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

Abstract

The application provides a bath curve-based air compressor health degree diagnosis method, device, terminal, medium and product. The method comprises the following steps: obtaining current operation data of a target air compressor; obtaining a current failure rate of the target air compressor based on a personal failure rate model of the target air compressor constructed based on a standard air compressor bath curve according to the current operation data of the target air compressor; and performing health degree diagnosis on the target air compressor according to the current failure rate of the target air compressor and the standard air compressor bath curve. The personal failure rate model constructed based on the bath curve is used to perform real-time health degree diagnosis on the target air compressor, abnormal conditions of the health degree of the air compressor can be responded in a timely manner, and the service life of the air compressor is improved.

Description

TECHNICAL FIELD

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

[0002] As a core device in an air compression system, the operation state of an air compressor is directly related to the energy consumption and production capacity of the entire plant. Therefore, it is particularly necessary to monitor the operation state of the air compressor in real time and analyze it scientifically. The prior art generally uses a deep learning method to predict the fault of the air compressor. However, the prior art lacks mathematical model research on the fault mechanism of the air compressor device, which limits the accuracy and scientificity of fault prediction. SUMMARY

[0003] In view of the above-mentioned shortcomings of the prior art, the purpose of the present application is to provide an air compressor health degree diagnosis method and device based on a bathtub curve, a terminal, a medium and a product, which are 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 degree diagnosis method based on a bathtub curve, comprising: obtaining current operation data of a target air compressor; obtaining a current failure rate of the target air compressor based on a personal failure rate model of the target air compressor constructed based on a standard air compressor bathtub curve obtained according to the current operation data of the target air compressor; and performing health degree 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, the personal failure rate model of the target air compressor is constructed based on the standard air compressor bathtub curve obtained, comprising: obtaining operation data of a full life cycle air compressor; constructing an air compressor operation data set based on the operation data of the full life cycle air compressor according to the standard air compressor bathtub curve obtained; inputting the preprocessed air compressor operation data set into a 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 historical operation data of the target air compressor obtained; and inputting the historical operation data set of the target air compressor into the benchmark failure rate model for training to obtain the personal failure rate model of the target air compressor.

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

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

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

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

[0010] To achieve the above object and other related objects, the second aspect of the present application provides a bathtub curve-based air compressor health degree diagnosis device, comprising: an operation data acquisition module configured to acquire current operation data of a target air compressor; a failure rate acquisition module configured to obtain a current failure rate of the target air compressor based on a personal failure rate model of the target air compressor constructed according to an obtained standard air compressor bathtub curve, according to the current operation data of the target air compressor; and a health degree diagnosis module configured to diagnose the health degree of the target air compressor according to the current failure rate of the target air compressor and the standard air compressor bathtub curve.

[0011] To achieve the above object and other related objects, the third aspect of the present application provides a computer readable storage medium having a computer program stored thereon, wherein the computer program is executed by a processor to implement the bathtub curve-based air compressor health degree diagnosis method.

[0012] To achieve the above object and other related objects, the fourth aspect of the present application provides a computer program product, which comprises computer program codes, and when the computer program codes are run on a computer, the computer is caused to implement the air compressor health degree diagnosis method based on the bathtub curve.

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

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

[0015] The present application can diagnose the health degree of the target air compressor in real time through the individual failure rate model constructed according to the bathtub curve, and can respond to abnormal conditions of the health degree of the air compressor in time, thereby improving the service life of the air compressor. BRIEF DESCRIPTION OF DRAWINGS

[0016] Figure 1 Fig. 1 shows a flowchart of the air compressor health degree diagnosis method based on the bathtub curve in an embodiment of the present application.

[0017] Figure 2 Fig. 2 shows a flowchart of training the individual failure rate model in an embodiment of the present application.

[0018] Figure 3 Fig. 3 shows a schematic diagram of the standard air compressor bathtub curve in an embodiment of the present application.

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

[0020] Figure 5 Fig. 5 shows a structural schematic diagram of the electronic terminal in an embodiment of the present application. DETAILED DESCRIPTION

[0021] The embodiments of the present application will be described in detail below with specific reference to the drawings. Those skilled in the art can easily understand other advantages and effects of the present application from the content disclosed in the specification. The present application can also be implemented or applied in other different specific embodiments, and the details in the specification can be modified or changed based on different views 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 this application, terms such as "first" and "second" are used to distinguish between identical or similar items with substantially the same functions and effects. Those skilled in the art will understand that terms such as "first" and "second" do not limit the quantity or order of execution, and that terms such as "first" and "second" do not necessarily define differences.

[0023] It should be noted that in the embodiments of this application, words such as "exemplary" or "for example" represent examples, illustrations, or descriptions. Any embodiment or design described in this application as "exemplary" or "for example" should not be interpreted as being preferred or advantageous over other embodiments or designs. Rather, the use of words such as "exemplary" or "for example" is intended to present the relevant concepts in a concrete manner.

[0024] In the embodiments of the present application, "at least one" refers to one or more, and "more" 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: the existence of A alone, the existence of A and B at the same time, and the existence of B 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 items" or similar expressions refers to any combination of these items, including any combination of single items 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 this application, first Figure 1 Detailed description. Figure 1 The following is a flow chart showing a method for diagnosing the health of an air compressor based on a bathtub curve according to an embodiment of the present invention. The method for diagnosing the health of an air compressor based on a bathtub curve according to 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 running time refers to the total of all running times of the air compressor from the start of use to a certain time point. The inlet flow refers to the volume of air sucked by the air compressor inlet in the actual inlet state. The rated flow refers to the volume of gas compressed and discharged by the air compressor per unit time under standard conditions. The leakage amount refers to the leakage amount of compressed air due to poor sealing of the equipment or other reasons within a certain time. The outlet pressure refers to the pressure value of the gas compressed by the air compressor at the outlet. The inlet temperature refers to the temperature of the gas before entering the air compressor. The equipment pressure drop refers to the pressure reduction of the gas flowing in the air compressor system due to factors such as pipeline resistance, valve resistance, and internal resistance of the equipment.

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

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

[0031] In an embodiment, as shown in Figure 2 According to the obtained standard air compressor bathtub curve, the individual failure rate model of the target air compressor is constructed, which includes: obtaining the running data of the full life cycle air compressor; based on the running data of the full life cycle air compressor, the air compressor running data set is constructed according to the obtained standard air compressor bathtub curve; the preprocessed air compressor running data set is input into the neural network model for training to obtain the benchmark failure rate model; based on the obtained historical running data of the target air compressor, the historical running data set of the target air compressor is constructed; the historical running 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.

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

[0033] In an embodiment, the method for obtaining the standard air compressor bathtub curve includes: calculating the failure rate of the full life cycle air compressor based on the equipment failure data of the full life cycle air compressor; based on the cumulative running time of the full life cycle air compressor and the calculated failure rate, the benchmark Weibull distribution probability function is fitted to obtain the failure rate function, and then the standard air compressor bathtub curve is obtained.

[0034] In an embodiment, based on the cumulative running time of the full life cycle air compressor and the calculated failure rate, a fitting process is performed on the reference Weibull distribution probability function to obtain a failure rate function, including: based on the reference Weibull distribution probability function and the reliability rate function, an intermediate failure rate function is obtained; based on the cumulative running time of the full life cycle air compressor and the calculated failure rate, the intermediate failure rate function is fitted by using the least square method to obtain the specific value of the correlation coefficient in the reference Weibull distribution probability function; the obtained specific value of the correlation coefficient is substituted into the reference Weibull distribution probability function to obtain the failure rate function.

[0035] The way to obtain the standard air compressor bathtub curve will be explained below:

[0036] The running data of the full life cycle air compressor is obtained. The running data of the full life cycle air compressor includes: equipment failure data, cumulative running time, ratio of inlet flow to rated flow, leakage amount, outlet pressure, inlet temperature and equipment pressure drop of the full life cycle air compressor.

[0037] It should be understood that the cumulative running time refers to the total sum of all running times of the air compressor from the start of use to a certain time point. The inlet flow refers to the volume of air sucked by the air compressor inlet in the actual inlet state. The rated flow refers to the volume of gas compressed and discharged by the air compressor per unit time under standard conditions. The leakage amount refers to the leakage amount of compressed air due to loose sealing of the equipment or other reasons within a certain time. The outlet pressure refers to the pressure value of the compressed gas at the outlet of the air compressor. The inlet temperature refers to the temperature of the gas before entering the air compressor. The equipment pressure drop refers to the pressure drop of the gas flowing in the air compressor system due to factors such as pipeline resistance, valve resistance and internal resistance of the equipment.

[0038] Further, the equipment failure data of the full life cycle air compressor records the number of failures of the full life cycle air compressor and the time of each failure. According to the set time length, the cumulative running time of the full life cycle air compressor is divided into multiple calculation time periods, and multiple corresponding calculation time points are obtained. For example, the running time of the full life cycle air compressor is 1000 hours, and the set time length is 100 hours, so the ten calculation time periods are determined as [1-100], [101-200]…[900-1000], and the multiple calculation time points are 100, 200…1000. It should be noted that the skilled person in the art can set the time length to other values according to actual needs, and the present application does not limit this.

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

[0040] (Formula 1);

[0041] wherein, is the unit of the failure frequency per unit time; n represents the number of failures occurring in the calculation period T; and T represents the calculation period.

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

[0043] Further, it is defined that the change of the failure rate with respect to the cumulative running time of the air compressor satisfies a reference Weibull distribution probability function. The reference Weibull distribution probability function is shown in the following Formula 2:

[0044] (Formula 2);

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

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

[0047] (Formula 3);

[0048] wherein, t represents the time variable, represents the correlation coefficient.

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

[0050] (Formula 4);

[0051] wherein, t represents the time variable, represents the correlation coefficient.

[0052] Further, the intermediate failure rate function h(t) is taken as a logarithm to obtain the following Equation 1:

[0053] (Equation 1);

[0054] The equation is matched with a linear form (y = mx + c) ) one by one to obtain the following Equations 2 to 5:

[0055] (Equation 2);

[0056] (Equation 3);

[0057] (Equation 4);

[0058] (Equation 5);

[0059] Furthermore, the least squares 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 the 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 explains how to obtain the personalized failure rate model of the target air compressor:

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

[0063] The failure rates at multiple moments in the life cycle are determined based on the standard air compressor bathtub curve. Other operating data at multiple moments in the life cycle are mapped to the failure rates at multiple moments in the life cycle in a one-to-one manner to form an air compressor operation data set.

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

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

[0066] The neural network model uses a 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 count 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 loss value is calculated using the mean square error (MSE) loss function shown in the following formula 5:

[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 the MAPE was less than 4%.

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

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

[0072] Further, according to the historical equipment failure data of the target air compressor, the failure rates of multiple time points in the history of the target air compressor are calculated. For example, the length of time of the historical running data of the target air compressor is 1000 hours, and the failure rates in [1-10], [11-20], [20-30]…[991-1000], a total of 100 time periods, are calculated, corresponding to the failure rates of 10, 20, 30…1000 time points. It should be understood that the way of calculating the failure rate can refer to the way in the above embodiments, which will not be described here.

[0073] Further, the historical running data set of the target air compressor is obtained by one-to-one correspondence between the historical multiple time points of the target air compressor and the historical multiple time points of the target air compressor. The historical running data set of the target air compressor is input into the reference failure rate model for training to obtain the individual failure rate model of the target air compressor. The loss function shown in the following formula 6 is used as the error loss function of this training in the training process:

[0074] (Formula 6);

[0075] Wherein, Loss2 is the loss value of this training, N represents the number of multiple time points in the history, W is the weight matrix that needs to be regularized, is a regularization coefficient, is the actual failure rate of this training, is the failure rate predicted by the model of this training.

[0076] It should be noted that the mathematical model of the air compressor equipment failure mechanism (reference air compressor bathtub curve) is introduced in the training process of the failure rate model, which improves the accuracy of the air compressor failure prediction.

[0077] Step S13: According to the current failure rate of the target air compressor and the standard air compressor bathtub curve, the health degree of the target air compressor is diagnosed.

[0078] In an embodiment, the health degree diagnosis of the target air compressor according to the current failure rate of the target air compressor and the standard air compressor bathtub curve comprises: determining a failure rate warning threshold 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 high failure rate; and when the current failure rate of the target air compressor is not higher than the determined failure rate warning threshold, the target air compressor is diagnosed as low failure rate.

[0079] It should be noted that the failure rate warning threshold can be set by a person skilled in the art according to the standard air compressor bathtub curve and actual requirements, and the present application does not limit this.

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

[0081] An operation data acquisition module 41 is configured to acquire current operation data of a target air compressor.

[0082] A failure rate acquisition module 42 is connected with the operation data acquisition module 41 and configured to obtain a current failure rate of the target air compressor based on a personal failure rate model of the target air compressor constructed according to an acquired standard air compressor bathtub curve, according to the current operation data of the target air compressor.

[0083] A health degree diagnosis module 43 is connected with the failure rate acquisition module 42 and configured to diagnose the health degree of 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 performing the corresponding steps described above has been described in detail in the method embodiments described above, and thus will not be described here again for the sake of brevity.

[0085] It should also be understood that the division of the modules in the embodiments of the present application is schematic and is only a logical functional division, and another division mode can be used in actual implementation. In addition, each functional module in each embodiment of the present application can be integrated in one processor, or can be physically separated, or two or more modules can be integrated in one module. The integrated module can be realized in the form of hardware or in the form of a software functional module.

[0086] ​In an embodiment, the method for constructing a personalized failure rate model of a target air compressor according to an obtained standard air compressor bathtub curve comprises: obtaining operation data of a full life cycle air compressor; constructing an air compressor operation data set according to the obtained standard air compressor bathtub curve based on the operation data of the full life cycle air compressor; inputting the preprocessed air compressor operation data set into a 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 obtained historical operation data of the target air compressor; and inputting the historical operation 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 an embodiment, the operation data of the full life cycle air compressor comprises: equipment failure data of the full life cycle air compressor, cumulative running time, ratio of inlet flow rate to rated flow rate, leakage amount, outlet pressure, inlet temperature, and equipment pressure drop.

[0088] In an embodiment, the method for obtaining the standard air compressor bathtub curve comprises: calculating a failure rate of the full life cycle air compressor based on the equipment failure data of the full life cycle air compressor; and fitting a benchmark Weibull distribution probability function based on the cumulative running time of the equipment of the full life cycle air compressor and the calculated failure rate to obtain a failure rate function, and further obtain the standard air compressor bathtub curve.

[0089] In an embodiment, the method for fitting the benchmark Weibull distribution probability function based on the cumulative running time of the equipment of the full life cycle air compressor and the calculated failure rate to obtain the failure rate function comprises: obtaining an intermediate failure rate function based on the benchmark Weibull distribution probability function and a reliability rate function; fitting the intermediate failure rate function by using a least square method based on the cumulative running time of the equipment of the full life cycle air compressor and the calculated failure rate to obtain specific values of correlation coefficients in the benchmark Weibull distribution probability function; and substituting the obtained specific values of the correlation coefficients into the benchmark Weibull distribution probability function to obtain the failure rate function.

[0090] In an embodiment, the method for diagnosing the health degree of the target air compressor according to the current failure rate of the target air compressor and the standard air compressor bathtub curve comprises: determining a failure rate warning threshold according to the standard air compressor bathtub curve; diagnosing the target air compressor as having a high failure rate when the current failure rate of the target air compressor is higher than the failure rate warning threshold; and diagnosing the target air compressor as having a low failure rate when the current failure rate of the target air compressor is not higher than the determined failure rate warning threshold.

[0091] Figure 5is a schematic block diagram of an electronic terminal provided by an embodiment of the present application. As shown in Figure 5 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 apparatus are coupled together by a bus system 504. It can be understood that the bus system 504 is used to realize the connection communication between the components. In addition to including a data bus, the bus system 504 also includes a power bus, a control bus, and a state signal bus. However, for the sake of clarity in Figure 5 , all the buses are marked as the bus system.

[0092] The user interface 505 can include a display, a keyboard, a mouse, a trackball, a click gun, a key, a button, a touchpad, or a touch screen, etc.

[0093] It can be 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. The non-volatile memory can be a read-only memory (ROM, Read Only Memory), a programmable read-only memory (PROM, Programmable Read-Only Memory), which is used as an external cache. By way of example but not limitation, many forms of RAM can be used, such as static random access memory (SRAM, Static Random Access Memory), synchronous static random access memory (SSRAM, Synchronous Static Random Access Memory). The memory described in the embodiments of the present application is intended to include but not limited to these and any other suitable categories of memory.

[0094] The memory 502 in the embodiments of the present application is used to store various categories of data to support the operation of the electronic terminal 500. Examples of these data include any executable programs for operating on the electronic terminal 500, such as an operating system 5021 and an application program 5022. The operating system 5021 contains 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 program 5022 can contain various application programs, such as a media player (Media Player), a browser (Browser), etc., for implementing various application services. The implementation of the bath curve-based air compressor health diagnosis method provided by the embodiments of the present application can be included in the application program 5022.

[0095] The method disclosed in the embodiments of the present application can be applied to the processor 501 or implemented by the processor 501. The processor 501 can be an integrated circuit chip having a signal processing capability. In the implementation process, each step of the above method can be completed by an integrated logic circuit or an instruction in the form of software in the processor 501. The processor 501 described above can be a general processor, a digital signal processor (DSP), or other programmable logic device, discrete gate or transistor logic device, discrete hardware component, etc. The processor 501 can implement or execute the disclosed methods, steps and logic block diagrams in the embodiments of the present application. The general processor 501 can be a microprocessor or any conventional processor, etc. In combination with the steps of the accessory optimization method provided in the embodiments of the present application, the steps can be directly embodied as hardware decoding processor for execution, or 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 to complete the steps of the foregoing method in combination with the hardware thereof.

[0096] In the exemplary embodiments, the electronic terminal 500 can be one or more application specific integrated circuits (ASICs), DSPs, programmable logic devices (PLDs), complex programmable logic devices (CPLDs), or the like for executing the foregoing method.

[0097] According to the method provided in the embodiments of the present application, the present application further provides a computer program product, which comprises computer program code, when the computer program code runs on a computer, causes the computer to execute Figure 1 The air compressor health degree diagnosis method based on the bathtub curve in the embodiments shown.

[0098] According to the method provided in the embodiments of the present application, the present application further provides a computer program product, which comprises computer program code, when the computer program code runs on a computer, causes the computer to execute Figure 1 The air compressor health degree diagnosis method based on the bathtub curve in the embodiments shown.

[0099] As used in this description, the terms "component," "module," "system," and the like are intended to refer to a computer-related entity, either hardware, firmware, a combination of hardware and software, software, or software in execution. For example, a component can be, but is not limited to being, a process running on a processor, a processor, an object, an executable, a thread of execution, a program, and / or a computer. By way of illustration, both an application running on a computing device and the computing device can be a component. One or more components can reside within a process and / or thread of execution and a component can be localized, partially localized, and / or distributed across two or more computers. Also, these components can execute from various computer readable media having various data structures stored thereon. The components can communicate by way of local and / or remote processes such as in accordance with a signal having one or more data packets (e.g., data from one component interacting with another component in a local system, distributed system, and / or across a network such as the Internet with other systems via the signal).

[0100] Those of skill in the art would understand that the various illustrative logical blocks, modules, and steps described in connection with the embodiments disclosed herein can be implemented as electronic hardware, or a combination of computer software and electronic hardware. The choice of hardware or software, or combination thereof, would be dependent on the specific application and design constraints imposed on the overall system. Skilled artisans can implement the described functionality in varying ways for each particular application, but such implementation decisions should not be interpreted as causing a departure from the scope of the present application.

[0101] Those of skill in the art would understand that, for the described convenience and brevity, the specific working process of the system, device and unit described above can refer to the corresponding process in the foregoing method embodiment, which will not be described here.

[0102] In several embodiments provided in the present application, it should be understood that the disclosed system, device and method can be implemented in other ways. For example, the device embodiments described above are merely illustrative, for example, the division of units is only a logical function division, and actual implementation can have another division manner, for example, a plurality of units or components can be combined or integrated into another system, or some features can be ignored or not executed. In addition, the coupling or direct coupling or communication connection between the displayed or discussed each other can be through some interface, 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 separate, and the components displayed as units may or may not be physical units, i.e., may be located in one place, or may be distributed to multiple network units. Part or all of the units can be selected according to actual needs to achieve the purpose of the embodiment.

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

[0105] In the above embodiments, the functions of each functional unit can be implemented by software, hardware, firmware, or any combination thereof, in whole or in part. When implemented by 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 flow or function according to the embodiments 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 devices. Computer instructions can be stored in a computer-readable storage medium or transferred from one computer-readable storage medium to another, for example, computer instructions can be transferred from one website, computer, server or data center to another website, computer, server or data center through wired (such as coaxial cable, optical fiber, digital subscriber line (DSL)) or wireless (such as 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, data center, etc. integrated with one or more available media. The available media can be magnetic media (such as floppy disk, hard disk, magnetic tape), optical media (such as high-density digital video disc (digital video disc, DVD), or semiconductor media (such as solid state disk (solid state disk, SSD), etc.

[0106] If the functions are implemented in the form of software function units and sold or used as independent products, they can be stored in a computer readable storage medium. Based on this understanding, the technical solutions of the present application can be embodied in the form of a software product, which is stored in a storage medium and includes a plurality of instructions for causing a computer device (which can be a personal computer, a server, or a network device, etc.) to execute all or part of the steps of the methods of the various embodiments of the present application. The aforementioned storage medium includes: a U disk, a mobile hard disk, a read-only memory (ROM), a random access memory (RAM), a magnetic disk or an optical disk, and various media that can store program codes.

[0107] The above merely describes the specific embodiments of the present application, but the protection scope of the present application is not limited thereto. Any person skilled in the art can easily think of changes or replacements within the technical scope disclosed by the present application, which should be covered by the protection scope of the present application. Therefore, the protection scope of the present application should be subject to the protection scope of the claims.

[0108] In summary, the present application provides a bath curve-based air compressor health degree diagnosis method, device, terminal, medium and product. The method comprises: obtaining current operation data of a target air compressor; obtaining a current failure rate of the target air compressor based on a personal failure rate model of the target air compressor constructed according to a standard air compressor bath curve according to the current operation data of the target air compressor; and performing health degree diagnosis on the target air compressor according to the current failure rate of the target air compressor and the standard air compressor bath curve. The present application can respond to abnormal conditions of the health degree of the air compressor in time by performing real-time health degree diagnosis on the target air compressor based on the personal failure rate model constructed according to the bath curve, thereby improving the service life of the air compressor. Therefore, the present application effectively overcomes various shortcomings in the prior art and has high industrial utilization value.

[0109] The above embodiments only exemplarily illustrate the principles and effects of the present application, and are not used to limit the present application. Any person skilled in the art can modify or change the above embodiments without departing from the spirit and scope of the present application. Therefore, all equivalent modifications or changes made by those skilled in the art without departing from the spirit and technical idea disclosed by the present application should be covered by the claims of the present application.

Claims

1. A bathtub curve-based air compressor health diagnosis method, characterized by, The method comprises: obtaining current operation data of a target air compressor; obtaining a current failure rate of the target air compressor based on a personal failure rate model of the target air compressor constructed based on a standard air compressor bathtub curve according to the current operation data of the target air compressor; wherein the personal failure rate model of the target air compressor is constructed based on the standard air compressor bathtub curve according to the operation data of the full life cycle air compressor, and the air compressor operation data set is constructed based on the operation data of the full life cycle air compressor according to the standard air compressor bathtub curve, and wherein the air compressor operation data set is constructed based on the operation data of the full life cycle air compressor according to the standard air compressor bathtub curve, comprising: determining the failure rate at multiple time points in the full life cycle according to the standard air compressor bathtub curve; and corresponding the other operation data at the multiple time points in the full life cycle to the failure rate at the multiple time points in the full life cycle according to time to form the air compressor operation data set; the types of the other operation data include: cumulative operating time, ratio of inlet flow to rated flow, leakage, outlet pressure, inlet temperature, and equipment pressure drop; the preprocessed air compressor operation data set is input into a neural network model for training to obtain a benchmark failure rate model; a historical operation data set of the target air compressor is constructed based on the historical operation data of the target air compressor; the historical operation data set of the target air compressor is input into the benchmark failure rate model for training to obtain the personal failure rate model of the target air compressor; the types of the operation 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 for obtaining the standard air compressor bathtub curve comprises: calculating the failure rate of the full life cycle air compressor based on the equipment failure data of the full life cycle air compressor; wherein the failure rate of the full life cycle air compressor comprises: failure rates of multiple calculation time periods; fitting a benchmark Weibull distribution probability function based on the cumulative equipment operating time of the full life cycle air compressor and the calculated failure rate to obtain a failure rate function, and then obtaining the standard air compressor bathtub curve; diagnosing the health degree of the target air compressor according to the current failure rate of the target air compressor and the standard air compressor bathtub curve; wherein diagnosing the health degree of the target air compressor according to the current failure rate of the target air compressor and the standard air compressor bathtub curve comprises: determining a failure rate warning threshold 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 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 low failure rate.

2. The bathtub curve-based air compressor health diagnosis method of claim 1, wherein, fitting the benchmark Weibull distribution probability function based on the cumulative equipment operating time of the full life cycle air compressor and the calculated failure rate to obtain a failure rate function, comprising: Based on the benchmark Weibull distribution probability function and the reliability function, an intermediate failure rate function is obtained; Based on the device cumulative running time of the full life cycle air compressor and the calculated failure rate, the intermediate failure rate function is fitted by using the least square method to obtain the specific value of the correlation coefficient in the benchmark Weibull distribution probability function; The specific value of the correlation coefficient obtained is substituted into the benchmark Weibull distribution probability function to obtain the failure rate function.

3. A bathtub curve based air compressor health diagnostic device, characterized by, It comprises: 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 individual failure rate model of the target air compressor constructed according to the acquired standard air compressor bathtub curve, according to the current operation data of the target air compressor; Wherein, according to the acquired standard air compressor bathtub curve, the individual failure rate model of the target air compressor is constructed, which comprises: acquiring the operation data of the full life cycle air compressor; Based on the operation data of the full life cycle air compressor, the air compressor operation data set is constructed according to the acquired standard air compressor bathtub curve; and wherein, based on the operation data of the full life cycle air compressor, the air compressor operation data set is constructed according to the acquired standard air compressor bathtub curve, which comprises: determining the failure rate at multiple time points in the full life cycle according to the standard air compressor bathtub curve; Corresponding to the failure rate at multiple time points in the full life cycle, other operation data at multiple time points in the full life cycle are constructed into an air compressor operation data set; The type of other operation data includes: cumulative running time, ratio of inlet flow to rated flow, leakage, outlet pressure, inlet temperature and device pressure drop; The preprocessed air compressor operation data set is input into the neural network model for training to obtain the benchmark failure rate model; Based on the acquired historical operation data of the target air compressor, the historical operation data set of the target air compressor is constructed; The historical operation 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; The type of operation data includes: device failure data, cumulative running time, ratio of inlet flow to rated flow, leakage, outlet pressure, inlet temperature and device pressure drop; The way to acquire the standard air compressor bathtub curve comprises: calculating the failure rate of the full life cycle air compressor based on the device failure data of the full life cycle air compressor; wherein, the failure rate of the full life cycle air compressor comprises: the failure rate of multiple calculation time periods; Based on the device cumulative running time of the full life cycle air compressor and the calculated failure rate, the benchmark Weibull distribution probability function is fitted to obtain the failure rate function, and then the standard air compressor bathtub curve is obtained; A health degree diagnosis module for diagnosing the health degree of the target air compressor according to the current failure rate of the target air compressor and the standard air compressor bathtub curve; The health degree of the target air compressor is diagnosed according to the current failure rate of the target air compressor and the standard air compressor bathtub curve, comprising: determining a failure rate warning threshold 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 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 low failure rate.

4. A computer-readable storage medium having stored thereon a computer program, characterized in that, The computer program is executed by the processor to implement the method in any one of claims 1 to 2.

5. A computer program product, characterised in that, The computer program product comprises computer program code, which, when executed on a computer, causes the computer to implement the method in any one of claims 1 to 2.

6. An electronic terminal comprising a memory, a processor and a computer program stored on the memory, characterized in that, The processor executes the computer program to implement the method in any one of claims 1 to 2.

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