selection of a suitable data acquisition system to measure and process the pressure pulsation amplitude and phase. The health assessment process is usually completed after the data is uploaded to the host computer. However, when the high-frequency sampling and collection of the piston pump outlet pressure signal, a large amount of data uploaded to the host computer will cause data processing and response delays, especially in the case of long-term sampling or unstable network connection. This problem is more prominent. In addition, the processing method based on the host computer usually requires higher maintenance costs, increases the number of equipment, and is not conducive to shortening the link length of data collection, transmission and analysis.

[0082] like Figure 3 As shown, in a specific embodiment, an intelligent terminal is provided, including:

[0083] The acquisition module 1 is fixed to the bottom of the aluminum alloy housing 3;

[0084] Computing module 2 is responsible for data parsing, calculation, analysis and uploading, and performs artificial intelligence edge computing;

[0085] The acquisition module 1 and the calculation module 2 are fixed on the top of the housing 3 through copper pillars 4 and the like;

[0086] A sensor interface 5, used for connecting a sensor arranged on the industrial equipment;

[0087] Power supply interface 6, using 24V DC power supply to power acquisition module 1 and computing module 2;

[0088] Video output interface 7, used to connect the computing module 2 with a display device for easy debugging;

[0089] USB interface 8, used to connect the computing module 2 with a USB device;

[0090] The acquisition module network port 9 and the calculation module network port 10 are used for transmitting the acquisition data from the acquisition module 1 to the calculation module 2;

[0091] The network antenna interface 11 is used to provide a wireless network connection function to the computing module 2 .

[0092] In order to better utilize artificial intelligence deep learning technology, an artificial intelligence module is used as the computing module 2 of the intelligent perception terminal, equipped with a graphics processing unit GPU optimized for AI and machine learning workloads, which can execute complex deep learning algorithms at the edge. By deploying different deep learning models in the artificial intelligence module, the remaining useful life (RUL) can be predicted by using the real-time signals or pre-processed features collected by the acquisition module 1 as model input.

[0093] Remaining life prediction based on deep learning can include data collection, health factor (HI) construction, historical HI data accumulation and model training, RUL prediction and other steps. As the degree of failure of the axial piston pump develops, its HI usually shows a degradation trend to varying degrees. The performance degradation process of the axial piston pump includes the healthy stage, the degraded stage and the critical stage. The RUL of the piston pump can be obtained by predicting the time interval when the HI reaches the failure threshold.

[0094] HI prediction can be done through deep neural network models, such as convolutional neural networks, recurrent neural networks, long short-term memory networks, etc. During operation, the axial piston pump can continuously calculate and accumulate historical HI data for deep neural network model training. The trained model can predict the remaining life of the equipment. Since cloud servers usually have higher performance and larger storage space, model training can be completed on cloud servers. Computing module 2 can download the latest deep learning model to the local through SCP (Secure Copy Protocol) instructions for model reasoning.

[0095] The data processing of the intelligent terminal of this embodiment occurs closer to the data source, that is, on the computing module 2, rather than on the host computer or remote server, which reduces the data transmission time, has the ability to analyze high-frequency pressure signals in real time, reduces the dependence on central servers or cloud resources, improves the accuracy and real-time performance of the axial piston pump health assessment prediction, and improves the reliability of the data acquisition terminal in industrial environments with unstable networks. In addition, the computing module 2 can execute complex deep learning algorithms at the edge, can respond faster to changes in the state of the industrial system, and reduces the network burden and the number of devices. The edge and intelligentization of data processing are realized, making the monitoring and predictive maintenance of industrial equipment more efficient and advanced.

[0096] In this embodiment, the computing module 2 is connected to the acquisition module 1 via the TCP protocol, with the acquisition module 1 as the server and the computing module 2 as the client, sending acquisition commands to the acquisition module 1 and receiving the collected data. The pressure signal collected by the acquisition module 1 is saved in a data frame in chronological order, and the size of the numbers in the data frame represents the amplitude of the pressure pulsation. By reading the data at a specific position of the data frame, the outlet pressure signal when the axial piston pump is working can be obtained. As the core processing module, the computing module 2 can replace the traditional host computer, perform real-time parsing, calculation and analysis on the received data, and run the deep learning inference model deployed on the module.

[0097] The acquisition module 1 and the calculation module 2 adopt a two-layer structure, and the overall structure of the device is simple; the overall size of the device can be reduced to 184.5mm×176mm×75mm, which is compact. In addition, the whole can be covered by a 2.5mm thick aluminum alloy shell, which has a good protection function for the internal components.

[0098] The calculation results of health assessment and remaining life are usually transmitted to the cloud for relevant personnel to view. The device can have a wireless network connection function, and the parsed signals and calculation results can be transmitted to the cloud server through the wireless network, such as encapsulated in JSON data format, based on the MQTT protocol. The encapsulated content and the frequency of upload can be set according to needs. Generally, a piece of JSON data usually contains a pressure signal sampling data and a health factor calculation result. The prediction result of the remaining life can be encapsulated once at intervals of multiple pieces of JSON data.

[0099] The computing module 2 has the functions of a traditional host computer and integrates low-power and high-performance artificial intelligence reasoning functions. The collected data no longer needs to be uploaded to the host computer, but is transmitted to the computing module 2 through the network port. The computing module 2 parses, calculates, analyzes and uploads the data. It has wireless network functions, allowing the device to work in an environment without a wired network.

[0100] The intelligent terminal of the above-mentioned embodiment has the capability of high-frequency sampling and real-time processing and analysis of the axial piston pump outlet pressure pulsation signal, thereby realizing the collection of the high-frequency pressure signal of the piston pump, the construction of the health factor and the prediction of the remaining life at the edge.

[0101] The intelligent terminal in the embodiment of the present application can be a device, or a component, integrated circuit, or chip in the terminal. The device can be a mobile electronic device or a non-mobile electronic device. Exemplarily, the mobile electronic device can be a mobile phone, a tablet computer, a laptop computer, a PDA, an in-vehicle electronic device, a wearable device, an ultra-mobile personal computer (UMPC), a netbook, or a personal digital assistant (PDA), etc. The non-mobile electronic device can be a server, a network attached storage (NAS), a personal computer (PC), a television (TV), a teller machine or a self-service machine, etc., which is not specifically limited in the embodiment of the present application.

[0102] The intelligent terminal in the embodiment of the present application may be a device having an operating system. The operating system may be an Android operating system, an IOS operating system, or other possible operating systems, which are not specifically limited in the embodiment of the present application.

[0103] The intelligent terminal provided in the embodiment of the present application can realize Figure 1 To avoid repetition, the various processes implemented by the method embodiment are not described here.

[0104] Alternatively, if Figure 4 As shown, an embodiment of the present application also provides an electronic device 400, including a processor 401, a memory 402, and a program or instruction stored in the memory 402 and executable on the processor 401. When the program or instruction is executed by the processor 401, each process of the above-mentioned axial piston pump health status assessment and prediction method embodiment is implemented, and the same technical effect can be achieved. To avoid repetition, it will not be repeated here.

[0105] It should be noted that the electronic devices in the embodiments of the present application include the mobile electronic devices and non-mobile electronic devices mentioned above.

[0106] Figure 5 A schematic diagram of the hardware structure of an electronic device to implement an embodiment of the present application.

[0107] The electronic device 500 includes but is not limited to: a radio frequency unit 501, a network module 502, an audio output unit 503, an input unit 504, a sensor 505, a display unit 506, a user input unit 507, an interface unit 508, a memory 509, and a processor 510.

[0108] Those skilled in the art will appreciate that the electronic device 500 may also include a power source (such as a battery) for supplying power to various components, and the power source may be logically connected to the processor 510 through a power management system, thereby implementing functions such as managing charging, discharging, and power consumption management through the power management system. Figure 5 The electronic device structure shown in the figure does not constitute a limitation on the electronic device. The electronic device may include more or fewer components than shown in the figure, or combine certain components, or arrange the components differently, which will not be described in detail here.

[0109] It should be understood that in the embodiment of the present application, the input unit 504 may include a graphics processor (Graphics Processing Unit, GPU) 5041 and a microphone 5042, and the graphics processor 5041 processes the image data of the static picture or video obtained by the image capture device (such as a camera) in the video capture mode or the image capture 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, etc. The user input unit 507 includes a touch panel 5071 and other input devices 5072. The touch panel 5071 is also called a touch screen. The 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, a function key (such as a volume control button, a switch button, etc.), a trackball, a mouse, and a joystick, which will not be repeated here. The memory 509 can be used to store software programs and various data, including but not limited to applications and operating systems. The processor 510 can integrate an application processor and a modem processor, wherein the application processor mainly processes an operating system, a user interface, and applications, etc., and the modem processor mainly processes wireless communications. It is understandable that the above-mentioned modem processor may not be integrated into the processor 510.

[0110] An embodiment of the present application also provides a readable storage medium, on which a program or instruction is stored. When the program or instruction is executed by a processor, each process of the above-mentioned axial piston pump health status assessment and prediction method embodiment is implemented, and the same technical effect can be achieved. To avoid repetition, it will not be repeated here.

[0111] 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 computer read-only memory (ROM), a random access memory (RAM), a magnetic disk or an optical disk.

[0112] An embodiment of the present application further provides a chip, which includes a processor and a communication interface, wherein the communication interface is coupled to the processor, and the processor is used to run programs or instructions to implement the various processes of the above-mentioned axial piston pump health status assessment and prediction method embodiment, and can achieve the same technical effect. To avoid repetition, it will not be repeated here.

[0113] It should be understood that the chip mentioned in the embodiments of the present application can also be called a system-level chip, a system chip, a chip system or a system-on-chip chip, etc.

[0114] It should be noted that, in this article, the terms "comprise", "include" or any other variant thereof are intended to cover non-exclusive inclusion, so that the process, method, article or device including a series of elements includes not only those elements, but also includes other elements not explicitly listed, or also includes elements inherent to such process, method, article or device. In the absence of further restrictions, the elements defined by the sentence "comprise one..." do not exclude the presence of other identical elements in the process, method, article or device including the element. In addition, it should be pointed out that the scope of the methods and devices in the embodiments of the present application is not limited to performing functions in the order shown or discussed, and may also include performing functions in a substantially simultaneous manner or in reverse order according to the functions involved, for example, the described method may be performed in an order different from that described, and various steps may also be added, omitted, or combined. In addition, the features described with reference to certain examples may be combined in other examples.

[0115] Through the description of the above implementation methods, those skilled in the art can clearly understand that the above-mentioned embodiment methods can be implemented by means of software plus a necessary general hardware platform, and of course by hardware, but in many cases the former is a better implementation method. Based on such an understanding, the technical solution of the present application, or the part that contributes to the prior art, can be embodied in the form of a computer software product, which is stored in a storage medium (such as ROM / RAM, a disk, or an optical disk), and includes a number of instructions for a terminal (which can be a mobile phone, a computer, a server, or a network device, etc.) to execute the methods described in each embodiment of the present application.

[0116] The embodiments of the present application are described above in conjunction with the accompanying drawings, but the present application is not limited to the above-mentioned specific implementation methods. The above-mentioned specific implementation methods are merely illustrative and not restrictive. Under the guidance of the present application, ordinary technicians in this field can also make many forms without departing from the purpose of the present application and the scope of protection of the claims, all of which are within the protection of the present application.

Claims

1. A method for evaluating and predicting the health status of an axial piston pump, characterized in that: include: Obtain the pressure signal of the axial piston pump; Calculating a health factor of the axial piston pump according to the pulsation characteristics of the pressure signal; determining a health status of the axial piston pump according to the health factor; Calculating the health factor of the axial piston pump according to the pulsation characteristics of the pressure signal includes: Segmenting the pressure signal to obtain multiple sampling signals; Calculating the health factor of each sampling signal in the plurality of sampling signals; Calculating the health factor of the axial piston pump according to the health factor of each sampling signal in the multiple sampling signals; The calculating the health factor of each of the plurality of sampling signals comprises: Convert each sampling signal into pressure pulsation signal frequency; Calculate the sum of the fundamental wave of the pressure signal and each order of harmonic energy and the total energy of the pressure signal in the frequency domain; Determine the health factor of each sampling signal in the multiple sampling signals according to the sum of the energy of the fundamental wave and each order harmonic of the pressure signal pulsation and the total energy of the pressure signal in the frequency domain; The health factor is calculated using the following formula: f0 = plunger pump rotation frequency f × number of plungers n E0=|X(f0)| 2 +|X(2f0)| 2 +|X(3f0)| 2 +...+|X(nf0)| 2 Where f0 is the fundamental frequency of the normal pressure signal; f0, 2f0, 3f0, ..., nf0 are the frequencies of each order of harmonics; X is the amplitude of the pressure signal; E0 is the sum of the energy of the fundamental wave of the pressure signal pulsation and each order of harmonics; E is the total energy of the pressure signal in the frequency domain; P n is the amplitude of the nth frequency component in the spectrum; HI is the health factor.

2. The method for evaluating and predicting the health status of an axial piston pump according to claim 1, characterized in that: The obtaining of the pressure signal of the axial piston pump is specifically as follows: Acquire a high-frequency sampled pressure signal at the outlet line of an axial piston pump.

3. The method for evaluating and predicting the health status of an axial piston pump according to claim 1, characterized in that: The step of segmenting the pressure signal to obtain multiple sampling signals includes: Obtaining the number of sampling points of the axial piston pump during one rotation; The pressure signal is segmented according to the number of sampling points, so that the number of sampling points of each segment of the multiple sampling signals is the same as the number of sampling points of one rotation of the axial piston pump.

4. The method for evaluating and predicting the health status of an axial piston pump according to claim 1, characterized in that: The health factor of the axial piston pump is calculated according to the health factor of each sampling signal in the multiple sampling signals, specifically: The average value of the health factor of each sampling signal in the multiple sampling signals is calculated to obtain the health factor of the axial piston pump.

5. The method for evaluating and predicting the health status of an axial piston pump according to claim 1, characterized in that: After determining the health status of the axial piston pump according to the health factor, the axial piston pump health status assessment and prediction method further includes: The life of the axial piston pump is predicted based on the health factor using the trained machine learning model.

6. An intelligent terminal, characterized in that: include: An acquisition module, used for acquiring an outlet pressure signal of an axial piston pump; A calculation module, configured to calculate a health factor of the axial piston pump according to the pressure signal; The calculation module is further used to determine the health status of the axial piston pump according to the health factor; The calculation module is specifically used to segment the pressure signal to obtain multiple sampling signals; calculate the health factor of each sampling signal in the multiple sampling signals; calculate the health factor of the axial piston pump according to the health factor of each sampling signal in the multiple sampling signals; The step of calculating the health factor of each of the plurality of sampling signals comprises: Convert each sampling signal into pressure pulsation signal frequency; Calculate the sum of the fundamental wave of the pressure signal and each order of harmonic energy and the total energy of the pressure signal in the frequency domain; Determine the health factor of each sampling signal in the multiple sampling signals according to the sum of the energy of the fundamental wave and each order harmonic of the pressure signal pulsation and the total energy of the pressure signal in the frequency domain; The health factor is calculated using the following formula: f0 = plunger pump rotation frequency f × number of plungers n E0=|X(f0)| 2 +|X(2f0)| 2 +|X(3f0)| 2 +...+|X(nf0)| 2 Where f0 is the fundamental frequency of the normal pressure signal; f0, 2f0, 3f0, ..., nf0 are the frequencies of each order of harmonics; X is the amplitude of the pressure signal; E0 is the sum of the energy of the fundamental wave of the pressure signal pulsation and each order of harmonics; E is the total energy of the pressure signal in the frequency domain; P n is the amplitude of the nth frequency component in the spectrum; HI is the health factor.

7. An electronic device, characterized in that: include: A processor, a memory, and a program or instruction stored in the memory and executable on the processor, wherein the program or instruction, when executed by the processor, implements the steps of the axial piston pump health status assessment and prediction method as described in any one of claims 1 to 5.

8. A readable storage medium, characterized in that: The readable storage medium stores a program or instruction, and when the program or instruction is executed by the processor, the steps of the axial piston pump health status assessment and prediction method according to any one of claims 1 to 5 are implemented.

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