Air compressor health degree diagnosis method and device based on bathtub curve, terminal, medium and product

Abstract

The invention provides an air compressor health degree diagnosis method and device based on a bathtub curve, a terminal, a medium and a product. The method comprises the following steps: acquiring current operation data of a target air compressor; according to the current operation data of the target air compressor, the current failure rate of the target air compressor is obtained on the basis of a personalized failure rate model of the target air compressor constructed according to the obtained bathtub curve of the standard air compressor; and according to the current failure rate of the target air compressor and the bathtub curve of the standard air compressor, performing health degree diagnosis on the target air compressor. According to the method, real-time health degree diagnosis is carried out on the target air compressor through the personalized fault rate model constructed according to the bathtub curve, and the abnormal condition of the health degree of the air compressor can be responded in time, so that the service life of the air compressor is prolonged.

Description

Technical Field

[0001] The present application relates to the technical field of air compressors, and in particular to a method, device, terminal, medium and product for diagnosing the health of an air compressor based on a bathtub curve. Background Art

[0002] As a vital core equipment in the air compression system, the operating status of the air compressor is directly related to the energy consumption and production capacity of the entire plant. Therefore, it is particularly necessary to monitor the operating status of the air compressor in real time and conduct scientific analysis on it. The existing technology generally uses deep learning methods to predict air compressor failures. However, the existing technology lacks mathematical model research on the failure mechanism of air compressor equipment, which limits the accuracy and scientificity of failure prediction. Summary of the invention

[0003] In view of the shortcomings of the prior art mentioned above, the purpose of the present application is to provide an air compressor health diagnosis method, device, terminal, medium and product based on the bathtub curve, which is used to solve the problem of low accuracy of air compressor fault prediction in the prior art.

[0004] To achieve the above-mentioned purpose and other related purposes, the first aspect of the present application provides an air compressor health diagnosis method based on a bathtub curve, including: obtaining the current operating data of the target air compressor; according to the current operating data of the target air compressor, obtaining the current failure rate of the target air compressor based on the individual failure rate model of the target air compressor constructed according to the acquired standard air compressor bathtub curve; performing a health diagnosis on the target air compressor according to the current failure rate of the target air compressor and the standard air compressor bathtub curve.

[0005] In some embodiments of the first aspect of the present application, a personalized failure rate model of the target air compressor is constructed based on the acquired standard air compressor bathtub curve, including: acquiring operating data of the air compressor throughout its life cycle; based on the operating data of the air compressor throughout its life cycle, constructing an air compressor operating data set according to the acquired standard air compressor bathtub curve; inputting the preprocessed air compressor operating data set into a neural network model for training to obtain a benchmark failure rate model; based on the acquired historical operating data of the target air compressor, constructing a historical operating data set of the target air compressor; inputting the historical operating data set of the target air compressor into the benchmark failure rate model for training to obtain the personalized failure rate model of the target air compressor.

[0006] In some embodiments of the first aspect of the present application, the types of operating data include: equipment failure data, cumulative operating time, ratio of inlet flow to rated flow, leakage, outlet pressure, inlet temperature and equipment pressure drop.

[0007] In some embodiments of the first aspect of the present application, the method for obtaining a standard air compressor bathtub curve includes: calculating the failure rate of the air compressor throughout its life cycle based on the equipment failure data of the air compressor throughout its life cycle; fitting a benchmark Weibull distribution probability function based on the equipment cumulative operating time of the air compressor throughout its life cycle and the calculated failure rate to obtain a failure rate function, and then obtaining a standard air compressor bathtub curve.

[0008] In some embodiments of the first aspect of the present application, based on the cumulative operating time of the equipment of the air compressor throughout its life cycle and the calculated failure rate, a benchmark Weibull distribution probability function is fitted to obtain a failure rate function, including: obtaining an intermediate failure rate function based on the benchmark Weibull distribution probability function and the reliability function; fitting the intermediate failure rate function using the least squares method based on the cumulative operating time of the equipment of the air compressor throughout its life cycle and the calculated failure rate to obtain a specific value of the correlation coefficient in the benchmark Weibull distribution probability function; substituting the specific value of the obtained correlation coefficient into the benchmark Weibull distribution probability function to obtain a failure rate function.

[0009] In some embodiments of the first aspect of the present application, a health diagnosis is performed on the target air compressor based on the current failure rate of the target air compressor and the standard air compressor bathtub curve, including: determining a failure rate warning threshold based on the standard air compressor bathtub curve; when the current failure rate of the target air compressor is higher than the failure rate warning threshold, the target air compressor is diagnosed as having a high failure rate; when the current failure rate of the target air compressor is not higher than the failure rate warning threshold, the target air compressor is diagnosed as having a low failure rate.

[0010] To achieve the above-mentioned purpose and other related purposes, the second aspect of the present application provides an air compressor health diagnosis device based on a bathtub curve, including: an operation data acquisition module for acquiring the current operation data of the target air compressor; a failure rate acquisition module for obtaining the current failure rate of the target air compressor based on the current operation data of the target air compressor and based on the individual failure rate model of the target air compressor constructed according to the acquired standard air compressor bathtub curve; a health diagnosis module for performing a health diagnosis on the target air compressor based on the current failure rate of the target air compressor and the standard air compressor bathtub curve.

[0011] To achieve the above-mentioned purpose and other related purposes, the third aspect of the present application provides a computer-readable storage medium having a computer program stored thereon, and when the computer program is executed by a processor, the air compressor health diagnosis method based on the bathtub curve is implemented.

[0012] To achieve the above-mentioned purpose and other related purposes, the fourth aspect of the present application provides a computer program product, which includes a computer program code. When the computer program code runs on a computer, the computer implements the air compressor health diagnosis method based on the bathtub curve.

[0013] To achieve the above-mentioned purpose and other related purposes, the fifth aspect of the present application provides an electronic terminal, including a memory, a processor and a computer program stored in the memory; the processor executes the computer program to implement the air compressor health diagnosis method based on the bathtub curve.

[0014] As described above, the air compressor health diagnosis method, device, terminal, medium and product based on the bathtub curve of the present application have the following beneficial effects:

[0015] This application performs real-time health diagnosis on the target air compressor through a personalized failure rate model constructed according to the bathtub curve, and can respond promptly to abnormal conditions of the air compressor health, thereby increasing the service life of the air compressor. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] Figure 1 Shown is a flow chart of an air compressor health diagnosis method based on a bathtub curve in one embodiment of the present application.

[0017] Figure 2 Shown is a schematic diagram of the process of training a personality failure rate model in one embodiment of the present application.

[0018] Figure 3 Shown is a schematic diagram of a standard air compressor bathtub curve in one embodiment of the present application.

[0019] Figure 4 Shown is a schematic block diagram of an air compressor health diagnosis device based on a bathtub curve in one embodiment of the present application.

[0020] Figure 5 Shown is a schematic diagram of the structure of an electronic terminal in one embodiment of the present application. DETAILED DESCRIPTION

[0021] The following describes the embodiments of the present application through specific examples, and those skilled in the art can easily understand other advantages and effects of the present application from the contents disclosed in this specification. The present application can also be implemented or applied through other different specific embodiments, and the details in this specification can also be modified or changed in various ways based on different viewpoints and applications without departing from the spirit of the present application. It should be noted that the following embodiments and features in the embodiments can be combined with each other without conflict.

[0022] In the embodiments of the present application, words such as "first" and "second" are used to distinguish the same or similar items with substantially the same functions and effects. Those skilled in the art can understand that words such as "first" and "second" do not limit the quantity and execution order, and words such as "first" and "second" do not necessarily limit the difference.

[0023] It should be noted that in the embodiments of the present application, words such as "exemplary" or "for example" represent examples, illustrations or descriptions. Any embodiment or design described as "exemplary" or "for example" in the present application should not be interpreted as being more preferred or more advantageous than other embodiments or designs. Specifically, the use of words such as "exemplary" or "for example" is intended to present related concepts in a specific way.

[0024] In the embodiments of the present application, "at least one" refers to one or more, and "plurality" refers to two or more. "And / or" describes the association relationship of associated objects, indicating that three relationships may exist. For example, A and / or B can represent: A exists alone, A and B exist at the same time, and B exists alone, where A and B can be singular or plural. The character " / " generally indicates that the previous and next associated objects are in an "or" relationship. "At least one of the following" or similar expressions refers to any combination of these items, including any combination of single or plural items. For example, at least one of a, b or c can represent: a, b, c, ab, ac, bc or abc, where a, b, c can be single or multiple.

[0025] To facilitate understanding of the embodiments of the present application, first Figure 1 Detailed description. Figure 1 The flowchart of a method for diagnosing the health of an air compressor based on a bathtub curve in an embodiment of the present invention is shown. The method for diagnosing the health of an air compressor based on a bathtub curve in this embodiment mainly includes the following steps:

[0026] Step S11: Acquire the current operating data of the target air compressor.

[0027] In one embodiment, the current operating data of the target air compressor includes: current equipment fault data, cumulative operating time, ratio of inlet flow to rated flow, leakage, outlet pressure, inlet temperature and equipment pressure drop of the target air compressor.

[0028] It should be understood that the cumulative operating time refers to the sum of all operating times of the air compressor from the beginning of use to a certain point in time. The inlet flow rate refers to the volume of air sucked into the air compressor inlet in the actual intake state. The rated flow rate refers to the volume of gas compressed and discharged by the air compressor per unit time under standard conditions. The leakage volume refers to the amount of compressed air leakage caused by poor equipment sealing or other reasons within a certain period of time. The outlet pressure refers to the pressure value of the gas at the outlet after compression by the air compressor. The inlet temperature refers to the gas temperature before entering the air compressor. The equipment pressure drop refers to the pressure drop caused by factors such as pipeline resistance, valve resistance and internal resistance of the equipment when the gas flows in the air compressor system.

[0029] It should also be understood that operating data can be collected by installing various types of sensors in the air compressor.

[0030] Step S12: According to the current operating data of the target air compressor, based on the individual failure rate model of the target air compressor constructed according to the acquired standard air compressor bathtub curve, the current failure rate of the target air compressor is obtained.

[0031] In one embodiment, if Figure 2 As shown, based on the acquired standard air compressor bathtub curve, an individual failure rate model of the target air compressor is constructed, including: acquiring the operating data of the air compressor throughout its life cycle; based on the operating data of the air compressor throughout its life cycle, constructing an air compressor operating data set according to the acquired standard air compressor bathtub curve; inputting the preprocessed air compressor operating data set into a neural network model for training to obtain a benchmark failure rate model; based on the acquired historical operating data of the target air compressor, constructing a historical operating data set of the target air compressor; inputting the historical operating data set of the target air compressor into the benchmark failure rate model for training to obtain the individual failure rate model of the target air compressor.

[0032] It should be understood that the full life cycle management of air compressor equipment covers the entire process from equipment procurement, installation and commissioning, use, maintenance to final scrapping.

[0033] In one embodiment, the method for obtaining a standard air compressor bathtub curve includes: calculating the failure rate of the air compressor throughout its life cycle based on the equipment failure data of the air compressor throughout its life cycle; fitting a benchmark Weibull distribution probability function based on the equipment cumulative operating time of the air compressor throughout its life cycle and the calculated failure rate to obtain a failure rate function, and then obtaining a standard air compressor bathtub curve.

[0034] In one embodiment, based on the cumulative operating time of the air compressor equipment over its entire life cycle and the calculated failure rate, a benchmark Weibull distribution probability function is fitted to obtain a failure rate function, including: obtaining an intermediate failure rate function based on the benchmark Weibull distribution probability function and the reliability function; fitting the intermediate failure rate function using the least squares method based on the cumulative operating time of the air compressor equipment over its entire life cycle and the calculated failure rate to obtain a specific value of a correlation coefficient in the benchmark Weibull distribution probability function; substituting the obtained specific value of the correlation coefficient into the benchmark Weibull distribution probability function to obtain a failure rate function.

[0035] The following is an explanation of how to obtain a standard air compressor bathtub curve:

[0036] Obtain the operating data of the air compressor throughout its life cycle. The operating data of the air compressor throughout its life cycle includes: equipment failure data, cumulative operating time, ratio of inlet flow to rated flow, leakage, outlet pressure, inlet temperature, and equipment pressure drop.

[0037] It should be understood that the cumulative operating time refers to the sum of all operating times of the air compressor from the beginning of use to a certain point in time. The inlet flow rate refers to the volume of air sucked into the air compressor inlet in the actual intake state. The rated flow rate refers to the volume of gas compressed and discharged by the air compressor per unit time under standard conditions. The leakage volume refers to the amount of compressed air leakage caused by poor equipment sealing or other reasons within a certain period of time. The outlet pressure refers to the pressure value of the gas at the outlet after compression by the air compressor. The inlet temperature refers to the gas temperature before entering the air compressor. The equipment pressure drop refers to the pressure drop caused by factors such as pipeline resistance, valve resistance and internal resistance of the equipment when the gas flows in the air compressor system.

[0038] Furthermore, the equipment failure data of the air compressor throughout its life cycle records the number of failures of the air compressor throughout its life cycle and the time when each failure occurred. According to the set time length, according to the cumulative operating time of the air compressor throughout its life cycle, the cumulative operating time is divided into multiple calculation time periods, and the corresponding multiple calculation time points are obtained. For example, the operating time of the air compressor throughout its life cycle is 1000 hours, and the set time length is 100 hours. The ten determined calculation time periods are [1-100], [101-200]…[900-1000], and the corresponding multiple calculation time points are 100, 200…1000. It should be noted that those skilled in the art can set the time length to other values ​​according to actual needs, and the present invention is not limited to this.

[0039] Further, the failure rate of each calculation time period is calculated by referring to the following formula 1: :

[0040] (Formula 1);

[0041] in, The unit is the number of failures / unit time; n represents the number of failures that occurred within the calculation time period T; T represents the calculation time period.

[0042] Furthermore, the calculated failure rate of each calculation time period is the failure rate at the corresponding calculation time point.

[0043] Furthermore, the change of the failure rate to the cumulative running time of the air compressor is defined to satisfy the benchmark Weibull distribution probability function. The benchmark Weibull distribution probability function is shown in the following formula 2:

[0044] (Formula 2);

[0045] in, represents the failure rate, t represents the time variable, Represents the correlation coefficient.

[0046] Furthermore, a reliability function is defined, and the reliability function R(t) is shown in the following formula 3:

[0047] (Formula 3);

[0048] Among them, t represents the time variable, Represents the correlation coefficient.

[0049] Furthermore, in order to obtain the relationship between the failure rate and time, Formula 2 is divided by Formula 3 to obtain the intermediate failure rate function h(t) shown in the following Formula 4:

[0050] (Formula 4);

[0051] Among them, t represents the time variable, Represents the correlation coefficient.

[0052] Furthermore, taking the logarithm of the intermediate failure rate function h(t), we obtain the following equation 1:

[0053] (Equation 1);

[0054] Comparing this equation with the linear form ( ) are matched one by one, and the following equations 2 to 5 are obtained:

[0055] (Equation 2);

[0056] (Equation 3);

[0057] (Equation 4);

[0058] (Equation 5);

[0059] Furthermore, the least square method is used to fit the multiple calculation time points and the failure rates of the multiple calculation time points according to equations 1 to 5 to obtain specific values ​​of the slope b and the intercept a, and then the correlation coefficient is calculated according to equations 4 and 5. and the specific value of k.

[0060] Furthermore, the calculated correlation coefficient Substitute the specific value of k into Formula 2 to obtain the failure rate function; based on the failure rate function, draw the standard air compressor bathtub curve. The standard air compressor bathtub curve is as follows: Figure 3 As shown in Figure 2, the curve reflects the law of how the failure rate of the air compressor changes over time.

[0061] The following will explain how to obtain the personalized failure rate model of the target air compressor:

[0062] The cumulative operating time of the air compressor over its entire life cycle includes: the cumulative operating time at multiple moments in the entire life cycle; the ratio of the inlet flow rate to the rated flow rate of the air compressor over its entire life cycle includes: the ratio of the inlet flow rate to the rated flow rate at multiple moments in the entire life cycle; the leakage of the air compressor over its entire life cycle includes: the leakage at multiple moments in the entire life cycle; the outlet pressure of the air compressor over its entire life cycle includes: the outlet pressure at multiple moments in the entire life cycle; the inlet temperature of the air compressor over its entire life cycle includes: the inlet temperature at multiple moments in the entire life cycle; the equipment pressure drop of the air compressor over its entire life cycle includes: the equipment pressure drop at multiple moments in the entire life cycle. For ease of description, the following will refer to the cumulative operating time, the ratio of the inlet flow rate to the rated flow rate, the leakage, the outlet pressure, the inlet temperature and the equipment pressure drop as other operating data.

[0063] According to the standard air compressor bathtub curve, the failure rate at multiple moments in the whole life cycle is determined. Other operating data at multiple moments in the whole life cycle are matched with the failure rate at multiple moments in the whole life cycle in a one-to-one manner according to time to form an air compressor operation data set.

[0064] Further, the air compressor operation data set is preprocessed to obtain a preprocessed air compressor operation data set, wherein the preprocessing is a normalization process.

[0065] Furthermore, the preprocessed air compressor operation data set is divided into a training set and a test set according to a ratio of 4:1. The training set is input into the neural network model for training, and the model is tested using the test set to obtain a benchmark failure rate model.

[0066] The neural network model uses the BP neural network model, with a designed input size of (6,1), an output size of (1,1), a batch size of 64, and an epoch iteration number of 3000. The number of neurons in the fully connected structure is 64, 32, 16, and 1, respectively. The optimizer uses Adam, the learning rate is set to 0.0001, and the activation function uses sigmoid. The mean square error (MSE) error loss function shown in the following formula 5 is used to calculate the loss value:

[0067] (Formula 5);

[0068] Among them, Loss1 is the loss value, is the actual failure rate (determined according to the standard air compressor bathtub curve), is the failure rate predicted by the model, and n represents the number of multiple moments in the entire life cycle.

[0069] The mean absolute percentage error (MAPE) was set as the evaluation criterion, and the model was retained if MAPE was less than 4%.

[0070] Further, the historical operation data of the target air compressor is obtained. The historical operation data of the target air compressor includes: the historical equipment failure data, cumulative operation time, the ratio of inlet flow to rated flow, leakage, outlet pressure, inlet temperature and equipment pressure drop of the target air compressor. The historical cumulative operation time of the target air compressor includes: the cumulative operation time at multiple moments in history; the historical ratio of inlet flow to rated flow of the target air compressor includes: the ratio of inlet flow to rated flow at multiple moments in history; the historical leakage of the target air compressor includes: the leakage at multiple moments in history; the historical outlet pressure of the target air compressor includes: the outlet pressure at multiple moments in history; the historical inlet temperature of the target air compressor includes: the inlet temperature at multiple moments in history; the historical equipment pressure drop of the target air compressor includes: the equipment pressure drop at multiple moments in history. For the convenience of description, the cumulative operation time, the ratio of inlet flow to rated flow, leakage, outlet pressure, inlet temperature and equipment pressure drop are referred to as other operation data below.

[0071] It should be understood that the cumulative operating time refers to the sum of all operating times of the air compressor from the beginning of use to a certain point in time. The inlet flow rate refers to the volume of air sucked into the air compressor inlet in the actual intake state. The rated flow rate refers to the volume of gas compressed and discharged by the air compressor per unit time under standard conditions. The leakage volume refers to the amount of compressed air leakage caused by poor equipment sealing or other reasons within a certain period of time. The outlet pressure refers to the pressure value of the gas at the outlet after compression by the air compressor. The inlet temperature refers to the gas temperature before entering the air compressor. The equipment pressure drop refers to the pressure drop caused by factors such as pipeline resistance, valve resistance and internal resistance of the equipment when the gas flows in the air compressor system.

[0072] Furthermore, according to the historical equipment failure data of the target air compressor, the failure rate of the target air compressor at multiple moments in history is calculated. For example, the time length of the historical operation data of the target air compressor is 1000 hours, and the failure rates in the 100 time periods [1-10], [11-20], [20-30] ... [991-1000] are calculated, which corresponds to the failure rates of the 100 moments 10, 20, 30 ... 1000. It should be understood that the method for calculating the failure rate can refer to the method in the above embodiment, which will not be repeated here.

[0073] Furthermore, the other operating data of the target air compressor at multiple historical moments are matched with the failure rates of the target air compressor at multiple historical moments in time, and the historical operating data set of the target air compressor is obtained. The historical operating data set of the target air compressor is input into the benchmark failure rate model for training to obtain the individual failure rate model of the target air compressor. During the training process, the loss function shown in the following formula 6 is used as the error loss function of this training:

[0074] (Formula 6);

[0075] Among them, Loss2 is the loss value of this training, N represents the number of multiple moments in history, and W is the weight matrix that needs to be regularized. is the regularization coefficient, is the actual failure rate of this training, The failure rate predicted by the training model.

[0076] It should be noted that the present invention introduces a mathematical model of the air compressor equipment failure mechanism (benchmark air compressor bathtub curve) during the training process of the failure rate model, thereby improving the accuracy of air compressor failure prediction.

[0077] Step S13: Perform health diagnosis on the target air compressor according to the current failure rate of the target air compressor and the standard air compressor bathtub curve.

[0078] In one embodiment, a health diagnosis is performed on the target air compressor based on the current failure rate of the target air compressor and the standard air compressor bathtub curve, including: determining a failure rate warning threshold based on the standard air compressor bathtub curve; when the current failure rate of the target air compressor is higher than the failure rate warning threshold, the target air compressor is diagnosed as having a high failure rate; when the current failure rate of the air compressor is not higher than the determined failure rate warning threshold, the target air compressor is diagnosed as having a low failure rate.

[0079] It should be noted that those skilled in the art can set the fault rate warning threshold according to the standard air compressor bathtub curve and actual needs, and the present invention is not limited to this.

[0080] Figure 4 is a schematic block diagram of an air compressor health diagnosis device based on a bathtub curve provided in an embodiment of the present application. Figure 4 As shown, the air compressor health diagnosis device 4 based on the bathtub curve includes:

[0081] An operation data acquisition module 41 is used to acquire the current operation data of the target air compressor;

[0082] A failure rate acquisition module 42, connected to the operation data acquisition module 41, is used to obtain the current failure rate of the target air compressor based on the current operation data of the target air compressor and the individual failure rate model of the target air compressor constructed according to the acquired standard air compressor bathtub curve;

[0083] The health diagnosis module 43 is connected to the failure rate acquisition module 42, and is used to perform health diagnosis on the target air compressor according to the current failure rate of the target air compressor and the standard air compressor bathtub curve.

[0084] It should be understood that the specific process of each module executing the above corresponding steps has been described in detail in the above method embodiment, and for the sake of brevity, it will not be repeated here.

[0085] It should also be understood that the division of modules in the embodiments of the present application is schematic and is only a logical function division. There may be other division methods in actual implementation. In addition, each functional module in each embodiment of the present application may be integrated into a processor, or may exist physically separately, or two or more modules may be integrated into one module. The above-mentioned integrated modules may be implemented in the form of hardware or in the form of software functional modules.

[0086] In one embodiment, based on the acquired standard air compressor bathtub curve, a personalized failure rate model of the target air compressor is constructed, including: acquiring operating data of the air compressor throughout its life cycle; based on the operating data of the air compressor throughout its life cycle, constructing an air compressor operating data set according to the acquired standard air compressor bathtub curve; inputting the preprocessed air compressor operating data set into a neural network model for training to obtain a benchmark failure rate model; based on the acquired historical operating data of the target air compressor, constructing a historical operating data set of the target air compressor; inputting the historical operating data set of the target air compressor into the benchmark failure rate model for training to obtain the personalized failure rate model of the target air compressor.

[0087] In one embodiment, the operation data of the air compressor throughout its life cycle includes: equipment failure data of the air compressor throughout its life cycle, cumulative operating time, ratio of inlet flow to rated flow, leakage, outlet pressure, inlet temperature and equipment pressure drop.

[0088] In one embodiment, the method for obtaining a standard air compressor bathtub curve includes: calculating the failure rate of the air compressor throughout its life cycle based on the equipment failure data of the air compressor throughout its life cycle; fitting a benchmark Weibull distribution probability function based on the equipment cumulative operating time of the air compressor throughout its life cycle and the calculated failure rate to obtain a failure rate function, and then obtaining a standard air compressor bathtub curve.

[0089] In one embodiment, based on the cumulative operating time of the air compressor equipment over its entire life cycle and the calculated failure rate, a benchmark Weibull distribution probability function is fitted to obtain a failure rate function, including: obtaining an intermediate failure rate function based on the benchmark Weibull distribution probability function and the reliability function; fitting the intermediate failure rate function using the least squares method based on the cumulative operating time of the air compressor equipment over its entire life cycle and the calculated failure rate to obtain a specific value of a correlation coefficient in the benchmark Weibull distribution probability function; substituting the obtained specific value of the correlation coefficient into the benchmark Weibull distribution probability function to obtain a failure rate function.

[0090] In one embodiment, a health diagnosis is performed on the target air compressor based on the current failure rate of the target air compressor and the standard air compressor bathtub curve, including: determining a failure rate warning threshold based on the standard air compressor bathtub curve; when the current failure rate of the target air compressor is higher than the failure rate warning threshold, the target air compressor is diagnosed as having a high failure rate; when the current failure rate of the air compressor is not higher than the determined failure rate warning threshold, the target air compressor is diagnosed as having a low failure rate.

[0091] Figure 5is a schematic block diagram of an electronic terminal provided in an embodiment of the present application. Figure 5 As shown, the electronic terminal includes: at least one processor 501, a memory 502, at least one network interface 503 and a user interface 505. The various components in the device are coupled together through a bus system 504. It can be understood that the bus system 504 is used to realize the connection and communication between these components. In addition to the data bus, the bus system 504 also includes a power bus, a control bus and a status signal bus. However, for the sake of clarity, Figure 5 In the specification, various buses are labeled as bus systems.

[0092] The user interface 505 may include a display, a keyboard, a mouse, a trackball, a click gun, keys, buttons, a touch pad or a touch screen.

[0093] It is understood that the memory 502 can be a volatile memory or a non-volatile memory, and can also include both volatile and non-volatile memories. Among them, the non-volatile memory can be a read-only memory (ROM), a programmable read-only memory (PROM), which is used as an external cache. By way of example but not limitation, many forms of RAM are available, such as static random access memory (SRAM), synchronous static random access memory (SSRAM). The memory described in the embodiments of the present invention is intended to include but is not limited to these and any other suitable categories of memory.

[0094] The memory 502 in the embodiment of the present invention is used to store various categories of data to support the operation of the electronic terminal 500. Examples of these data include: any executable program for operating on the electronic terminal 500, such as an operating system 5021 and an application 5022; the operating system 5021 includes various system programs, such as a framework layer, a core library layer, a driver layer, etc., for implementing various basic services and processing hardware-based tasks. The application 5022 may include various applications, such as a media player (Media Player), a browser (Browser), etc., for implementing various application services. The air compressor health diagnosis method based on the bathtub curve provided in the embodiment of the present invention may be included in the application 5022.

[0095] The method disclosed in the above embodiment of the present invention can be applied to the processor 501, or implemented by the processor 501. The processor 501 may be an integrated circuit chip with signal processing capabilities. In the implementation process, each step of the above method can be completed by the hardware integrated logic circuit in the processor 501 or the instruction in the form of software. The above processor 501 may be a general processor, a digital signal processor (DSP, Digital Signal Processor), or other programmable logic devices, discrete gates or transistor logic devices, discrete hardware components, etc. The processor 501 can implement or execute the various methods, steps and logic block diagrams disclosed in the embodiment of the present invention. The general processor 501 may be a microprocessor or any conventional processor, etc. In combination with the steps of the accessory optimization method provided in the embodiment of the present invention, it can be directly embodied as a hardware decoding processor to execute, or it can be executed by a combination of hardware and software modules in the decoding processor. The software module can be located in a storage medium, which is located in a memory, and the processor reads the information in the memory and completes the steps of the above method in combination with its hardware.

[0096] In an exemplary embodiment, the electronic terminal 500 may be implemented by one or more application specific integrated circuits (ASIC), DSP, programmable logic device (PLD), complex programmable logic device (CPLD) to execute the aforementioned method.

[0097] According to the method provided in the embodiment of the present application, the present application also provides a computer program product, which includes: a computer program code, when the computer program code is run on a computer, the computer executes Figure 1 The air compressor health diagnosis method based on the bathtub curve in the illustrated embodiment.

[0098] According to the method provided in the embodiment of the present application, the present application also provides a computer-readable storage medium, which stores a program code, and when the program code is run on a computer, the computer executes Figure 1 The air compressor health diagnosis method based on the bathtub curve in the illustrated embodiment.

[0099] The terms "component", "module", "system", etc. used in this specification are used to represent computer-related entities, hardware, firmware, a combination of hardware and software, software, or software in execution. For example, a component can be, but is not limited to, a process, a processor, an object, an executable file, an execution thread, a program and / or a computer running on a processor. By way of illustration, both applications and computing devices running on a computing device can be components. One or more components may reside in a process and / or an execution thread, and a component may be located on a computer and / or distributed between two or more computers. In addition, these components may be executed from various computer-readable media having various data structures stored thereon. Components may, for example, communicate through local and / or remote processes according to signals having one or more data packets (e.g., data from two components interacting with another component between a local system, a distributed system and / or a network, such as the Internet interacting with other systems through signals).

[0100] Those of ordinary skill in the art will appreciate that the various illustrative logical blocks and steps described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel may use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of this application.

[0101] Those skilled in the art can clearly understand that, for the convenience and brevity of description, the specific working processes of the systems, devices and units described above can refer to the corresponding processes in the aforementioned method embodiments and will not be repeated here.

[0102] In the several embodiments provided in the present application, it should be understood that the disclosed systems, devices and methods can be implemented in other ways. For example, the device embodiments described above are only schematic. For example, the division of units is only a logical function division. There may be other division methods in actual implementation. For example, multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, indirect coupling or communication connection of devices or units, which can be electrical, mechanical or other forms.

[0103] The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0104] In addition, each functional unit in each embodiment of the present application may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit.

[0105] In the above embodiments, the functions of each functional unit can be implemented in whole or in part by software, hardware, firmware or any combination thereof. When implemented using software, it can be implemented in whole or in part in the form of a computer program product. The computer program product includes one or more computer instructions (programs). When the computer program instructions (programs) are loaded and executed on a computer, the process or function according to the embodiment of the present application is generated in whole or in part. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. The computer instructions can be stored in a computer-readable storage medium, or transmitted from one computer-readable storage medium to another computer-readable storage medium. For example, the computer instructions can be transmitted from a website site, computer, server or data center to another website site, computer, server or data center by wired (e.g., coaxial cable, optical fiber, digital subscriber line (DSL)) or wireless (e.g., infrared, wireless, microwave, etc.) mode. The computer-readable storage medium can be any available medium that can be accessed by a computer or a data storage device such as a server or data center that includes one or more available media integrated. Available media may be magnetic media (e.g., floppy disks, hard disks, tapes), optical media (e.g., high-density digital video discs (DVDs), or semiconductor media (e.g., solid state disks (SSDs)).

[0106] If the function is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present application, or the part that contributes to the prior art or the part of the technical solution, can be embodied in the form of a software product. The computer software product is stored in a storage medium, including a number of instructions to enable a computer device (which can be a personal computer, server, or network device, etc.) to execute all or part of the steps of the various embodiments of the present application. The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (ROM), random access memory (RAM), disk or optical disk, and other media that can store program codes.

[0107] The above is only a specific implementation of the present application, but the protection scope of the present application is not limited thereto. Any person skilled in the art who is familiar with the present technical field can easily think of changes or substitutions within the technical scope disclosed in the present application, which should be included in the protection scope of the present application. Therefore, the protection scope of the present application should be based on the protection scope of the claims.

[0108] In summary, the present application provides an air compressor health diagnosis method, device, terminal, medium and product based on the bathtub curve. The method includes: obtaining the current operating data of the target air compressor; according to the current operating data of the target air compressor, obtaining the current failure rate of the target air compressor based on the individual failure rate model of the target air compressor constructed according to the acquired standard air compressor bathtub curve; and performing health diagnosis on the target air compressor according to the current failure rate of the target air compressor and the standard air compressor bathtub curve. The present application performs real-time health diagnosis on the target air compressor through the individual failure rate model constructed according to the bathtub curve, and can respond to abnormal conditions of the health of the air compressor in a timely manner, thereby increasing the service life of the air compressor. Therefore, the present application effectively overcomes the various shortcomings in the prior art and has a high industrial utilization value.

[0109] The above embodiments are merely illustrative of the principles and effects of the present application and are not intended to limit the present application. Anyone familiar with the technology may modify or change the above embodiments without violating the spirit and scope of the present application. Therefore, all equivalent modifications or changes made by a person of ordinary skill in the art without departing from the spirit and technical ideas disclosed in the present application shall still be covered by the claims of the present application.

Claims

1. A method for diagnosing the health of an air compressor based on a bathtub curve, characterized in that: include: Obtain the current operating data of the target air compressor; According to the current operating data of the target air compressor, based on the individual failure rate model of the target air compressor constructed according to the acquired standard air compressor bathtub curve, the current failure rate of the target air compressor is obtained; wherein, according to the acquired standard air compressor bathtub curve, the individual failure rate model of the target air compressor is constructed, including: obtaining the operating data of the air compressor throughout its life cycle; constructing an air compressor operating data set based on the operating data of the air compressor throughout its life cycle and according to the acquired standard air compressor bathtub curve; inputting the preprocessed air compressor operating data set into the neural network model for training to obtain a benchmark failure rate model; constructing a historical operating data set of the target air compressor based on the acquired historical operating data of the target air compressor; inputting the historical operating data set of the target air compressor into the benchmark failure rate model for training to obtain the individual failure rate model of the target air compressor; the types of operating data include: equipment failure data, cumulative operating time, ratio of inlet flow to rated flow, leakage, outlet pressure, inlet temperature and equipment pressure drop; The method of obtaining a standard air compressor bathtub curve includes: calculating the failure rate of the air compressor over its entire life cycle based on the equipment failure data of the air compressor over its entire life cycle; fitting a benchmark Weibull distribution probability function based on the equipment cumulative operating time of the air compressor over its entire life cycle and the calculated failure rate to obtain a failure rate function, thereby obtaining a standard air compressor bathtub curve; The health status of the target air compressor is diagnosed based on the current failure rate of the target air compressor and the standard air compressor bathtub curve.

2. The air compressor health diagnosis method based on the bathtub curve according to claim 1 is characterized in that: Based on the cumulative operating time of the air compressor in the entire life cycle and the calculated failure rate, the benchmark Weibull distribution probability function is fitted to obtain the failure rate function, including: Based on the benchmark Weibull distribution probability function and the reliability function, the intermediate failure rate function is obtained; Based on the cumulative operating time of the air compressor equipment during the entire life cycle and the calculated failure rate, the intermediate failure rate function is fitted using the least squares method to obtain a specific value of the correlation coefficient in the benchmark Weibull distribution probability function; The specific value of the obtained correlation coefficient is substituted into the reference Weibull distribution probability function to obtain the failure rate function.

3. The air compressor health diagnosis method based on the bathtub curve according to claim 1 is characterized in that: According to the current failure rate of the target air compressor and the standard air compressor bathtub curve, the health diagnosis of the target air compressor is performed, including: Determining a failure rate warning threshold value according to the standard air compressor bathtub curve; When the current failure rate of the target air compressor is higher than the failure rate warning threshold, the target air compressor is diagnosed as having a high failure rate; When the current failure rate of the target air compressor is not higher than the failure rate warning threshold, the target air compressor is diagnosed as having a low failure rate.

4. An air compressor health diagnosis device based on a bathtub curve, characterized in that: include: An operation data acquisition module is used to acquire the current operation data of the target air compressor; A failure rate acquisition module is used to obtain the current failure rate of the target air compressor based on the current operation data of the target air compressor and the individual failure rate model of the target air compressor constructed according to the acquired standard air compressor bathtub curve; wherein, constructing the individual failure rate model of the target air compressor according to the acquired standard air compressor bathtub curve includes: obtaining the operation data of the air compressor over the entire life cycle; constructing an air compressor operation data set based on the operation data of the air compressor over the entire life cycle and the acquired standard air compressor bathtub curve; inputting the preprocessed air compressor operation data set into the neural network model for training to obtain a benchmark failure rate model; constructing a historical operation data set of the target air compressor based on the acquired historical operation data of the target air compressor; inputting the historical operation data set of the target air compressor into the benchmark failure rate model for training to obtain the individual failure rate model of the target air compressor; the types of operation data include: equipment failure data, cumulative operation time, ratio of inlet flow to rated flow, leakage, outlet pressure, inlet temperature and equipment pressure drop; The method of obtaining a standard air compressor bathtub curve includes: calculating the failure rate of the air compressor over its entire life cycle based on the equipment failure data of the air compressor over its entire life cycle; fitting a benchmark Weibull distribution probability function based on the equipment cumulative operating time of the air compressor over its entire life cycle and the calculated failure rate to obtain a failure rate function, thereby obtaining a standard air compressor bathtub curve; The health diagnosis module is used to perform health diagnosis on the target air compressor according to the current failure rate of the target air compressor and the standard air compressor bathtub curve.

5. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the method according to any one of claims 1 to 3 is implemented.

6. A computer program product, characterized in that The computer program product includes a computer program code, and when the computer program code is executed on a computer, the computer is enabled to implement the method according to any one of claims 1 to 3.

7. An electronic terminal comprising a memory, a processor and a computer program stored in the memory, characterized in that: The processor executes the computer program to implement the method according to any one of claims 1 to 3.

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