Rotation machine equipment service life analysis method and system

Abstract

The invention discloses a rotating machine equipment life analysis method and system, and belongs to the technical field of mechanical equipment life prediction, and the method specifically comprises the steps: collecting data related to the life of rotating machine equipment, carrying out the preprocessing of the collected data related to the life of the rotating machine equipment, and carrying out the analysis of the life of the rotating machine equipment according to the obtained data related to the life of the rotating machine equipment; establishing a rotating machine equipment life analysis model, predicting the life of the rotating machine equipment by using parameter values of the rotating machine equipment life analysis model and the adjustment model, analyzing the life condition of the rotating machine equipment according to a prediction result, finding out an existing risk, and formulating a corresponding maintenance plan or measure. Compared with the prior art that the service life prediction model of the rotating machine equipment is directly trained through the equipment operation data, the precision of predicting the residual service life of the rotating machine equipment is greatly improved, and manpower is saved.

Description

TECHNICAL FIELD

[0001] The application belongs to the technical field of mechanical equipment life prediction, and particularly relates to a rotating machine equipment life analysis method and system. BACKGROUND

[0002] During the working process of the rotating machine equipment, the rotating machine equipment is subjected to the effects of friction, cyclic stress, high and low temperature for a long time and is in a working state for a long time. In this working environment, on the one hand, the lubricating grease structure of the bearing is easily damaged due to the influence of the current, thereby causing the bearing to be damaged due to lubrication failure, and on the other hand, the bearing can be affected by electric corrosion, and pits, plate-like scratches and other damage conditions will appear at the roller and raceway, which will seriously affect the operation safety of the rotating machine equipment.

[0003] In the prior art, the remaining life of the rotating machine equipment is actually mostly predicted by using the traditional periodic inspection method, that is, after the rotating machine equipment completes a certain operation cycle, the surface state of each part of the bearing is manually inspected, the damage degree is observed, and the remaining service life of the bearing is calculated by using relevant mechanical principle formulas or by relying on experience to judge the replacement time of the bearing. This remaining life prediction method is low in efficiency and not accurate enough.

[0004] A device life prediction model training method and a device life prediction method are disclosed in Chinese Patent No. CN117807878A, which are applied to the industrial technology field. The method obtains the running data of devices of preset types, preset functions and preset positions. The running data has a remaining life label. The first data component of the non-vibration physical quantity in the running data is obtained based on the STL algorithm and the EEMD algorithm. The second data component of the vibration physical quantity in the running data is obtained based on the VMD algorithm. The data set is constructed based on the first data component, the second data component and the running data. The initial device life prediction model is trained based on the data set to obtain the trained device life prediction model. The data components obtained by feature extraction of the device running data are used to construct the data set for training, which improves the accuracy of the model prediction result compared with the prior art of directly training the device remaining device life prediction model based on the device running data.

[0005] A device life prediction method and device are disclosed in Chinese Patent Publication No. CN114154415A. The method includes: obtaining parameter data of a target device, wherein the parameter data includes: first historical maintenance data, first current operation data, and first energy consumption data; extracting first data features of the parameter data, wherein the first data features include: root mean square, peak value, energy, entropy, inverse hyperbolic sine standard deviation, and inverse tangent standard deviation; calculating the first data features using a feature accumulation algorithm to obtain first accumulated features; and inputting the first accumulated features into a wavelet limit superposition model to output the remaining useful life of the target device. The above technical means can solve the problem of low accuracy of estimating the remaining useful life of the device by testing the device in the prior art, thereby improving the accuracy of estimating the remaining useful life of the device.

[0006] The defects of the above patent are: 1) no further feature extraction is performed on the transfer machine equipment operation data, resulting in poor accuracy of the transfer machine equipment remaining device life prediction model; 2) the above method cannot be directly applied to the transfer machine equipment. SUMMARY

[0007] To overcome the shortcomings of the prior art, the present application provides a transfer machine equipment life analysis method and system, which collects data related to the life of the transfer machine equipment, pre-processes the collected data related to the life of the transfer machine equipment, establishes a transfer machine equipment life analysis model based on the obtained data related to the life of the transfer machine equipment, uses the parameters of the transfer machine equipment life analysis model and the adjustment model to predict the life of the transfer machine equipment, analyzes the life status of the transfer machine equipment based on the predicted results, finds out the existing risks, and formulates corresponding maintenance plans or measures to ensure the safe operation and prolong the service life of the transfer machine equipment.

[0008] To achieve the above object, the present application provides the following technical scheme:

[0009] A transfer machine equipment life analysis method, comprising the following specific steps:

[0010] Step S1: collecting data related to the life of the transfer machine equipment;

[0011] Step S2: pre-processing the collected data related to the life of the transfer machine equipment;

[0012] Step S3: establishing a transfer machine equipment life analysis model based on the obtained data related to the life of the transfer machine equipment, and using the parameters of the transfer machine equipment life analysis model and the adjustment model to predict the life of the transfer machine equipment;

[0013] Step S4: According to the predicted results, analyze the life condition of the transfer equipment, find out the existing risks, and formulate the corresponding maintenance plan or measures to ensure the safe operation and prolong the service life of the transfer equipment.

[0014] Specifically, the data related to the life of the transfer equipment in step S1 include the production date, use time, working environment, transfer equipment operation data and historical maintenance records of the transfer equipment.

[0015] Specifically, the preprocessing in step S2 includes data cleaning, processing of abnormal values and correction of missing values.

[0016] Specifically, the specific method of step S3 is:

[0017] Step S301: Set the transfer equipment operation data set in the preprocessed transfer equipment life related data as Y, Wherein represents the mth transfer equipment operation data set at the t T th moment,

[0018] Here, the mth transfer equipment operation data set at the t T th moment is the mth type of transfer equipment operation data at the t T th moment, and one transfer equipment operation data set is a type of data set, including temperature, operation efficiency, processing efficiency and wear, etc.

[0019] Step S302: Extract the characteristics of the jth transfer equipment operation data at the t T th moment, and the specific formula for extracting the characteristics is:

[0020]

[0021] Wherein, Tz j represents the characteristics of the jth transfer equipment operation data at the t T th moment, W1 and W2 represent the transfer equipment operation data characteristic weight parameters, T represents the total running time of the transfer equipment, α and β represent the transfer equipment operation data characteristic correction parameters, and λ represents the deviation coefficient.

[0022] Step S303: Calculate the influence factor of the jth transfer equipment operation data on the life of the transfer equipment, and the calculation formula is:

[0023]

[0024] Wherein, δ j represents the influence factor of the jth transfer equipment operation data on the life of the transfer equipment, and Pj jwherein, Sj represents the jth transfer equipment operation data, m represents the number of transfer equipment operation data types;

[0025] Step S304: According to the probability P of the transfer equipment failure at each time gz , the influence factor of the failure on the transfer equipment life is calculated, and the calculation formula is:

[0026] η=P gz ×(1-K(t)), wherein, η represents the influence factor of the failure on the transfer equipment life, and K(t) represents the reliability after the transfer equipment operation t time;

[0027] Step S305: The remaining life of the transfer equipment is predicted, and the calculation formula is:

[0028]

[0029] wherein, Sm represents the remaining life of the transfer equipment, Sm bz represents the standard service life of the transfer equipment, and Hj represents the environmental influence factor.

[0030] Specifically, the calculation formula of the probability P of the transfer equipment failure at each time in the step S304 is: gz wherein, Gz represents the number of failures in the historical maintenance records of the transfer equipment, and use represents the historical use times of the transfer equipment.

[0031] Specifically, the maintenance plan or measures in the step S4 include cleaning, tidying, lubricating and safety.

[0032] A transfer equipment life analysis system, comprising: a data acquisition module, a data preprocessing module, a life analysis and prediction module, and a maintenance and care module;

[0033] The data acquisition module is used for acquiring data related to the life of the transfer equipment.

[0034] The data preprocessing module is used for preprocessing the acquired data related to the life of the transfer equipment.

[0035] The life analysis and prediction module is used for establishing a transfer equipment life analysis model according to the obtained data related to the life of the transfer equipment, and predicting the life of the transfer equipment by using the transfer equipment life analysis model and the parameter values of the adjustment model.

[0036] The care module is used for analyzing the life condition of the transfer equipment according to the predicted result, finding out the existing risks, and formulating corresponding maintenance plans or measures to ensure the safe operation and prolong the service life of the transfer equipment.

[0037] ​Specifically, the life analysis and prediction module comprises a life influence factor calculation unit and a residual life prediction unit.

[0038] The life influence factor calculation unit is configured to calculate the influence factor of the transfer machine equipment operation data on the life of the transfer machine equipment and the influence factor of the fault on the life of the transfer machine.

[0039] The residual life prediction unit is configured to perform a prediction calculation on the residual life of the transfer machine equipment.

[0040] An electronic device comprises a memory and a processor, the memory stores a computer program, and the processor implements the steps of a transfer machine life analysis method when executing the computer program.

[0041] A computer readable storage medium stores computer instructions, and when the computer instructions are executed, the steps of a transfer machine life analysis method are performed.

[0042] Compared with the prior art, the beneficial effects of the present application are:

[0043] 1. The present application proposes a transfer machine life analysis system, and the architecture, operation steps and flow are optimized and improved, and the system has the advantages of simple flow, low investment and operation cost, and low production cost.

[0044] 2. The present application proposes a transfer machine life analysis method, which first preprocesses the collected transfer machine equipment data, and then extracts features, which improves the accuracy of the model prediction result compared with the prior art of directly training a transfer machine life prediction model through equipment operation data.

[0045] 3. The present application proposes a transfer machine life analysis method, which considers the influence of the transfer machine equipment environment and the number of faults, and adds the equipment environment, fault probability and fault influence factor when establishing the transfer machine life prediction model, which greatly improves the accuracy of predicting the residual life of the transfer machine equipment and saves manpower.

[0046] 4. The present application proposes a transfer machine life analysis method, which is less researched in the prior art for the residual life of the transfer machine equipment, and proposes a method for predicting and calculating the residual life of the transfer machine equipment, which has great significance for the research and development of the transfer machine equipment. BRIEF DESCRIPTION OF DRAWINGS

[0047] Figure 1 The present application is a flow chart of a transfer machine life analysis method.

[0048] Figure 2 The present application is a system architecture diagram of a transfer machine life analysis system.

[0049] Figure 3Electronic device diagram for a turning equipment life analysis method of the present application. DETAILED DESCRIPTION

[0050] In order to make the technical means, creative features, purposes and effects of the present application easy to understand, in the description of the present application, it should be noted that the terms "center", "upper", "lower", "left", "right", "vertical", "horizontal", "inner", "outer" and the like indicate the orientation or positional relationship shown in the drawings, and are only for the convenience of describing the present application and simplifying the description, and do not indicate or imply that the devices or elements referred to must have a particular orientation, be constructed and operated in a particular orientation, and therefore cannot be understood as limiting the present application. In addition, the terms "No. 1", "No. 2", "No. 3" are only for the purpose of description, and cannot be understood as indicating or implying relative importance. The present application will be further described below in conjunction with the specific embodiments.

[0051] Embodiment 1

[0052] Please refer to Figure 1 The present application provides an embodiment: a turning equipment life analysis method, comprising the following specific steps:

[0053] Step S1: collecting data related to the life of the turning equipment;

[0054] Step S2: preprocessing the collected data related to the life of the turning equipment;

[0055] Step S3: establishing a turning equipment life analysis model according to the obtained data related to the life of the turning equipment, and using the parameters of the turning equipment life analysis model and the adjustment model to predict the life of the turning equipment;

[0056] Step S4: analyzing the life status of the turning equipment according to the predicted results, finding out the existing risks, and formulating corresponding maintenance plans or measures to ensure the safe operation and prolong the service life of the turning equipment.

[0057] The data related to the life of the turning equipment in step S1 includes the production date, use time, working environment, turning equipment operation data and historical maintenance records of the turning equipment.

[0058] The preprocessing in step S2 includes data cleaning, processing of abnormal values and correction of missing values.

[0059] The specific method of step S3 is:

[0060] Step S301: set the turning equipment operation data set in the preprocessed data related to the life of the turning equipment as Y, Wherein Ym(t) represents the mth turning equipment operation data at the tth time point.T a set of transfer equipment operation data at time t,

[0061] the mth type of transfer equipment operation data at time t T a set of transfer equipment operation data at time t, the mth type of transfer equipment operation data at time t T A set of transfer equipment operation data is a set of data of one type, and the types include temperature, operation efficiency, processing efficiency, and wear, etc.

[0062] Step S302: Extract the feature of the jth transfer equipment operation data at time t T The specific formula for extracting the feature is:

[0063]

[0064] Wherein, Tz j represents the feature of the jth transfer equipment operation data at time t T , W1 and W2 represent the transfer equipment operation data feature weight parameters, T represents the total running time of the transfer equipment, α and β represent the transfer equipment operation data feature correction parameters, and λ represents the deviation coefficient;

[0065] Step S303: Calculate the influence factor of the jth transfer equipment operation data on the service life of the transfer equipment, and the calculation formula is:

[0066]

[0067] Wherein, δ j represents the influence factor of the jth transfer equipment operation data on the service life of the transfer equipment, Pj j represents the average value of the jth transfer equipment operation data, and m represents the number of types of transfer equipment operation data;

[0068] Step S304: According to the probability P gz of failure of the transfer equipment at each time, the influence factor of the failure on the service life of the transfer equipment is calculated, and the calculation formula is:

[0069] η=P gz ×(1-K(t)), wherein η represents the influence factor of the failure on the service life of the transfer equipment, and K(t) represents the reliability after the transfer equipment runs t time;

[0070] Step S305: Predict the remaining life of the transfer equipment, and the calculation formula is:

[0071]

[0072] Wherein, Sm represents the remaining life of the transfer equipment, and Sm bzHj represents the factory standard service life of the transfer machine, and Hj represents the environmental influence factor.

[0073] The environmental influence factor Hj here mainly includes the oxygen and water molecule content in the air, dust, and oil dirt, which can cause the transfer machine to rust and corrode, and the calculation formula is: wherein ζ O represents the influence coefficient of the oxygen content in the air on the transfer machine, ζ H represents the influence coefficient of the water molecule content in the air on the transfer machine, H hl represents the water molecule content in the air, Yg mj represents the surface area covered by oil dirt in the transfer machine, Yg z represents the surface area of the transfer machine exposed to the air, ζ Yg represents the influence coefficient of oil dirt on the transfer machine, ζ O , ζ H , and ζ Yg are obtained through experiments.

[0074] The calculation formula of the probability P gz that the transfer machine fails at each time in step S304 is: wherein Gz represents the number of times of failures in the historical maintenance records of the transfer machine, and use represents the historical use number of the transfer machine.

[0075] The maintenance plan or measure in step S4 includes cleaning, tidying, lubrication, and safety; cleaning: the inside and outside of the equipment are clean, no oil and gas leakage in each part, and the cuttings, sundries, and dirt around the equipment are cleaned; tidying: tools, accessories, and workpieces (products) are placed neatly; good lubrication: no dry grinding phenomenon, normal oil pressure, unobstructed oil path, and oil quality meeting the requirements; safety: safety operation procedures are followed, the equipment is not used beyond the load, the safety protection devices of the equipment are complete and reliable, and unsafe factors are eliminated in time.

[0076] Embodiment 2

[0077] Please refer to Figure 2 , the present application provides another embodiment: a transfer machine life analysis system, comprising a data acquisition module, a data preprocessing module, a life analysis and prediction module, and a maintenance and care module.

[0078] The data acquisition module is used for acquiring data related to the life of the transfer machine.

[0079] The data preprocessing module is used for preprocessing the acquired data related to the life of the transfer machine.

[0080] The life analysis prediction module is configured to establish a life analysis model of the transfer equipment according to the obtained data related to the life of the transfer equipment, and to predict the life of the transfer equipment by using the life analysis model of the transfer equipment and the parameter values of the adjustment model.

[0081] The maintenance module is configured to analyze the life condition of the transfer equipment according to the predicted result, to find out the existing risks, and to formulate a corresponding maintenance plan or measure to ensure the safe operation and prolong the service life of the transfer equipment.

[0082] The life analysis prediction module comprises a life influence factor calculation unit and a residual life prediction unit.

[0083] The life influence factor calculation unit is configured to calculate the influence factors of the operation data of the transfer equipment on the life of the transfer equipment and the influence factors of the faults on the life of the transfer equipment.

[0084] The residual life prediction unit is configured to predict and calculate the residual life of the transfer equipment.

[0085] Embodiment 3

[0086] Please refer to Figure 3 An electronic device comprises a memory and a processor, the memory stores a computer program, and the processor implements the steps of the transfer equipment life analysis method when executing the computer program.

[0087] A computer readable storage medium stores computer instructions, and the computer instructions execute the steps of the transfer equipment life analysis method when running.

[0088] Those skilled in the art should understand that the embodiments of the present application can be provided as a method, a system, or a computer program product. Therefore, the present application can take the form of an entirely hardware embodiment, an entirely software embodiment, or an embodiment combining software and hardware aspects. Moreover, the present application can take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0089] The present application is described with reference to flowcharts and / or block diagrams of the method, device (system), and computer program product according to the embodiments of the present application. It should be understood that each flow and / or block in the flowcharts and / or block diagrams, and the combination of the flows and / or blocks in the flowcharts and / or block diagrams can be implemented by computer program instructions. These computer program instructions can be provided to the processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device produce a device that implements the functions described in the flowcharts and / or block diagrams.Figure 1 one or more processes and / or blocks Figure 1 an apparatus for performing functions specified in the flow

[0090] These computer program instructions can also be stored in a computer readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer readable memory produce an article of manufacture including instructions which implement the Figure 1 one or more processes and / or blocks Figure 1 an apparatus for performing functions specified in the flow

[0091] These computer program instructions can also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions that are executed on the computer or other programmable apparatus provide steps for implementing the Figure 1 one or more processes and / or blocks Figure 1 an apparatus for performing functions specified in the flow

[0092] The above embodiments of the present application have been described only with regard to their current embodiments and not with regard to all integers that can possibly be made or used thereof. However, all such suitable combinations and permutations of the above described elements do not fall within the scope of the present application as it is described in the appended claims and can be considered as falling within the scope of the present application.

[0093] Finally, the above description is intended to be illustrative and not restrictive. Many other embodiments will be apparent to those of skill in the art upon reviewing the above description. The scope of the application should, therefore, be determined not with reference to the above description, but instead with reference to the appended claims, along with their full scope of equivalents.

Claims

1. A method for analyzing the lifespan of rotating equipment, characterized in that, The specific steps include the following: Step S1: Collect data related to the lifespan of the transfer equipment; Step S2: Preprocess the collected data related to the lifespan of the rotating equipment; Step S3: Based on the obtained data related to the lifespan of the rotating equipment, establish a lifespan analysis model for the rotating equipment, and use the parameter values ​​of the lifespan analysis model and the adjustment model to predict the lifespan of the rotating equipment. Step S4: Based on the prediction results, analyze the lifespan of the equipment, identify existing risks, and formulate corresponding maintenance plans or measures to ensure the safe operation of the equipment and extend its service life.

2. The method for analyzing the lifespan of a rotating equipment as described in claim 1, characterized in that, The data related to the lifespan of the transfer equipment in step S1 includes: the production date of the transfer equipment, the usage time, the working environment, the operation data of the transfer equipment, and historical maintenance records.

3. The method for analyzing the lifespan of a rotating equipment as described in claim 1, characterized in that, The preprocessing in step S2 includes: data cleaning, handling outliers, and correcting missing values.

4. The method for analyzing the lifespan of a rotating equipment as described in claim 1, characterized in that, The specific method for step S3 is as follows: Step S301: Set the set of rotating equipment operation data in the preprocessed rotating equipment life-related data as Y. in It means that the m-th element is in the t-th order. T The collection of operating data of the time-transfer equipment. Step S302: Extract the j-th element in t T The specific formula for extracting features from the operating data of the transfer equipment within a given time period is as follows: Among them, Tz j Let W1 and W2 represent the feature of the transfer equipment operation data at time tT, T represent the total operation time of the transfer equipment, α and β represent the correction parameters of the transfer equipment operation data feature, and λ represent the deviation coefficient. Step S303: Calculate the impact factor of the operating data of the j-th rotating equipment on the lifespan of the rotating equipment. The calculation formula is as follows: Where, δ j Pj represents the influence factor of the operating data of the j-th transfer equipment on the lifespan of the transfer equipment. j This represents the average value of the operating data of the j-th transfer equipment, and m represents the number of types of operating data for the transfer equipment; Step S304: Based on the probability P of the transfer equipment failing at each moment gz The impact factor of the fault on the lifespan of the turbine was calculated using the following formula: η = P gz ×(1-K(t)), where η represents the impact factor of the fault on the life of the rotating machine, and K(t) represents the reliability of the rotating machine after time t. Step S305: Establish a life analysis model for the rotating equipment to predict its remaining lifespan. The calculation formula is as follows: Where Sm represents the remaining lifespan of the transfer equipment, Sm bz Hj represents the standard service life of the equipment at the time of manufacture, and Hj represents the environmental impact factor.

5. The method for analyzing the lifespan of a rotating equipment as described in claim 4, characterized in that, The formula for calculating the probability Pgz of the transfer equipment failing at each moment in step S304 is as follows: Where Gz represents the number of times a fault occurred in the historical maintenance record of the transfer equipment, and use represents the number of times the transfer equipment was used in history.

6. The method for analyzing the lifespan of a rotating equipment as described in claim 5, characterized in that, The maintenance plan or measures in step S4 include: cleaning, tidying, lubrication, and safety.

7. A life analysis system for rotating equipment, used to implement the life analysis method for rotating equipment according to any one of claims 1-6, characterized in that, include: The system includes a data acquisition module, a data preprocessing module, a lifespan analysis and prediction module, and a maintenance and upkeep module. The data acquisition module is used to collect data related to the lifespan of the transfer equipment; The data preprocessing module is used to preprocess the collected data related to the lifespan of the rotating equipment. The lifespan analysis and prediction module is used to establish a lifespan analysis model for the rotating equipment based on the obtained data related to the lifespan of the rotating equipment, and to predict the lifespan of the rotating equipment using the parameter values ​​of the lifespan analysis model and the adjustment model. The maintenance module is used to analyze the lifespan of the rotating equipment based on the prediction results, identify existing risks, and formulate corresponding maintenance plans or measures to ensure the safe operation of the rotating equipment and extend its service life.

8. The life analysis system for rotating equipment as described in claim 7, characterized in that, The lifespan analysis and prediction module includes: a lifespan impact factor calculation unit and a remaining lifespan prediction unit; The lifespan impact factor calculation unit is used to calculate the impact factor of the operating data of the rotating equipment on the lifespan of the rotating equipment and the impact factor of the fault on the lifespan of the rotating equipment. The remaining life prediction unit is used to predict and calculate the remaining life of the rotating equipment.

9. An electronic device comprising a memory and a processor, wherein the memory stores a computer program, characterized in that, When the processor executes the computer program, it implements the steps of the method for analyzing the lifespan of a rotating equipment as described in any one of claims 1-6.

10. A computer-readable storage medium, characterized in that, It stores computer instructions, which, when executed, perform the steps of the life analysis method for a rotating equipment as described in any one of claims 1-6.

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