Method, device and equipment for predicting service life of large-end bearing of engine connecting rod

Abstract

The invention discloses an engine connecting rod large-head bearing life prediction method, device and equipment, and relates to the technical field of engines, and the method comprises the steps: determining a physical model fatigue factor of a connecting rod large-head bearing based on a fatigue analysis physical model of the engine connecting rod large-head bearing, and mapping the physical model fatigue factor into an initial category label; performing one-dimensional depth feature extraction on the collected segmented vibration signals to obtain feature vectors; a classification unit and a clustering unit are initialized based on the initial category labels, a loss function between the input initial category labels and the output category labels is determined, and network parameters are updated; and calculating divergence difference, distributing dynamic weight, correcting the initial category label, obtaining output results of the target classification unit and the target clustering unit, and generating a life prediction result of the connecting rod large-end bearing. Through the mode, the advantages of deep learning and a physical model are combined, the service life of the engine connecting rod large-end bearing is predicted, and the prediction accuracy and reliability can be improved.

Description

Technical Field

[0001] The present application relates to the field of engine technology, and in particular to a method, device and equipment for predicting the life of an engine connecting rod big end bearing. Background Art

[0002] The connecting rod big end bearing is a key component that connects the connecting rod big end and the crankshaft. On the one hand, it transmits the gas combustion pressure borne by the piston to the crankshaft to achieve power conversion and output. On the other hand, the connecting rod big end bearing supports the rotation of the crankshaft, ensures the stability and accuracy of the crankshaft when rotating at high speed, and reduces the vibration and wear caused by unbalanced forces. Any form of failure of the connecting rod big end bearing may cause serious engine damage, such as bearing damage, crankshaft bending or breaking, and may even cause complete engine failure. Therefore, ensuring the reliable durability and life of the connecting rod big end bearing is crucial to the long-term stable operation, reliability, and safety of the engine.

[0003] At present, for the life prediction of connecting rod big end bearings, the traditional empirical formula method has limited accuracy, is difficult to adapt to the complex and changeable actual working conditions, is overly dependent on historical data, and is not accurate enough for the prediction of bearing life under new engines or special working conditions; the traditional finite element analysis method uses engineering software to perform mechanical analysis to predict the stress and strain distribution of connecting rod big end bearings under specific working conditions, including model establishment, boundary setting, model debugging and operation calculation, which requires a large amount of simulation resources and has a long cycle and high computing cost, and is not suitable for real-time monitoring and rapid prediction; traditional machine learning methods are highly dependent on feature engineering, and it is difficult to process high-dimensional data and capture complex patterns in time series, and their accuracy needs to be improved.

[0004] The above contents are only used to assist in understanding the technical solution of the present invention and do not constitute an admission that the above contents are prior art. Summary of the invention

[0005] The main purpose of the present application is to provide a method, device and equipment for predicting the life of an engine connecting rod big end bearing, aiming to solve the technical problem of low accuracy in predicting the life of an engine connecting rod big end bearing in the prior art.

[0006] To achieve the above objectives, the present application provides a method for predicting the life of an engine connecting rod big end bearing, the method comprising:

[0007] Based on the fatigue analysis physical model of the engine connecting rod big end bearing, the physical model fatigue factor of the connecting rod big end bearing is determined, and the physical model fatigue factor is mapped to an initial category label;

[0008] Obtain the vibration signals of the connecting rod big end bearing at different times during the entire life cycle, perform one-dimensional deep feature extraction on the segmented vibration signals, and obtain the feature vector corresponding to the vibration signal;

[0009] Initialize the classification unit and the clustering unit based on the initial category label, input the feature vector into the classification unit and the clustering unit, determine the loss function between the initial category label and the output category label, and update the network parameters of the classification unit and the clustering unit;

[0010] Assign dynamic weights based on the divergence difference between the initial category labels and the category labels;

[0011] Based on the dynamic weight, the initial category label is corrected, the loss function and the dynamic weight are re-determined, and the iteration is continued until the termination condition is met to obtain the target classification unit and the target clustering unit with the target initial category label;

[0012] Based on the output results of the target classification unit and the target clustering unit, the life prediction result of the connecting rod big end bearing is determined.

[0013] In one embodiment, based on the fatigue analysis physical model of the engine connecting rod big end bearing, the step of determining the fatigue factor of the physical model of the connecting rod big end bearing further includes:

[0014] Obtain fatigue influencing parameters of the connecting rod big end bearing, the fatigue influencing parameters at least including local maximum stress, nominal stress, average stress, tensile limit strength of bearing material, fatigue strength of standard test piece, fatigue strength of real sample, surface fatigue strength of smooth standard test piece, surface fatigue strength of real sample, symmetric fatigue limit of bearing material and sensitivity coefficient of bearing material to stress cycle asymmetry;

[0015] Based on the first correspondence between fatigue influencing parameters and fatigue factors of the physical model, a fatigue analysis physical model is constructed.

[0016] In one embodiment, the step of performing one-dimensional deep feature extraction on the segmented vibration signal to obtain a feature vector corresponding to the vibration signal includes:

[0017] The segmented vibration signal is subjected to one-dimensional deep feature extraction through a one-dimensional deep self-encoding feature unit to obtain a feature vector corresponding to the vibration signal. The one-dimensional deep self-encoding feature unit includes an encoding network and a decoding network. The encoding network includes a convolution layer and a pooling layer, and the decoding network includes an upsampling layer and a convolution layer.

[0018] In one embodiment, the category label includes a first category label and a second category label, and the steps of inputting the feature vector into the classification unit and the clustering unit, determining the loss function between the initial category label and the output category label, and updating the network parameters of the classification unit and the clustering unit include:

[0019] The feature vector is input into the classification unit and the clustering unit to obtain the first category label output by the classification unit and the second category label output by the clustering unit respectively;

[0020] Calculate the classification loss based on the initial category label and the first category label;

[0021] Calculate the clustering loss based on the initial category label and the second category label;

[0022] Determine the loss function based on classification loss and clustering loss;

[0023] Based on the gradient information of the loss function, the network parameters of the classification unit and the clustering unit are updated.

[0024] In one embodiment, based on the classification loss and the clustering loss, the step of determining the loss function includes:

[0025] Obtain the reconstruction loss of vibration signals during one-dimensional deep feature extraction;

[0026] Obtain the second correspondence between classification loss, clustering loss, reconstruction loss, balance coefficient and loss function;

[0027] Based on the classification loss, the clustering loss, the reconstruction loss, the balancing coefficient, and the second correspondence, a loss function between the initial category label and the category label is determined.

[0028] In one embodiment, based on the divergence difference between the initial category label and the category label, before the step of allocating the dynamic weight, the step further includes:

[0029] Based on the category labels and the initial category labels, determine the label mean;

[0030] Obtain the third correspondence between the initial category label, label mean and divergence difference;

[0031] Based on the initial category label, the label mean, and the third corresponding relationship, the divergence difference between the initial category label and the category label is determined.

[0032] In one embodiment, the step of assigning a dynamic weight based on the divergence difference between the initial category label and the category label includes:

[0033] Obtaining a fourth correspondence between the divergence difference, the adjustment parameter, and the dynamic weight;

[0034] The dynamic weight is determined based on the divergence difference between the initial category label and the category label, the adjustment parameter, and the fourth corresponding relationship.

[0035] In one embodiment, the step of modifying the initial category label based on the dynamic weight includes:

[0036] Obtaining a fifth corresponding relationship between the initial category label, the category label, the weight of the category label, the dynamic weight, the transformation coefficient, and the corrected initial category label;

[0037] Based on the initial category label, the category label, the weight of the category label, the dynamic weight, the transformation coefficient, and the fifth corresponding relationship, a revised initial category label is determined.

[0038] In addition, to achieve the above purpose, the present application also proposes a life prediction device for an engine connecting rod big end bearing, the engine connecting rod big end bearing life prediction device comprising:

[0039] An analysis module, for determining a physical model fatigue factor of the connecting rod big end bearing based on a fatigue analysis physical model of the engine connecting rod big end bearing, and mapping the physical model fatigue factor to an initial category label;

[0040] The correction module is used to obtain the vibration signals of the connecting rod big end bearing at different times during the whole life cycle, perform one-dimensional deep feature extraction on the segmented vibration signals, and obtain the feature vector corresponding to the vibration signal;

[0041] The correction module is further used to initialize the classification unit and the clustering unit based on the initial category label, input the feature vector into the classification unit and the clustering unit, determine the loss function between the initial category label and the output category label, and update the network parameters of the classification unit and the clustering unit;

[0042] The correction module is also used to assign dynamic weights based on the divergence difference between the initial category labels and the category labels;

[0043] The correction module is also used to correct the initial category label based on the dynamic weight, redefine the loss function and the dynamic weight, and iterate continuously until the termination condition is met to obtain the target classification unit and the target clustering unit with the target initial category label;

[0044] The prediction module is used to determine the life prediction result of the connecting rod big end bearing based on the output results of the target classification unit and the target clustering unit.

[0045] In addition, to achieve the above-mentioned purpose, the present application also proposes an engine connecting rod big end bearing life prediction device, which includes: a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the computer program is configured to implement the steps of the engine connecting rod big end bearing life prediction method as described above.

[0046] In addition, to achieve the above-mentioned purpose, the present invention also proposes a storage medium, which is a computer-readable storage medium, and a computer program is stored on the storage medium. When the computer program is executed by a processor, the steps of the engine connecting rod big end bearing life prediction method as described above are implemented.

[0047] In addition, to achieve the above-mentioned purpose, the present application also provides a computer program product, which includes a computer program. When the computer program is executed by a processor, the steps of the engine connecting rod big end bearing life prediction method as described above are implemented.

[0048] The present application provides a life prediction method for an engine connecting rod big end bearing. Based on a fatigue analysis physical model of an engine connecting rod big end bearing, a physical model fatigue factor of the connecting rod big end bearing is determined, and the physical model fatigue factor is mapped to an initial category label; vibration signals of the connecting rod big end bearing at different times during the entire life cycle are obtained, and one-dimensional deep feature extraction is performed on the segmented vibration signal to obtain a feature vector corresponding to the vibration signal; a classification unit and a clustering unit are initialized based on the initial category label, and the feature vector is input into the classification unit and the clustering unit to determine the loss function between the initial category label and the output category label, and the network parameters of the classification unit and the clustering unit are updated; dynamic weights are assigned based on the divergence difference between the initial category label and the category label; based on the dynamic weights, the initial category label is corrected, the loss function and the dynamic weights are re-determined, and the iteration is continued until the termination condition is met to obtain a target classification unit and a target clustering unit with a target initial category label; based on the output results of the target classification unit and the target clustering unit, the life prediction result of the connecting rod big end bearing is determined. Based on the physical analysis model, this application assigns an initial life label to the collected vibration signal of the connecting rod big end bearing, then extracts the deep features of the vibration signal, and finally based on the KL divergence, continuously and adaptively corrects the life label during the model training process, and finally obtains a more stable and accurate life prediction result. It combines the advantages of deep learning and physical models to improve the accuracy and reliability of the prediction, and through multi-dimensional analysis, considering the actual operating conditions and material properties of the equipment, it can adapt to the life prediction needs under different working conditions and usage conditions, reduces the dependence on manual experience, has good versatility and adaptability, and solves the technical problem of low accuracy in life prediction of engine connecting rod big end bearings. BRIEF DESCRIPTION OF THE DRAWINGS

[0049] The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the present application and, together with the description, serve to explain the principles of the present application.

[0050] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, for ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative labor.

[0051] Figure 1 This is a flow chart of Embodiment 1 of the method for predicting the life of the engine connecting rod big end bearing of the present application;

[0052] Figure 2 A schematic diagram of the coding network structure of the engine connecting rod big end bearing life prediction method provided in Example 1 of the present application;

[0053] Figure 3 A schematic diagram of a decoding network structure of a method for predicting the life of an engine connecting rod big end bearing provided in Example 1 of the present application;

[0054] Figure 4 This is a schematic diagram of the overall structure of the engine connecting rod big end bearing life prediction method provided in Example 1 of the present application;

[0055] Figure 5 A schematic diagram of a simplified process of a method for predicting the life of an engine connecting rod big end bearing provided in Example 1 of the present application;

[0056] Figure 6 This is a schematic diagram of the module structure of the engine connecting rod big end bearing life prediction device according to an embodiment of the present application;

[0057] Figure 7 Schematic diagram of the equipment structure of the hardware operating environment involved in the engine connecting rod big end bearing life prediction method in the embodiment of the present application.

[0058] The realization of the purpose, functional features and advantages of this application will be further explained in conjunction with embodiments and with reference to the accompanying drawings. DETAILED DESCRIPTION

[0059] It should be understood that the specific embodiments described herein are only used to explain the technical solutions of the present application and are not used to limit the present application.

[0060] In order to better understand the technical solution of the present application, a detailed description will be given below in conjunction with the accompanying drawings and specific implementation methods.

[0061] The main solution of the embodiment of the present application is: based on the fatigue analysis physical model of the engine connecting rod big end bearing, determine the physical model fatigue factor of the connecting rod big end bearing, and map the physical model fatigue factor to the initial category label; obtain the vibration signals of the connecting rod big end bearing at different times in the whole life cycle, perform one-dimensional deep feature extraction on the segmented vibration signal, and obtain the feature vector corresponding to the vibration signal; initialize the classification unit and the clustering unit based on the initial category label, input the feature vector into the classification unit and the clustering unit, determine the loss function between the initial category label and the output category label, and update the network parameters of the classification unit and the clustering unit; assign dynamic weights based on the divergence difference between the initial category label and the category label; based on the dynamic weight, correct the initial category label, redetermine the loss function and the dynamic weight, and iterate continuously until the termination condition is met to obtain the target classification unit and the target clustering unit with the target initial category label; based on the output results of the target classification unit and the target clustering unit, determine the life prediction result of the connecting rod big end bearing.

[0062] The present application provides a solution, which assigns an initial life label to the collected vibration signal of the connecting rod big end bearing according to the physical analysis model, then extracts the deep features of the vibration signal, and finally based on the KL divergence, continuously and adaptively corrects the life label during the model training process, and finally obtains a more stable and accurate life prediction result. It combines the advantages of deep learning and physical models, improves the accuracy and reliability of the prediction, and through multi-dimensional analysis, considers the actual operating conditions and material properties of the equipment, can adapt to the life prediction needs under different working conditions and usage conditions, reduces the dependence on manual experience, has good versatility and adaptability, and solves the technical problem of low accuracy of life prediction of engine connecting rod big end bearings.

[0063] It should be noted that the execution subject of this embodiment can be a computing service device with data processing, network communication and program running functions, such as a tablet computer, a personal computer, a mobile phone, etc., or an electronic device capable of realizing the above functions, an engine connecting rod big end bearing life prediction device, etc., and this embodiment does not specifically limit this. The following takes the engine connecting rod big end bearing life prediction device as an example to illustrate this embodiment and the following embodiments.

[0064] The present application embodiment provides a method for predicting the life of an engine connecting rod big end bearing, referring to Figure 1 , Figure 1 This is a flow chart of the first embodiment of the engine connecting rod big end bearing life prediction method of the present application.

[0065] In this embodiment, the engine connecting rod big end bearing life prediction method includes steps S10 to S60:

[0066] Step S10, determining a physical model fatigue factor of the connecting rod big end bearing based on a fatigue analysis physical model of the engine connecting rod big end bearing, and mapping the physical model fatigue factor to an initial category label;

[0067] It should be noted that the fatigue analysis physical model of the engine connecting rod big end bearing is a calculation model used to perform fatigue analysis on the connecting rod big end bearing and evaluate the fatigue safety factor. In this embodiment, the fatigue factor of the physical model of the connecting rod big end bearing, that is, the fatigue safety factor of the connecting rod big end bearing, is used as an evaluation indicator of the connecting rod big end bearing life.

[0068] In a feasible implementation, the step of constructing a fatigue analysis physical model includes: obtaining fatigue influencing parameters of the connecting rod big end bearing; and constructing a fatigue analysis physical model based on a first corresponding relationship between the fatigue influencing parameters and the fatigue factor of the physical model.

[0069] It should be noted that fatigue influencing parameters are parameters that have an impact on fatigue analysis. Based on the preset finite element model / material properties, the fatigue influencing parameters of the connecting rod big end bearing are obtained. The fatigue influencing parameters at least include the local maximum stress, nominal stress, average stress, tensile strength of the bearing material, fatigue strength of the standard test piece, fatigue strength of the real sample, surface fatigue strength of the smooth standard test piece, surface fatigue strength of the real sample, symmetrical fatigue limit of the bearing material, and sensitivity coefficient of the bearing material to stress cycle asymmetry. During the operation of the engine, the connecting rod big end bearing is mainly subjected to the periodic alternating loads generated by the gas combustion pressure and the reciprocating inertia force, which is prone to fatigue damage. Therefore, the fatigue influencing parameters should consider multiple factors such as workload, operating conditions, lubrication method, lubrication condition, material properties, geometric structure, etc., to provide comprehensive input data for the life prediction and performance evaluation of the connecting rod big end bearing.

[0070] It should be noted that the first correspondence between fatigue influencing parameters and physical model fatigue factors is as follows:

[0071]

[0072] Where SF represents the physical model fatigue factor, σ max represents the local maximum stress, σ1 represents the nominal stress, σ S1 Represents the surface fatigue strength of the real sample, σ S2 represents the surface fatigue strength of the smooth standard test piece, S1 represents the fatigue strength of the real sample, S2 represents the fatigue strength of the standard test piece, The sensitivity coefficient of the bearing material to the asymmetric stress cycle, σ α represents the stress amplitude, σ m represents the mean stress, σ -1Indicates the symmetrical fatigue limit of the bearing material. This embodiment constructs a fatigue analysis physical model based on the above first corresponding relationship.

[0073] In addition, it should be noted that the physical model fatigue factor SF is used to characterize the fatigue influencing parameters of the connecting rod big end bearing and the influence characteristics of the bearing material properties on the bearing life. It represents the theoretical stress concentration coefficient, β represents the surface strengthening coefficient, and ε σ represents the size influence coefficient, The sensitivity coefficient of the bearing material to the asymmetric stress cycle, σ α represents the stress amplitude, σ m represents the mean stress, σ -1 Represents the symmetric fatigue limit of the bearing material.

[0074] It can be understood that the physical model fatigue factor σ is used to characterize the fatigue influencing parameters of the connecting rod big end bearing and the influence characteristics of the bearing material properties on the bearing life. Also known as the theoretical stress concentration factor, it is used to characterize the local stress concentration phenomenon caused by notches, chamfers, fillets, etc. in the structural design of parts, and its influence on the fatigue life of bearings. max is the local maximum stress, and σ1 is the nominal stress. Also known as the surface strengthening coefficient, it is used to characterize the influence of the surface roughness, surface treatment state, and surface treatment method of the connecting rod big end bearing on the fatigue strength. Generally, the worse the bearing surface condition, the lower the fatigue strength. S1 is the surface fatigue strength of the real sample, σ S2 is the surface fatigue strength of the smooth standard test piece. Due to the difference between the actual part size and the standard test piece size, size correction is required, so the ratio is set Also known as the size influence coefficient, it is used to characterize the influence of the size structure of the bearing on the fatigue strength. S1 is the fatigue strength of the real sample, and S2 is the fatigue strength of the standard test piece. The sensitivity coefficient of the bearing material to the asymmetry of the stress cycle Related to the bearing material properties and provided by the supplier. Also known as stress amplitude, it is used to characterize the characteristics of the stress amplitude of the connecting rod big end bearing. The stress amplitude can be obtained according to the preset connecting rod finite element model, or it can be estimated according to the fitting formula. There are multiple estimation models for the stress amplitude of the connecting rod big end bearing, which can be flexibly adjusted according to actual conditions. There is no specific limitation on this. In this embodiment, the estimation method using the fitting formula is adopted, and the estimation formula is: σ b is the ultimate tensile strength of the bearing material, σ m is the mean stress, σ -1 is the symmetrical fatigue limit of the bearing material. The tensile ultimate strength of the bearing material σ bRelated to the material properties, provided by the supplier, the mean stress σ m It can be obtained based on the preset connecting rod finite element model. The symmetric fatigue limit of the bearing material σ -1 It can be obtained by test measurement or estimation by fitting formula. For example, it can be calculated according to the static strength σ of the material. If it is steel material and the static strength σ<1800MPa, then σ -1 =0.5σ, if it is magnesium, copper or nickel alloy material, then σ -1 =0.35σ, if it is aluminum alloy material, then σ -1 =1.49σ 0.63 .

[0075] Since fatigue analysis is a complex process influenced by multiple factors, in practical applications, fatigue influencing parameters can be flexibly adjusted according to actual needs. The formula of fatigue factor of the physical model of connecting rod big end bearing may vary depending on the specific situation, and a more complex model may be required for accurate prediction. It should be flexibly adjusted according to actual conditions to ensure the reliability and performance optimization of the connecting rod big end bearing design.

[0076] It should be understood that the fatigue factor of the physical model obtained is mapped with the life label defined in the actual situation to obtain the initial category label, and the life label can be set according to the actual situation, for example: with the year as the basic unit, less than 1 year is one category, more than 1 year and less than 5 years is one category, 5 years to 10 years is one category, and more than 10 years is one category, and there is no specific limitation on this. The initial category label at this time is a preliminary label, and further correction is required later.

[0077] Step S20, obtaining vibration signals of the connecting rod big end bearing at different times during the entire life cycle, performing one-dimensional deep feature extraction on the segmented vibration signals, and obtaining feature vectors corresponding to the vibration signals;

[0078] It should be noted that this embodiment collects vibration signals at different times during the life cycle of the engine connecting rod big end bearing for the life prediction of the connecting rod big end bearing. The vibration signal data can be angular velocity data or angular acceleration data collected by the sensor, which can be flexibly adjusted according to actual conditions and is not specifically limited.

[0079] In addition, it should be noted that the collected vibration signals need to be processed in segments, and the length of the segments can be set according to actual needs. There is no specific limitation on this. In this embodiment, the collected vibration signals are segmented according to the length of 2560.

[0080] It can be understood that the feature vector is a vector composed of the features of the vibration signal. The segmented vibration signal is subjected to one-dimensional deep feature extraction to obtain the corresponding feature vector

[0081] In a feasible implementation, the step of performing one-dimensional deep feature extraction on the segmented vibration signal to obtain a feature vector corresponding to the vibration signal includes: performing one-dimensional deep feature extraction on the segmented vibration signal through a one-dimensional deep self-encoding feature unit to obtain a feature vector corresponding to the vibration signal.

[0082] It should be noted that the present embodiment performs one-dimensional deep feature extraction through a one-dimensional deep self-encoding feature unit. The one-dimensional deep self-encoding feature unit includes an encoding network (encoding module) and a decoding network (decoding module), the encoding network includes a convolution layer and a pooling layer, and the decoding network includes an upsampling layer and a convolution layer, wherein the number of convolution layers and pooling layers in the encoding network can be set according to actual needs, and the number of upsampling layers and convolution layers in the decoding network can be set according to actual needs, and there is no specific limitation on this.

[0083] For example, reference Figure 2 , 6 convolutional layers (Conv1~Conv6) and 4 pooling layers (Pool1~Pool4) are set in the encoding module, refer to Figure 3 , 6 convolutional layers (Conv6~Conv12) and 4 upsampling layers (Upsample1~Upsample4) are set in the decoding module.

[0084] Step S30, initializing the classification unit and the clustering unit based on the initial category label, inputting the feature vector into the classification unit and the clustering unit, determining the loss function between the initial category label and the output category label, and updating the network parameters of the classification unit and the clustering unit;

[0085] It should be noted that the classification unit and clustering unit are initialized respectively using the initial category labels. After the initialization is completed, the feature vector is input into the classification unit and clustering unit. The classification unit is the unit in the deep learning model responsible for mapping the learned features to specific categories. The clustering unit divides the feature vectors into different specific categories by learning the relationship between the feature vectors. Finally, the classification unit and clustering unit will output the corresponding category probabilities, thereby determining the category labels corresponding to the classification unit and clustering unit. Then, the loss function is calculated using the initial category labels and the category labels of the classification unit and clustering unit.

[0086] In a feasible implementation, step S30 may include steps S301 to S304:

[0087] Step S301, inputting the feature vector into the classification unit and the clustering unit, and obtaining the first category label output by the classification unit and the second category label output by the clustering unit respectively;

[0088] It should be noted that the category label includes a first category label and a second category label. The category label output by the classification unit is used as the first category label, and the category label output by the clustering unit is used as the second category label. Figure 4 , the feature vector is input into the classification unit and clustering unit respectively, and the classification unit outputs the first category label l c , the clustering unit outputs the second category label l cl .

[0089] Step S302, calculating the classification loss based on the initial category label and the first category label, and calculating the clustering loss based on the initial category label and the second category label;

[0090] It should be noted that the classification loss is the loss L corresponding to the difference between the initial category label and the first category label output by the classification unit c , the clustering loss is the loss L corresponding to the difference between the initial category label and the second category label output by the clustering unit cl .

[0091] Step S303, determining a loss function based on the classification loss and the clustering loss;

[0092] In a feasible implementation, step S303 may include: obtaining the reconstruction loss of the vibration signal in the one-dimensional deep feature extraction process; obtaining the second correspondence between the classification loss, clustering loss, reconstruction loss, balance coefficient and the loss function; based on the classification loss, clustering loss, reconstruction loss, balance coefficient and the second correspondence, determining the loss function between the initial category label and the category label.

[0093] It should be noted that the reconstruction loss is the loss calculated between the reconstructed signal and the original vibration signal. The original vibration signal is the segmented vibration signal input into the one-dimensional deep self-encoding feature unit, and the reconstructed signal is the reconstructed vibration signal. Figure 4 The original vibration signal can be considered as the input signal L1 of the first convolutional layer Conv1 of the encoding unit, and the reconstructed signal can be considered as the output signal L2 of the last convolutional layer Conv12 of the decoding unit. The loss between signals L1 and L2 is the reconstruction loss L x .

[0094] It can be understood that the second corresponding relationship between classification loss, clustering loss, reconstruction loss, balance coefficient and loss function, that is, the calculation relationship of loss function, is as follows:

[0095] L=L x +λ(L c +L cl )

[0096] In the formula, L represents the loss function, Lc represents the classification loss, L cl represents the clustering loss, L x Represents reconstruction loss, λ represents balance coefficient, and balance coefficient λ is the weight used to balance classification loss and clustering loss. In the early stage of training, due to the unstable performance of classification unit and clustering unit, λ will be assigned a lower weight, but when the classification performance of classification unit and clustering unit tends to be stable, λ will be assigned a higher weight. Substitute the classification loss, clustering loss, reconstruction loss, and balance coefficient into the second corresponding relationship above to calculate the loss function between the initial category label and the category label.

[0097] Step S304: based on the gradient information of the loss function, update the network parameters of the classification unit and the clustering unit.

[0098] It should be understood that after the loss function is obtained, the gradient of the loss function with respect to the network parameters of the classification unit and clustering unit is calculated and passed back to the network through the back propagation algorithm. An optimization algorithm (such as SGD or Adam) can be used to update the network parameters of the classification unit and clustering unit according to the gradient information.

[0099] Step S40, assigning a dynamic weight based on the divergence difference between the initial category label and the category label;

[0100] It should be noted that this embodiment uses KL divergence to calculate the difference between the category label and the initial category label, and allocates dynamic weights accordingly. The calculated difference is the divergence difference.

[0101] In a feasible implementation, the step of calculating the divergence difference may include: determining the label mean based on the category label and the initial category label; obtaining a third corresponding relationship between the initial category label, the label mean and the divergence difference; and determining the divergence difference between the initial category label and the category label based on the initial category label, the label mean and the third corresponding relationship.

[0102] It should be noted that the label mean is the mean of the category label and the initial category label, that is, the mean between the first category label, the second category label and the initial category label. The calculation relationship is as follows:

[0103]

[0104] In the formula, represents the label mean, l c represents the first category label, l cl represents the second category label, and l represents the initial category label.

[0105] It can be understood that the third corresponding relationship between the initial category label, the label mean and the divergence difference, that is, the calculation relationship of the divergence difference, is as follows:

[0106]

[0107] Where D KL (l|l) represents the divergence difference, l i represents the initial category label of the ith category, represents the label mean of the ith category. Substitute the relevant data into the ith category label and calculate the divergence difference between the category label and the initial category label, so as to assign dynamic weights according to the divergence difference.

[0108] In a feasible implementation, step S40 may include: obtaining a fourth correspondence between a divergence difference, an adjustment parameter and a dynamic weight; and determining the dynamic weight based on the divergence difference between the initial category label and the category label, the adjustment parameter and the fourth correspondence.

[0109] It should be noted that the fourth corresponding relationship between the divergence difference, the adjustment parameter and the dynamic weight, that is, the dynamic weight calculation relationship, is as follows:

[0110]

[0111] In the formula, β represents the dynamic weight, represents the divergence difference, τ represents the adjustment parameter, τ is a positive adjustment parameter that can control the change speed of the dynamic weight. When τ is large, the dynamic weight is more sensitive to the divergence difference, and when τ is small, the dynamic weight changes more slowly to the divergence difference. Substitute the divergence difference and the adjustment parameter into the fourth corresponding relationship to calculate the dynamic weight.

[0112] Step S50, based on the dynamic weight, the initial category label is corrected, the loss function and the dynamic weight are re-determined, and the iteration is continued until the termination condition is met, and the target classification unit and the target clustering unit with the target initial category label are obtained;

[0113] It can be understood that dynamic weights can reduce the impact of errors on label correction.

[0114] In a feasible implementation, the step of correcting the initial category label based on the dynamic weight includes: obtaining the fifth correspondence between the initial category label, the category label, the weight of the category label, the dynamic weight, the transformation coefficient and the corrected initial category label; based on the initial category label, the category label, the weight of the category label, the dynamic weight, the transformation coefficient and the fifth correspondence, determining the corrected initial category label.

[0115] It should be noted that the fifth corresponding relationship between the initial category label, the category label, the weight of the category label, the dynamic weight, the transformation coefficient and the revised initial category label is the calculation relationship used to revise the initial category label, as shown below;

[0116]

[0117] In the formula, l* represents the initial category label after correction, β represents the dynamic weight, l represents the initial category label before correction, and l c represents the first category label, β c represents the weight of the first category label, l cl represents the second category label, β cl represents the weight of the second category label, and w represents the transformation coefficient. c With β cl It can be set according to the actual situation, and there is no specific limitation on this. In addition, when the value of the loss function is large, the weight β is usually adjusted. c With β cl , making the output of the classification unit and clustering unit closer to the true label. The transformation coefficient w is linearly transformed from 0 to 1 during the correction process, so that the new label will not be given a high weight at the beginning, and the purpose of stable correction can be achieved.

[0118] Understandably, reference Figure 4 , using the fifth corresponding relationship mentioned above, determine the corrected initial category label l*, and update the previous initial category label l. At this time, it is necessary to determine whether the termination condition is met. The termination condition can be reaching the maximum number of iterations or meeting the accuracy requirements, which is not specifically limited. If the termination condition is met, it means that the classification unit and clustering unit at this time have been trained and can be used to predict the life of the connecting rod big end bearing. If the termination condition is not met, it means that the classification unit and clustering unit at this time have not been trained. It is necessary to continue training, redefine the loss function and dynamic weight, and re-correct the initial category label. Iterate continuously until the termination condition is met. The final initial category label is the target initial category label, the final trained classification unit is the target classification unit, and the final trained clustering unit is the target clustering unit.

[0119] It should be understood that when it is determined that the termination condition is not met, the network parameters of the classification unit and the clustering unit can be fine-tuned first, and then return to the previous step to redefine the loss function and dynamic weights, and re-correct the initial category labels.

[0120] In this embodiment, dynamic weights are determined based on KL divergence, which is a dynamic weight allocation mechanism based on prediction reliability and importance. Labels can be dynamically modified during training, so that the model has adaptive learning capabilities and can improve the accuracy and robustness of predictions. This mechanism not only enhances the generalization ability of the model for new or unknown situations, but also reduces the dependence on large-scale labeled data sets, reducing the complexity and cost of data preparation. In addition, based on the nature of unsupervised learning, the generalization performance of the model is further enhanced, enabling it to adapt to changing industrial environments, while optimizing maintenance strategies and achieving cost-effectiveness improvements.

[0121] Step S60, determining the life prediction result of the connecting rod big end bearing based on the output results of the target classification unit and the target clustering unit.

[0122] It can be understood that the target classification unit and the target clustering unit are used to perform the final life prediction and obtain the corresponding life prediction result.

[0123] It should be understood that this embodiment significantly improves the accuracy of connecting rod big end bearing life prediction by innovatively integrating deep learning models and physical analysis models. The application of deep learning models can capture subtle and complex nonlinear features in vibration signals, while the combination of physical models provides an in-depth understanding of bearing fatigue behavior. In addition, one-dimensional deep feature extraction optimizes computational efficiency and reduces dependence on computing resources while maintaining sensitivity to small changes in signals. Under comprehensive application, high prediction accuracy and rapid response capabilities under different industrial conditions are ensured.

[0124] The present embodiment provides a life prediction method for an engine connecting rod big end bearing. Based on a fatigue analysis physical model of the engine connecting rod big end bearing, a physical model fatigue factor of the connecting rod big end bearing is determined, and the physical model fatigue factor is mapped to an initial category label; vibration signals of the connecting rod big end bearing at different times in the entire life cycle are obtained, and one-dimensional deep feature extraction is performed on the segmented vibration signal to obtain a feature vector corresponding to the vibration signal; a classification unit and a clustering unit are initialized based on the initial category label, the feature vector is input into the classification unit and the clustering unit, a loss function between the initial category label and the output category label is determined, and network parameters of the classification unit and the clustering unit are updated; dynamic weights are assigned based on the divergence difference between the initial category label and the category label; based on the dynamic weight, the initial category label is corrected, the loss function and the dynamic weight are re-determined, and the iteration is continued until the termination condition is met to obtain a target classification unit and a target clustering unit with a target initial category label; based on the output results of the target classification unit and the target clustering unit, a life prediction result of the connecting rod big end bearing is determined. According to the physical analysis model, the initial life label is assigned to the collected vibration signal of the connecting rod big end bearing, and then the deep features of the vibration signal are extracted. Finally, based on the KL divergence, the life label is continuously and adaptively corrected during the model training process, and finally a more stable and accurate life prediction result is obtained. The vibration signal analysis of the deep learning model and the safety factor calculation of the physical model are combined to utilize the advantages of both to improve the accuracy and reliability of the prediction. Moreover, through multi-dimensional analysis, considering the actual operating conditions and material properties of the equipment, it can adapt to the life prediction needs under different working conditions and usage conditions, reducing the dependence on manual experience, and has good versatility and adaptability.

[0125] For example, in order to help understand the implementation process of the engine connecting rod big end bearing life prediction method obtained by combining this embodiment with the above-mentioned embodiment 1, please refer to Figure 5 , Figure 5 A brief flow chart of a method for predicting the life of an engine connecting rod big end bearing is provided, specifically:

[0126] Use the physical model life analysis unit to obtain the physical model fatigue factor of the connecting rod big end bearing; collect the vibration signals of the connecting rod big end bearing at different times during the whole life cycle, and extract the deep features of all vibration signals through the one-dimensional deep autoencoding feature unit; convert the obtained physical model fatigue factor into the corresponding bearing life label as the initial category label; initialize the classification unit and clustering unit according to the initial category label; calculate the loss function according to the initial category label and the category label output by the classification unit and clustering unit; use the KL divergence to calculate the difference between the category label and the initial category label, and assign dynamic weights accordingly to correct the initial category label; in the final stage of training, the network parameters can be fine-tuned to further optimize the classification performance; combine the dynamic weights and classifier output to obtain the final life prediction result.

[0127] It should be noted that the above examples are only used to understand the present application and do not constitute a limitation on the engine connecting rod big end bearing life prediction method of the present application. More simple transformations based on this technical concept are all within the scope of protection of the present application.

[0128] This application also provides a device for predicting the life of the engine connecting rod big end bearing. Please refer to Figure 6 , the engine connecting rod big end bearing life prediction device includes:

[0129] An analysis module 10 is used to determine a physical model fatigue factor of the connecting rod big end bearing based on a fatigue analysis physical model of the engine connecting rod big end bearing, and map the physical model fatigue factor to an initial category label;

[0130] The correction module 20 is used to obtain the vibration signals of the connecting rod big end bearing at different times during the whole life cycle, perform one-dimensional deep feature extraction on the segmented vibration signals, and obtain the feature vector corresponding to the vibration signal;

[0131] The correction module 20 is further used to initialize the classification unit and the clustering unit based on the initial category label, input the feature vector into the classification unit and the clustering unit, determine the loss function between the initial category label and the output category label, and update the network parameters of the classification unit and the clustering unit;

[0132] The correction module 20 is further used to assign dynamic weights based on the divergence difference between the initial category label and the category label;

[0133] The correction module 20 is further used to correct the initial category label based on the dynamic weight, re-determine the loss function and the dynamic weight, and continuously iterate until the termination condition is met to obtain the target classification unit and the target clustering unit with the target initial category label;

[0134] The prediction module 30 is used to determine the life prediction result of the connecting rod big end bearing based on the output results of the target classification unit and the target clustering unit.

[0135] In a feasible implementation manner, the analysis module 10 is further used to obtain fatigue influencing parameters of the connecting rod big end bearing, the fatigue influencing parameters at least including local maximum stress, nominal stress, average stress, tensile limit strength of the bearing material, fatigue strength of the standard test piece, fatigue strength of the real sample, surface fatigue strength of the smooth standard test piece, surface fatigue strength of the real sample, symmetric fatigue limit of the bearing material, and sensitivity coefficient of the bearing material to stress cycle asymmetry;

[0136] Based on the first correspondence between fatigue influencing parameters and fatigue factors of the physical model, a fatigue analysis physical model is constructed.

[0137] In a feasible implementation, the correction module 20 is also used to perform one-dimensional deep feature extraction on the segmented vibration signal through a one-dimensional deep self-encoding feature unit to obtain a feature vector corresponding to the vibration signal. The one-dimensional deep self-encoding feature unit includes an encoding network and a decoding network. The encoding network includes a convolution layer and a pooling layer, and the decoding network includes an upsampling layer and a convolution layer.

[0138] In a feasible implementation, the category label includes a first category label and a second category label, and the correction module 20 is further used to input the feature vector into the classification unit and the clustering unit to obtain the first category label output by the classification unit and the second category label output by the clustering unit respectively;

[0139] Calculate the classification loss based on the initial category label and the first category label;

[0140] Calculate the clustering loss based on the initial category label and the second category label;

[0141] Determine the loss function based on classification loss and clustering loss;

[0142] Based on the gradient information of the loss function, the network parameters of the classification unit and the clustering unit are updated.

[0143] In a feasible implementation manner, the correction module 20 is further used to obtain the reconstruction loss of the vibration signal in the one-dimensional depth feature extraction process;

[0144] Obtain the second correspondence between classification loss, clustering loss, reconstruction loss, balance coefficient and loss function;

[0145] Based on the classification loss, the clustering loss, the reconstruction loss, the balancing coefficient, and the second correspondence, a loss function between the initial category label and the category label is determined.

[0146] In a feasible implementation manner, the correction module 20 is further used to determine a label mean based on the category label and the initial category label;

[0147] Obtain the third correspondence between the initial category label, label mean and divergence difference;

[0148] Based on the initial category label, the label mean, and the third corresponding relationship, the divergence difference between the initial category label and the category label is determined.

[0149] In a feasible implementation manner, the correction module 20 is further used to obtain a fourth corresponding relationship between the divergence difference, the adjustment parameter and the dynamic weight;

[0150] The dynamic weight is determined based on the divergence difference between the initial category label and the category label, the adjustment parameter, and the fourth corresponding relationship.

[0151] In a feasible implementation manner, the correction module 20 is further used to obtain a fifth corresponding relationship between the initial category label, the category label, the weight of the category label, the dynamic weight, the transformation coefficient and the corrected initial category label;

[0152] Based on the initial category label, the category label, the weight of the category label, the dynamic weight, the transformation coefficient, and the fifth corresponding relationship, a revised initial category label is determined.

[0153] The engine connecting rod big end bearing life prediction device provided by the present application adopts the engine connecting rod big end bearing life prediction method in the above embodiment, which can solve the technical problem of low accuracy in engine connecting rod big end bearing life prediction. Compared with the prior art, the beneficial effects of the engine connecting rod big end bearing life prediction device provided by the present application are the same as the beneficial effects of the engine connecting rod big end bearing life prediction method provided by the above embodiment, and other technical features in the engine connecting rod big end bearing life prediction device are the same as the features disclosed in the above embodiment method, which will not be repeated here.

[0154] The present application provides an engine connecting rod big end bearing life prediction device, the engine connecting rod big end bearing life prediction device includes: at least one processor; and a memory connected to the at least one processor in communication; wherein the memory stores instructions that can be executed by the at least one processor, and the instructions are executed by the at least one processor so that the at least one processor can execute the engine connecting rod big end bearing life prediction method in the above-mentioned embodiment one.

[0155] Reference below Figure 5, which shows a schematic diagram of the structure of the engine connecting rod big end bearing life prediction device suitable for implementing the embodiment of the present application. The engine connecting rod big end bearing life prediction device in the embodiment of the present application may include but is not limited to mobile terminals such as mobile phones, laptop computers, digital broadcast receivers, PDAs (Personal Digital Assistants), PADs (Portable Application Descriptions), PMPs (Portable Media Players), vehicle-mounted terminals (such as vehicle-mounted navigation terminals), etc., and fixed terminals such as digital TVs, desktop computers, etc. Figure 7 The engine connecting rod big end bearing life prediction device shown is only an example and should not bring any limitation to the functions and scope of use of the embodiments of the present application.

[0156] like Figure 7 As shown, the engine connecting rod big end bearing life prediction device may include a processing device 1001 (such as a central processing unit, a graphics processor, etc.), which can perform various appropriate actions and processes according to a program stored in a read-only memory (ROM: Read Only Memory) 1002 or a program loaded from a storage device 1003 to a random access memory (RAM: Random Access Memory) 1004. In RAM1004, various programs and data required for the operation of the engine connecting rod big end bearing life prediction device are also stored. The processing device 1001, ROM1002 and RAM1004 are connected to each other through a bus 1005. An input / output (I / O) interface 1006 is also connected to the bus. Typically, the following systems can be connected to the I / O interface 1006: input devices 1007 including, for example, a touch screen, a touchpad, a keyboard, a mouse, an image sensor, a microphone, an accelerometer, a gyroscope, etc.; output devices 1008 including, for example, a liquid crystal display (LCD), a speaker, a vibrator, etc.; storage devices 1003 including, for example, a magnetic tape, a hard disk, etc.; and communication devices 1009. The communication device 1009 can allow the engine connecting rod big end bearing life prediction device to communicate wirelessly or wired with other devices to exchange data. Although the figure shows an engine connecting rod big end bearing life prediction device with various systems, it should be understood that it is not required to implement or have all the systems shown. More or fewer systems may be implemented or have alternatively.

[0157] In particular, according to the embodiments disclosed in the present application, the process described above with reference to the flowchart can be implemented as a computer software program. For example, the embodiments disclosed in the present application include a computer program product, which includes a computer program carried on a computer-readable medium, and the computer program includes a program code for executing the method shown in the flowchart. In such an embodiment, the computer program can be downloaded and installed from a network through a communication device, or installed from a storage device 1003, or installed from a ROM 1002. When the computer program is executed by the processing device 1001, the above-mentioned functions defined in the method of the embodiment disclosed in the present application are executed.

[0158] The engine connecting rod big end bearing life prediction device provided by the present application adopts the engine connecting rod big end bearing life prediction method in the above embodiment, which can solve the technical problem of low accuracy in engine connecting rod big end bearing life prediction. Compared with the prior art, the beneficial effects of the engine connecting rod big end bearing life prediction device provided by the present application are the same as the beneficial effects of the engine connecting rod big end bearing life prediction method provided by the above embodiment, and other technical features in the engine connecting rod big end bearing life prediction device are the same as the features disclosed in the previous embodiment method, which will not be repeated here.

[0159] It should be understood that the various parts disclosed in this application can be implemented by hardware, software, firmware or a combination thereof. In the description of the above embodiments, specific features, structures, materials or characteristics can be combined in any one or more embodiments or examples in a suitable manner.

[0160] The above are only specific implementations of the present application, but the protection scope of the present application is not limited thereto. Any technician familiar with the 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.

[0161] The present application provides a computer-readable storage medium having computer-readable program instructions (ie, computer programs) stored thereon, and the computer-readable program instructions are used to execute the engine connecting rod big end bearing life prediction method in the above-mentioned embodiment.

[0162] The computer-readable storage medium provided in the present application may be, for example, a USB flash drive, but is not limited to electrical, magnetic, optical, electromagnetic, infrared, or semiconductor systems, systems or devices, or any combination of the above. More specific examples of computer-readable storage media may include, but are not limited to: an electrical connection with one or more wires, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the above. In this embodiment, the computer-readable storage medium may be any tangible medium containing or storing a program that can be used by or in combination with an instruction execution system, system or device. The program code contained on the computer-readable storage medium may be transmitted using any appropriate medium, including but not limited to: wires, optical cables, RF (Radio Frequency), etc., or any suitable combination of the above.

[0163] The computer-readable storage medium may be included in the engine connecting rod big end bearing life prediction device; or may exist independently without being assembled into the engine connecting rod big end bearing life prediction device.

[0164] The computer-readable storage medium carries one or more programs. When the one or more programs are executed by the engine connecting rod big end bearing life prediction device, the engine connecting rod big end bearing life prediction device: determines the physical model fatigue factor of the connecting rod big end bearing based on the fatigue analysis physical model of the engine connecting rod big end bearing, and maps the physical model fatigue factor to an initial category label; obtains vibration signals at different times in the entire life cycle of the connecting rod big end bearing, performs one-dimensional deep feature extraction on the segmented vibration signal, and obtains a feature vector corresponding to the vibration signal; initializes the classification unit and the clustering unit based on the initial category label, inputs the feature vector into the classification unit and the clustering unit, determines the loss function between the initial category label and the output category label, and updates the network parameters of the classification unit and the clustering unit; allocates dynamic weights based on the divergence difference between the initial category label and the category label; based on the dynamic weight, corrects the initial category label, re-determines the loss function and the dynamic weight, and iterates continuously until the termination condition is met, and obtains a target classification unit and a target clustering unit with a target initial category label; determines the life prediction result of the connecting rod big end bearing based on the output results of the target classification unit and the target clustering unit.

[0165] Computer program code for performing the operations of the present application may be written in one or more programming languages ​​or a combination thereof, including object-oriented programming languages ​​such as Java, Smalltalk, C++, and conventional procedural programming languages ​​such as "C" or similar programming languages. The program code may be executed entirely on the user's computer, partially on the user's computer, as a separate software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In the case of a remote computer, the remote computer may be connected to the user's computer through any type of network, including a local area network (LAN) or a wide area network (WAN), or may be connected to an external computer (e.g., via the Internet using an Internet service provider).

[0166] The flow chart and block diagram in the accompanying drawings illustrate the possible architecture, function and operation of the system, method and computer program product according to various embodiments of the present application. In this regard, each square box in the flow chart or block diagram can represent a module, a program segment or a part of a code, and the module, the program segment or a part of the code contains one or more executable instructions for realizing the specified logical function. It should also be noted that in some alternative implementations, the functions marked in the square box can also occur in a sequence different from that marked in the accompanying drawings. For example, two square boxes represented in succession can actually be executed substantially in parallel, and they can sometimes be executed in the opposite order, depending on the functions involved. It should also be noted that each square box in the block diagram and / or flow chart, and the combination of the square boxes in the block diagram and / or flow chart can be implemented with a dedicated hardware-based system that performs a specified function or operation, or can be implemented with a combination of dedicated hardware and computer instructions.

[0167] The modules involved in the embodiments described in this application may be implemented by software or hardware, wherein the name of the module does not constitute a limitation on the unit itself in some cases.

[0168] The readable storage medium provided in the present application is a computer-readable storage medium, which stores computer-readable program instructions (i.e., computer programs) for executing the above-mentioned engine connecting rod big end bearing life prediction method, and can solve the technical problem of low accuracy in engine connecting rod big end bearing life prediction. Compared with the prior art, the beneficial effects of the computer-readable storage medium provided in the present application are the same as the beneficial effects of the engine connecting rod big end bearing life prediction method provided in the above-mentioned embodiment, and will not be repeated here.

[0169] The present application also provides a computer program product, including a computer program, which implements the steps of the above-mentioned engine connecting rod big end bearing life prediction method when the computer program is executed by a processor.

[0170] The computer program product provided by the present application can solve the technical problem of low accuracy in predicting the life of the engine connecting rod big end bearing. Compared with the prior art, the beneficial effects of the computer program product provided by the present application are the same as the beneficial effects of the engine connecting rod big end bearing life prediction method provided by the above embodiment, which will not be repeated here.

[0171] The above are only some embodiments of the present application, and are not intended to limit the patent scope of the present application. All equivalent structural changes made using the contents of the present application specification and drawings under the technical concept of the present application, or direct / indirect application in other related technical fields are included in the patent protection scope of the present application.

Claims

1. A method for predicting the life of an engine connecting rod big end bearing, characterized in that: The method comprises: Based on a fatigue analysis physical model of an engine connecting rod big end bearing, determining a physical model fatigue factor of the connecting rod big end bearing, and mapping the physical model fatigue factor to an initial category label; Acquire vibration signals of the connecting rod big end bearing at different times during the entire life cycle, perform one-dimensional deep feature extraction on the segmented vibration signals, and obtain feature vectors corresponding to the vibration signals; Initializing a classification unit and a clustering unit based on the initial category label, inputting the feature vector into the classification unit and the clustering unit, determining a loss function between the initial category label and the output category label, and updating network parameters of the classification unit and the clustering unit; assigning a dynamic weight based on a divergence difference between the initial category label and the category label; Based on the dynamic weight, the initial category label is modified, the loss function and the dynamic weight are re-determined, and the iteration is continued until the termination condition is met, so as to obtain the target classification unit and the target clustering unit with the target initial category label; Based on the output results of the target classification unit and the target clustering unit, a life prediction result of the connecting rod big end bearing is determined.

2. The method according to claim 1, characterized in that The step of determining the fatigue factor of the physical model of the connecting rod big end bearing based on the fatigue analysis physical model of the engine connecting rod big end bearing also includes: Obtain fatigue influencing parameters of the connecting rod big end bearing, wherein the fatigue influencing parameters at least include local maximum stress, nominal stress, average stress, tensile limit strength of bearing material, fatigue strength of standard test piece, fatigue strength of real sample, surface fatigue strength of smooth standard test piece, surface fatigue strength of real sample, symmetric fatigue limit of bearing material and sensitivity coefficient of bearing material to stress cycle asymmetry; The fatigue analysis physical model is constructed based on a first corresponding relationship between the fatigue influencing parameter and the fatigue factor of the physical model.

3. The method according to claim 1, characterized in that The step of performing one-dimensional deep feature extraction on the segmented vibration signal to obtain a feature vector corresponding to the vibration signal comprises: A one-dimensional deep feature extraction is performed on the segmented vibration signal through a one-dimensional deep self-encoding feature unit to obtain a feature vector corresponding to the vibration signal. The one-dimensional deep self-encoding feature unit includes an encoding network and a decoding network. The encoding network includes a convolution layer and a pooling layer, and the decoding network includes an upsampling layer and a convolution layer.

4. The method according to claim 1, characterized in that The category label includes a first category label and a second category label, and the steps of inputting the feature vector into the classification unit and the clustering unit, determining a loss function between the initial category label and the output category label, and updating network parameters of the classification unit and the clustering unit include: Inputting the feature vector into the classification unit and the clustering unit to obtain a first category label output by the classification unit and a second category label output by the clustering unit respectively; Calculating classification loss based on the initial category label and the first category label; Calculating clustering loss based on the initial category label and the second category label; Determining the loss function based on the classification loss and the clustering loss; Based on the gradient information of the loss function, the network parameters of the classification unit and the clustering unit are updated.

5. The method according to claim 4, characterized in that The step of determining the loss function based on the classification loss and the clustering loss comprises: Obtaining the reconstruction loss of the vibration signal during one-dimensional deep feature extraction; Obtain the second correspondence between classification loss, clustering loss, reconstruction loss, balance coefficient and loss function; A loss function between the initial category label and the category label is determined based on the classification loss, the clustering loss, the reconstruction loss, a balancing coefficient, and the second corresponding relationship.

6. The method according to claim 1, characterized in that Before the step of allocating a dynamic weight based on the divergence difference between the initial category label and the category label, the step further includes: Determine a label mean based on the category label and the initial category label; Obtain the third correspondence between the initial category label, label mean and divergence difference; Based on the initial category label, the label mean, and the third corresponding relationship, a divergence difference between the initial category label and the category label is determined.

7. The method according to claim 1, characterized in that The step of allocating a dynamic weight based on the divergence difference between the initial category label and the category label comprises: Obtaining a fourth correspondence between the divergence difference, the adjustment parameter, and the dynamic weight; The dynamic weight is determined based on a divergence difference between the initial category label and the category label, an adjustment parameter, and the fourth corresponding relationship.

8. The method according to claim 1, characterized in that The step of modifying the initial category label based on the dynamic weight comprises: Obtaining a fifth corresponding relationship between the initial category label, the category label, the weight of the category label, the dynamic weight, the transformation coefficient, and the corrected initial category label; A revised initial category label is determined based on the initial category label, the category label, the weight of the category label, the dynamic weight, the transformation coefficient, and the fifth corresponding relationship.

9. An engine connecting rod big end bearing life prediction device, characterized in that: The device comprises: An analysis module, for determining a physical model fatigue factor of the connecting rod big end bearing based on a fatigue analysis physical model of the engine connecting rod big end bearing, and mapping the physical model fatigue factor to an initial category label; A correction module is used to obtain vibration signals of the connecting rod big end bearing at different times during the whole life cycle, perform one-dimensional deep feature extraction on the segmented vibration signals, and obtain a feature vector corresponding to the vibration signal; The correction module is further used to initialize the classification unit and the clustering unit based on the initial category label, input the feature vector into the classification unit and the clustering unit, determine the loss function between the initial category label and the output category label, and update the network parameters of the classification unit and the clustering unit; The correction module is further used to assign a dynamic weight based on a divergence difference between the initial category label and the category label; The correction module is further used to correct the initial category label based on the dynamic weight, re-determine the loss function and the dynamic weight, and continuously iterate until the termination condition is met to obtain the target classification unit and the target clustering unit with the target initial category label; A prediction module is used to determine the life prediction result of the connecting rod big end bearing based on the output results of the target classification unit and the target clustering unit.

10. An engine connecting rod big end bearing life prediction device, characterized in that: The device comprises: a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the computer program is configured to implement the steps of the engine connecting rod big end bearing life prediction method according to any one of claims 1 to 8.

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