Plunger pump anomaly detection method based on simulation and test data similarity analysis

Abstract

The invention discloses a plunger pump anomaly detection method based on simulation and test data similarity analysis, and belongs to the field of plunger pump detection.The method comprises the steps that an ideal waveform for monitoring a plunger pump outlet pressure signal is generated through simulation, and a standard reference signal is obtained; a plunger pump outlet pressure signal in a healthy state is collected, and an actually-measured healthy signal is obtained; calculating a modal distance of a weighted graph similar matrix between the actually measured health signal and the standard reference signal to obtain a similarity index; determining an alarm threshold value based on the similarity index; monitoring a pressure signal of an outlet of the to-be-detected plunger pump to obtain a to-be-detected signal; calculating the similarity between the to-be-detected signal and the standard reference signal; and when the similarity is lower than an alarm threshold value in the continuous target number of times of detection, determining that the plunger pump to be detected is abnormal. According to the method, an anomaly detection model can be established on the premise of no fault sample, the actual problem of fault data scarcity is effectively solved, and the method conforms to the engineering practical application background.

Description

Technical Field

[0001] The present application belongs to the technical field of plunger pump detection, and specifically relates to a plunger pump anomaly detection method based on similarity analysis of simulation and test data. Background Art

[0002] The plunger pump has the advantages of high power density, compact structure and high efficiency, and is widely used in aerospace, engineering machinery, automotive industry and other fields. As a key energy supply component of the hydraulic system, the health status of the plunger pump will directly affect the working efficiency and safety of the hydraulic system.

[0003] In order to ensure the normal operation of the hydraulic system, it is necessary to monitor the health status of the plunger pump in real time to detect early failures of the plunger pump, so as to reasonably arrange the hydraulic system maintenance plan and avoid accident risks and economic losses caused by emergency shutdown.

[0004] In actual engineering applications, since the equipment runs in a healthy state for a long time, abnormal data is scarce, resulting in poor results of data-driven anomaly detection methods. Summary of the invention

[0005] The purpose of this application is to provide a plunger pump anomaly detection method based on similarity analysis of simulation and test data to achieve plunger pump anomaly detection when anomaly data is scarce.

[0006] According to a first aspect of an embodiment of the present application, a plunger pump abnormality detection method based on similarity analysis of simulation and test data is provided, and the method may include:

[0007] The ideal waveform of the monitoring plunger pump outlet pressure signal is generated by simulation to obtain a standard reference signal; and the plunger pump outlet pressure signal under a healthy state is collected to obtain a measured healthy signal;

[0008] Calculate the modal distance of the weighted graph similarity matrix between the measured health signal and the standard reference signal to obtain a similarity index; and determine the alarm threshold based on the similarity index;

[0009] Monitor the pressure signal at the outlet of the plunger pump to be tested to obtain the signal to be tested; and calculate the similarity between the signal to be tested and the standard reference signal;

[0010] When the similarity is measured continuously for a target number of times and is lower than the alarm threshold, it is determined that the plunger pump to be tested is abnormal.

[0011] In some optional embodiments of the present application, collecting the plunger pump outlet pressure signal in a healthy state to obtain a measured healthy signal includes:

[0012] The pressure sensor is installed at the outlet of the plunger pump in the hydraulic pipeline in a healthy state to collect the pressure signal of the plunger pump in a healthy state; the hydraulic pipeline is a rigid hard pipe;

[0013] The pressure signal is converted by the digital acquisition circuit into a digital signal to obtain a measured health signal. In some optional embodiments of the present application, the sampling frequency of the pressure sensor satisfies the following formula:

[0014] f s ≥10nm / 60

[0015] Among them, f s is the sampling frequency; n is the speed of the plunger pump, in rpm; m is the number of plungers.

[0016] In some optional embodiments of the present application, after the pressure sensor is installed at the outlet of the plunger pump in the hydraulic pipeline in a healthy state to collect the pressure signal of the plunger pump in a healthy state, the plunger pump abnormality detection method based on the similarity analysis of simulation and test data further includes:

[0017] The pressure signal is resampled to 360 points / revolution using interpolation method and truncated to a length of 400 as pressure signal samples.

[0018] In some optional embodiments of the present application, the modal distance of the weighted graph similarity matrix between the measured health signal and the standard reference signal is calculated to obtain a similarity index, including:

[0019] Use the sliding window method to intercept the pressure signal samples of the measured health signal, calculate the similarity between the pressure signal samples and the standard reference signal, and take the sample with the maximum similarity as the aligned sample;

[0020] According to graph similarity theory, the standard reference signal and the homogenized samples are respectively used to generate the corresponding graph adjacency matrix;

[0021] Create a weight matrix graph of the same size as the adjacency matrix graph, and use the Hadamard product of the weight matrix graph and the adjacency matrix graph as its weighted graph similarity matrix;

[0022] The distance between weighted graph similarity matrices is used as the similarity measure between the measured health signal and the standard reference signal to obtain the similarity index.

[0023] In some optional embodiments of the present application, the sample length intercepted by the sliding window method is 360 to ensure that the intercepted sample length is consistent with the standard reference signal, the sliding step length of the sliding window is 1, and the maximum offset distance is 40.

[0024] In some optional embodiments of the present application, the offset phase when the pressure signal sample is aligned with the standard reference signal is calculated by the following formula:

[0025] x align =argmax S(G(s),G(w(x)))

[0026] Among them, x align is the aligned pressure signal sample, s is the standard reference signal, x is the pressure signal sample, w(·) is the window function, G is the mapping function from the input signal to the weighted graph similarity matrix, and S represents the calculated similarity.

[0027] According to a second aspect of an embodiment of the present application, a plunger pump abnormality detection device based on similarity analysis of simulation and test data is provided, and the device may include:

[0028] The signal acquisition module is used to simulate and generate an ideal waveform for monitoring the outlet pressure signal of the plunger pump to obtain a standard reference signal; and to collect the outlet pressure signal of the plunger pump in a healthy state to obtain a measured healthy signal;

[0029] A calculation module is used to calculate the modal distance of the weighted graph similarity matrix between the measured health signal and the standard reference signal to obtain a similarity index; and determine an alarm threshold based on the similarity index;

[0030] A monitoring module is used to monitor the pressure signal at the outlet of the plunger pump to be tested, obtain the signal to be tested, and calculate the similarity between the signal to be tested and the standard reference signal;

[0031] The determination module is used to determine that the plunger pump to be tested is abnormal when the similarity is lower than the alarm threshold for a continuous target number of times.

[0032] According to a third aspect of an embodiment of the present application, an electronic device is provided, which may include:

[0033] processor;

[0034] a memory for storing processor-executable instructions;

[0035] Wherein, the processor is configured to execute instructions to implement the plunger pump abnormality detection method based on similarity analysis of simulation and test data as shown in any one of the embodiments of the first aspect.

[0036] 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 a plunger pump abnormality detection method based on similarity analysis of simulation and experimental data as shown in any one of the embodiments of the first aspect.

[0037] The above technical solution of the present application has the following beneficial technical effects:

[0038] The method of the embodiment of the present application can establish an anomaly detection model under the premise of no fault samples, which effectively solves the practical problem of scarce fault data and conforms to the actual application background of engineering; and, by using the simulation signal as the standard reference signal and the similarity between the monitoring signal and the simulation reference signal as the characteristic indicator of monitoring, the abnormal samples and normal samples have a high degree of distinction, and the similarity indicator has a clear physical meaning, and has good interpretability and reliability; in addition, the method measures the health status of the plunger pump by the similarity between the monitoring signal and the simulation reference signal, and can detect slight deformations of the pressure pulsation waveform of the plunger pump, and has a high sensitivity to early failures of the plunger pump. BRIEF DESCRIPTION OF THE DRAWINGS

[0039] Figure 1 It is a flow chart of a plunger pump abnormality detection method based on similarity analysis of simulation and test data in an exemplary embodiment of the present application;

[0040] Figure 2 It is a structural schematic diagram of a plunger pump abnormality detection device based on similarity analysis of simulation and test data in an exemplary embodiment of the present application;

[0041] Figure 3 It is a structural schematic diagram of a plunger pump abnormality detection system based on similarity analysis of simulation and test data in an exemplary embodiment of the present application;

[0042] Figure 4 It is a flow chart of a plunger pump abnormality detection method based on similarity analysis of simulation and test data in an exemplary embodiment of the present application;

[0043] Figure 5 It is a flow chart of a signal similarity evaluation module in a plunger pump abnormality detection method based on similarity analysis of simulation and test data in an exemplary embodiment of the present application;

[0044] Figure 6 is a waveform diagram of a standard reference signal in an exemplary embodiment of the present application;

[0045] Figure 7 is a waveform diagram of a measured health signal in an exemplary embodiment of the present application;

[0046] Figure 8 is a waveform diagram of a signal to be detected in an exemplary embodiment of the present application;

[0047] Fig. 9 is the detection result of a plunger pump abnormality detection method based on similarity analysis of simulation and test data in an exemplary embodiment of the present application;

[0048] Fig.10 is a schematic structural diagram of an electronic device in an exemplary embodiment of the present application;

[0049] Fig.11 It is a schematic diagram of the hardware structure of an electronic device in an exemplary embodiment of the present application. DETAILED DESCRIPTION

[0050] 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.

[0051] 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.

[0052] 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.

[0053] 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.

[0054] 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.

[0055] Among all monitoring signals of hydraulic systems, pressure signals have the lowest acquisition cost and are the most widely used, so the health status of plunger pumps is often monitored through pressure signals.

[0056] The plunger pump anomaly detection method based on simulation and test data similarity analysis provided in the embodiment of the present application is described in detail below with reference to the accompanying drawings through specific embodiments and their application scenarios.

[0057] like Figure 1 As shown, in a first aspect of an embodiment of the present application, a plunger pump abnormality detection method based on similarity analysis of simulation and test data is provided, and the method may include:

[0058] S110: Simulate and generate an ideal waveform for monitoring the outlet pressure signal of the plunger pump to obtain a standard reference signal; and collect the outlet pressure signal of the plunger pump in a healthy state to obtain a measured healthy signal;

[0059] S120: Calculate the modal distance of the weighted graph similarity matrix between the measured health signal and the standard reference signal to obtain a similarity index; and determine an alarm threshold based on the similarity index;

[0060] S130: monitoring the pressure signal at the outlet of the plunger pump to be tested, obtaining a signal to be tested; and calculating the similarity between the signal to be tested and a standard reference signal;

[0061] S140: When the similarity is lower than the alarm threshold in the detection of the consecutive target number of times, it is determined that the plunger pump to be tested is abnormal.

[0062] The method of this embodiment can establish an anomaly detection model under the premise of no fault samples, which effectively solves the practical problem of scarce fault data and conforms to the actual application background of engineering; and by using the simulation signal as the standard reference signal and the similarity between the monitoring signal and the simulation reference signal as the characteristic indicator of monitoring, the abnormal sample and the normal sample have a high degree of distinction, and the similarity indicator has a clear physical meaning, and has good interpretability and reliability; in addition, the method measures the health status of the plunger pump by the similarity between the monitoring signal and the simulation reference signal, and can detect the slight deformation of the pressure pulsation waveform of the plunger pump, and has a high sensitivity to the early failure of the plunger pump. The target number in this embodiment is a preset value.

[0063] In some embodiments, collecting the plunger pump outlet pressure signal in a healthy state to obtain a measured healthy signal includes:

[0064] The pressure sensor is installed at the outlet of the plunger pump in the hydraulic pipeline in a healthy state to collect the pressure signal of the plunger pump in a healthy state; the hydraulic pipeline is a rigid hard pipe;

[0065] The pressure signal is converted into a digital signal by the digital acquisition circuit to obtain the measured health signal.

[0066] In this embodiment, the pressure sensor is a high frequency pressure sensor. Since the hydraulic pipeline between the pressure sensor and the outlet of the plunger pump should be a rigid hard pipe, the quality of the sampled signal can be guaranteed.

[0067] In some embodiments, the sampling frequency of the pressure sensor satisfies the following formula:

[0068] f s ≥10nm / 60

[0069] Among them, f s is the sampling frequency; n is the speed of the plunger pump, in rpm; m is the number of plungers.

[0070] By using the sampling frequency of this embodiment, it is possible to ensure that the pressure pulsation waveform is not distorted.

[0071] In some embodiments, after the pressure sensor is installed at the outlet of the plunger pump in the healthy state in the hydraulic pipeline to collect the pressure signal of the plunger pump in the healthy state, the plunger pump abnormality detection method based on the similarity analysis of simulation and test data further includes:

[0072] The pressure signal is resampled to 360 points / revolution using interpolation method and truncated to a length of 400 as pressure signal samples.

[0073] In this embodiment, the plunger pump outlet pressure signal is resampled to 360 points / revolution using an interpolation method, which can ensure that it is consistent with the standard reference signal.

[0074] In some embodiments, the modal distance of the weighted graph similarity matrix between the measured health signal and the standard reference signal is calculated to obtain a similarity index, including:

[0075] Use the sliding window method to intercept the pressure signal samples of the measured health signal, calculate the similarity between the pressure signal samples and the standard reference signal, and take the sample with the maximum similarity as the aligned sample;

[0076] According to graph similarity theory, the standard reference signal and the homogenized samples are respectively used to generate the corresponding graph adjacency matrix;

[0077] Create a weight matrix graph of the same size as the adjacency matrix graph, and use the Hadamard product of the weight matrix graph and the adjacency matrix graph as its weighted graph similarity matrix;

[0078] The distance between weighted graph similarity matrices is used as the similarity measure between the measured health signal and the standard reference signal to obtain the similarity index.

[0079] In some embodiments, the sample length intercepted by the sliding window method is 360 to ensure that the intercepted sample length is consistent with the standard reference signal, the sliding step length of the sliding window is 1, and the maximum offset distance is 40.

[0080] In some embodiments, the offset phase when the pressure signal sample is aligned with the standard reference signal is calculated by the following formula:

[0081] x align =argmax S(G(s),G(w(x)))

[0082] Among them, x align is the aligned pressure signal sample, s is the standard reference signal, x is the pressure signal sample, w(·) is the window function, G is the mapping function from the input signal to the weighted graph similarity matrix, and S represents the calculated similarity.

[0083] During the calculation process, a one-dimensional signal with a length of 360 is generated into a 360×360 adjacency matrix D. The value of each element in the matrix is ​​defined by the following formula:

[0084]

[0085] In the formula, y i Represents the value of the i-th point in a one-dimensional signal, d i,j is an element in the adjacency matrix D, where i and j are the rows and columns of the element.

[0086] The weight matrix W of size 360×360 is defined to assign weights to the adjacency matrix. Generally, the weight matrix assigns higher weights to elements closer to the main diagonal of the matrix.

[0087] The weight matrix is ​​defined as follows:

[0088]

[0089] In the formula, w i,j is an element in the adjacency matrix W, where i and j are the rows and columns of the element.

[0090] The weighted graph similarity matrix is ​​defined as the Hadamard product of the weight matrix graph and the adjacency matrix, that is:

[0091] ε=W⊙D

[0092] Where ⊙ is the calculation of Hadamard product, and ε represents the weighted graph similarity matrix.

[0093] The distance between the weighted graph similarity matrices corresponding to the standard reference signal and the monitoring signal is calculated, and the above distance is normalized to measure the similarity of the two input signals.

[0094] The similarity measure is defined using the following formula:

[0095] S Modality =1-||π(ε)-π(ε′)||2 / max(||π(ε)||2,||π(ε′)||2)

[0096] Where S Modality represents the modal similarity, ε and ε' represent the weighted graph similarity matrix generated by the standard reference signal and the monitoring signal, π(·) represents the Perron vector of the calculation matrix, ||·||2 represents the 2-norm of the calculation vector, and max(·,·) is the maximum value.

[0097] Furthermore, other similarity measures can be used to evaluate the similarity of two matrices, but the similarity measures should have the following properties:

[0098] Property 1: Normalization, the similarity between any two inputs should be in the range of 0-1, that is, S(G,G')∈[0,1];

[0099] Property 2: Identity, the similarity between the same signal and itself is 1, that is, S(G,G) = 1;

[0100] Property 3: Symmetry. When the two input signals are exchanged, the similarity remains unchanged, that is, S(G,G') = S(G',G).

[0101] In some embodiments, according to the similarity value distribution, the 3σ criterion is adopted, and the 3σ lower bound of the similarity index is taken as the alarm threshold. According to the similarity value distribution, the minimum similarity value is taken as the alarm threshold.

[0102] It should be noted that the plunger pump anomaly detection method based on similarity analysis of simulation and test data provided in the embodiment of the present application can be executed by a plunger pump anomaly detection device based on similarity analysis of simulation and test data, or a control module in the plunger pump anomaly detection device based on similarity analysis of simulation and test data for executing the method of plunger pump anomaly detection based on similarity analysis of simulation and test data. In the embodiment of the present application, the plunger pump anomaly detection device based on similarity analysis of simulation and test data executes the method of plunger pump anomaly detection based on similarity analysis of simulation and test data as an example to illustrate the device for plunger pump anomaly detection based on similarity analysis of simulation and test data provided in the embodiment of the present application.

[0103] like Figure 2 As shown, in a second aspect of an embodiment of the present application, a plunger pump abnormality detection device based on similarity analysis of simulation and test data is provided, and the device may include:

[0104] The signal acquisition module 210 is used to simulate and generate an ideal waveform for monitoring the outlet pressure signal of the plunger pump to obtain a standard reference signal; and to collect the outlet pressure signal of the plunger pump in a healthy state to obtain a measured healthy signal;

[0105] The calculation module 220 is used to calculate the modal distance of the weighted graph similarity matrix between the measured health signal and the standard reference signal to obtain a similarity index; and determine the alarm threshold based on the similarity index;

[0106] The monitoring module 230 is used to monitor the pressure signal at the outlet of the plunger pump to be tested, obtain the signal to be tested, and calculate the similarity between the signal to be tested and the standard reference signal;

[0107] The determination module 240 is used to determine that the plunger pump to be tested is abnormal when the similarity is lower than the alarm threshold in the detection of consecutive target times.

[0108] In a specific embodiment, a plunger pump abnormality detection device based on similarity analysis of simulation and test data can build an abnormality detection system established by a plunger pump abnormality detection method, such as Figure 3 shown.

[0109] The anomaly detection system consists of monitoring sensors in the hydraulic system, edge monitoring and early warning systems, and cloud simulation computing centers. In the plunger pump anomaly detection system, a simulation model is established based on the plunger pump parameters in the hydraulic system, and the simulation model is deployed in the cloud computing center to generate simulation reference signals.

[0110] In the plunger pump anomaly detection system, the anomaly monitoring model is arranged in the edge computing node, and the anomaly detection method is deployed in the edge computing node. The anomaly detection model is composed of a simulation reference signal, a similarity evaluation module and a threshold, which is used to monitor the health status of the plunger pump.

[0111] In the edge computing node, the edge computing terminal has modules such as CPU, network port communication, and 5G communication to realize functions such as monitoring signal processing and analysis calculation, communication between edge nodes and AD conversion modules, cloud computing centers, and databases.

[0112] In the plunger pump anomaly detection system, the pressure sensor is installed at the outlet of the monitored hydraulic pump for pressure signal monitoring; the signal is input to the edge computing node through network port communication after AD conversion.

[0113] Exemplarily, the plunger pump outlet pressure signal is cut into pressure signal segments with a length of 400 to meet the requirements of the sliding window method signal alignment. Because the maximum offset of the sliding window is 40, the final cut sample length is 360, so the cut signal length is 360+40=400.

[0114] Furthermore, the pressure signal segment and the simulated reference signal are input into the similarity evaluation module to calculate the similarity of the two signals. The monitoring signal is sampled several times at intervals, and the similarity obtained is calculated in turn with the similarity of the reference signal. If the similarity of z consecutive samples is lower than the threshold, it is determined that the plunger pump is abnormal, where z is a preset parameter.

[0115] In the plunger pump anomaly detection system, the edge computing terminal monitors the operating information of the plunger pump (such as pump speed, pressure, swash plate inclination, etc.) through sensors. When the operating conditions of the plunger pump change, the edge computing node sends the new operating parameters to the cloud computing center; the cloud computing center re-simulates the calculation by changing the simulation model, and sends the generated new simulation pressure signal to the edge computing node for updating the reference signal.

[0116] The cloud computing center and the edge computing node communicate through the http protocol via a wired network port or a wireless network port. The edge computing node sends real-time working condition information to the cloud computing center, and the cloud computing center sends simulation signals to the edge computing node to update the model.

[0117] The edge computing terminal stores and reports the monitored pressure pulsation waveform, operating condition information and calculated similarity value, and the stored data is stored in the form of a timing signal.

[0118] Optionally, based on data storage requirements, the above data can be uploaded to a server database or cloud database via wireless networks, wired networks, 5G communications, etc.

[0119] Specifically, the detection process of the abnormal monitoring method is as follows: Figure 4-5 As shown; the plunger pump outlet pressure simulation reference signal waveform, such as Figure 6 As shown; Figure 7 This is a normal sample time domain diagram of the plunger pump outlet pressure signal; Figure 8 FIG. 1 is a time domain diagram of an abnormal sample of the outlet pressure signal of the plunger pump; FIG. 2 is a result of abnormal detection of the test sample using the method of the embodiment of the present application, as shown in FIG. Fig. 9 shown.

[0120] The plunger pump anomaly detection device based on the similarity analysis of simulation and test data in the embodiment of the present application can be a device, or a component, integrated circuit, or chip in a 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 handheld computer, an in-vehicle electronic device, a wearable device, an ultra-mobile personal computer (ultra-mobile personal computer, UMPC), a netbook or a personal digital assistant (personal digital assistant, PDA), etc., and the non-mobile electronic device can be a server, a network attached storage (Network Attached Storage, NAS), a personal computer (personalcomputer, PC), a television (television, TV), a teller machine or a self-service machine, etc., and the embodiment of the present application is not specifically limited.

[0121] The plunger pump anomaly detection device based on simulation and test data similarity analysis in the embodiment of the present application can be a device with an operating system. The operating system can be a Windows operating system, a Linux operating system, 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.

[0122] The plunger pump abnormality detection device based on simulation and test data similarity analysis provided in the embodiment of the present application can achieve Figure 1 To avoid repetition, the various processes implemented by the method embodiment are not described here.

[0123] Alternatively, if Fig.10 As shown, an embodiment of the present application also provides an electronic device 1000, including a first processor 1001, a second memory 1002, and a program or instruction stored in the second memory 1002 and executable on the first processor 1001. When the program or instruction is executed by the first processor 1001, each process of the above-mentioned plunger pump abnormality detection method embodiment based on similarity analysis of simulation and test data is implemented, and the same technical effect can be achieved. To avoid repetition, it will not be repeated here.

[0124] 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.

[0125] Fig.11 A schematic diagram of the hardware structure of an electronic device to implement an embodiment of the present application.

[0126] The hardware structure 1100 of the electronic device includes but is not limited to: a radio frequency unit 1101, a network module 1102, an audio output unit 1103, an input unit 1104, a sensor 1105, a display unit 1106, a user input unit 1107, an interface unit 1108, a second memory 1109, and a second processor 1110 and other components.

[0127] Those skilled in the art will appreciate that the hardware structure 1100 of the electronic device may also include a power source (such as a battery) for supplying power to each component, and the power source may be logically connected to the second processor 1110 through a power management system, thereby implementing functions such as managing charging, discharging, and power consumption management through the power management system. Fig.11 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.

[0128] It should be understood that in the embodiment of the present application, the input unit 1104 may include a graphics processor (Graphics Processing Unit, GPU) 11041 and a microphone 11042, and the graphics processor 11041 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 1106 may include a display panel 11061, and the display panel 11061 may be configured in the form of a liquid crystal display, an organic light emitting diode, etc. The user input unit 1107 includes a touch panel 11071 and other input devices 11072. The touch panel 11071 is also called a touch screen. The touch panel 11071 may include two parts: a touch detection device and a touch controller. Other input devices 11072 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 second memory 1109 can be used to store software programs and various data, including but not limited to applications and operating systems. The second processor 1110 may integrate an application processor and a modem processor, wherein the application processor mainly processes an operating system, a user interface, and application programs, and the modem processor mainly processes wireless communications. It is understandable that the modem processor may not be integrated into the second processor 1110.

[0129] 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 plunger pump abnormality detection method embodiment based on similarity analysis of simulation and test data is implemented, and the same technical effect can be achieved. To avoid repetition, it will not be repeated here.

[0130] 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.

[0131] 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 plunger pump abnormality detection method embodiment based on similarity analysis of simulation and test data, and can achieve the same technical effect. To avoid repetition, it will not be repeated here.

[0132] 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.

[0133] 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.

[0134] 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.

[0135] 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 plunger pump anomaly detection method based on similarity analysis of simulation and test data, characterized in that: include: The ideal waveform of the monitoring piston pump outlet pressure signal is generated by simulation to obtain the standard reference signal; And collect the outlet pressure signal of the plunger pump in a healthy state to obtain the measured healthy signal; Calculating the modal distance of the weighted graph similarity matrix between the measured health signal and the standard reference signal to obtain a similarity index; and determining an alarm threshold based on the similarity index; Monitor the pressure signal at the outlet of the plunger pump to be tested to obtain a signal to be tested; and calculate the similarity between the signal to be tested and the standard reference signal; When the similarity is lower than the alarm threshold in the detection of the target number of consecutive times, it is determined that the plunger pump to be tested is abnormal.

2. The plunger pump anomaly detection method based on simulation and test data similarity analysis according to claim 1 is characterized in that: The collecting of the plunger pump outlet pressure signal in a healthy state to obtain a measured healthy signal includes: The pressure sensor is installed at the outlet of the plunger pump in the healthy state in the hydraulic pipeline to collect the pressure signal of the plunger pump in the healthy state; the hydraulic pipeline is a rigid hard pipe; The pressure signal is converted into a digital signal by a digital acquisition circuit to obtain a measured health signal.

3. The plunger pump abnormality detection method based on simulation and test data similarity analysis according to claim 2 is characterized in that: The sampling frequency of the pressure sensor satisfies the following formula: f s ≥10nm / 60 Among them, f s is the sampling frequency; n is the speed of the plunger pump, in rpm; m is the number of plungers.

4. The plunger pump anomaly detection method based on simulation and test data similarity analysis according to claim 3 is characterized in that: After the pressure sensor is installed at the outlet of the plunger pump in the healthy state in the hydraulic pipeline to collect the pressure signal of the plunger pump in the healthy state, the plunger pump abnormality detection method based on the similarity analysis of simulation and test data further includes: The pressure signal is resampled to 360 points / revolution using an interpolation method and is truncated to a length of 400 as a pressure signal sample.

5. The plunger pump anomaly detection method based on simulation and test data similarity analysis according to claim 1 is characterized in that: The calculating the modal distance of the weighted graph similarity matrix between the measured healthy signal and the standard reference signal to obtain a similarity index includes: Using a sliding window method to intercept a pressure signal sample of the measured health signal, calculating the similarity between the pressure signal sample and the standard reference signal, and taking the sample with the maximum similarity as the aligned sample; According to graph similarity theory, the standard reference signal and the homogenized samples are respectively used to generate corresponding graph adjacency matrices; Establishing a weight matrix graph of the same size as the adjacency matrix graph, and using the Hadamard product of the weight matrix graph and the adjacency matrix graph as its weighted graph similarity matrix; The distance between the weighted graph similarity matrices is used as a similarity measure between the measured healthy signal and the standard reference signal to obtain a similarity index.

6. The plunger pump anomaly detection method based on similarity analysis of simulation and test data according to claim 5 is characterized in that: The sample length intercepted by the sliding window method is 360 to ensure that the intercepted sample length is consistent with the standard reference signal, the sliding step length of the sliding window is 1, and the maximum offset distance is 40.

7. The plunger pump anomaly detection method based on similarity analysis of simulation and test data according to claim 6 is characterized in that: The offset phase when the pressure signal sample is aligned with the standard reference signal is calculated by the following formula: x align =argmax S(G(s),G(w(x))) Among them, x align is the aligned pressure signal sample, s is the standard reference signal, x is the pressure signal sample, w(·) is the window function, G is the mapping function from the input signal to the weighted graph similarity matrix, and S represents the calculated similarity.

8. A plunger pump abnormality detection device based on similarity analysis of simulation and test data, characterized in that: include: A signal acquisition module is used to simulate and generate an ideal waveform for monitoring the outlet pressure signal of the plunger pump to obtain a standard reference signal; And collect the outlet pressure signal of the plunger pump in a healthy state to obtain the measured healthy signal; A calculation module, used to calculate the modal distance of the weighted graph similarity matrix between the measured health signal and the standard reference signal to obtain a similarity index; and determine an alarm threshold based on the similarity index; A monitoring module, used for monitoring the pressure signal at the outlet of the plunger pump to be tested, obtaining a signal to be tested; and calculating the similarity between the signal to be tested and the standard reference signal; The determination module is used to determine that the plunger pump to be tested is abnormal when the similarity is lower than the alarm threshold in the measurement of the consecutive target number of times.

9. 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 plunger pump abnormality detection method based on similarity analysis of simulation and test data as described in any one of claims 1 to 7.

10. 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 plunger pump abnormality detection method based on similarity analysis of simulation and test data as described in any one of claims 1 to 7 are implemented.

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