A spring impact test device and a spring remaining life analysis method

Abstract

The present invention discloses a spring impact test device and a spring remaining life analysis method. The spring impact test device includes a base, a top plate, a guide rod, a pressure block and a positioning plate. The top plate is located above the base. The guide rod and the positioning plate are connected between the top plate and the base. The spring test piece is sleeved on the guide rod and falls on the base. The pressure block is sleeved on the guide rod and has the freedom to move along the length of the guide rod so that the pressure block can fall freely to impact the spring test piece. The positioning plate is provided with a switch mechanism for fixing the pressure block at a set position on the guide rod. The positioning plate is provided with an anti-rebound mechanism to avoid secondary impact on the spring test piece during the pressure block falling process. The present invention is applied to the field of spring impact test analysis and can effectively avoid secondary impact on the spring during the impact test, reproduce the impact damage to the spring to the greatest extent, and improve the accuracy of the spring remaining life analysis.

Description

Technical Field

[0001] The invention relates to the technical field of spring impact test analysis, in particular to a spring impact test device and a spring remaining life analysis method. Background Art

[0002] A spring is an elastic mechanical component that deforms when subjected to an external force and returns to its original shape when the force is removed. Springs can be divided into various types, including compression springs, extension springs, and torsion springs, depending on their purpose and operating principle. Each type of spring has its own specific design and application. As an important mechanical component, springs play a vital role in various mechanical systems, and their performance and quality directly affect the operation and stability of the entire system.

[0003] In various types of buffer devices, springs will be subjected to impacts of a certain speed during operation. The quality of the spring performance has a direct impact on the performance and safety of the device. The service life and reliability of the spring are very important to ensure the stable operation of the equipment. Studying the cumulative damage of the spring is of great significance to improving the reliability of the mechanism.

[0004] Most existing spring testing devices are designed for fatigue testing of springs, but fewer are designed to study the cumulative impact damage of springs. During fatigue testing, both ends of the spring are in constant contact with the testing device. Accumulated impact damage testing requires that the spring be subjected to impact at a certain velocity, with at least one end in motion. Furthermore, during the test, the spring must be protected from secondary impacts. Therefore, it is necessary to develop a testing device that can withstand high-speed impacts on the spring while preventing secondary impacts. By simulating actual operating conditions, the impact damage experienced by the spring can be reproduced to the greatest extent possible, yielding relatively accurate test data.

[0005] Existing methods for predicting the remaining life of springs are primarily categorized into two types: physical modeling and data-driven modeling. Because most modern equipment features complex structures that are difficult to solve using physical equations, data-driven modeling has gained widespread application. Data-driven methods utilize observed data to derive a remaining life model. These models are categorized as random coefficient regression, Wiener process, Gamma process, and inverse Gaussian process. Compared to other random process models, the Wiener process is a non-monotonic degrading process with Gaussian independently distributed increments. Due to its excellent mathematical properties, it has been widely used in reliability analysis and life prediction.

[0006] In the Wiener process model, the average degradation of a spring and its progression are controlled by the drift coefficient. Existing Wiener process models are divided into deterministic drift coefficient models and variable drift coefficient models. Deterministic drift coefficient models assume that the drift coefficient remains constant throughout the degradation process, and parameter estimation can be directly solved using the maximum likelihood function. However, in the actual degradation process, due to the differences between springs, the drift coefficients of each spring are not exactly the same. Variable drift coefficient models assume that the drift coefficients follow a Gaussian distribution. Existing variable drift coefficient models assume that the drift coefficients vary only between products and do not account for the variability of drift parameters within a product at different degradation moments. Summary of the Invention

[0007] In response to the above-mentioned deficiencies in the prior art, the present invention provides a spring impact test device and a spring remaining life analysis method, which can effectively avoid secondary impact on the spring during the impact test, reproduce the impact damage to the spring to the greatest extent, and improve the accuracy of the spring remaining life analysis.

[0008] To achieve the above-mentioned object, the present invention provides a spring impact test device, comprising a base, a top plate, a guide rod, a pressure block, and a positioning plate, wherein the top plate is located above the base, the guide rod and the positioning plate are connected between the top plate and the base, and a spring specimen is sleeved on the guide rod and falls on the base;

[0009] The pressing block is sleeved on the guide rod, and has the freedom to move along the length direction of the guide rod, so that the pressing block can fall freely to impact the spring test piece;

[0010] The positioning plate is provided with a switch mechanism for fixing the pressing block at a set position on the guide rod;

[0011] The positioning plate is provided with an anti-rebound mechanism to avoid secondary impact on the spring test piece during the falling process of the pressing block.

[0012] To achieve the above object, the present invention further provides a method for analyzing the remaining life of a spring, which uses the above-mentioned spring impact test device and includes the following steps:

[0013] Step 1: Establish a degradation model consisting of an observation equation and a state equation, and establish a spring remaining life prediction model;

[0014] Step 2: Using the spring impact test device, the spring specimen is subjected to N0 impacts as one cycle, and after one cycle, the degradation amount of the spring specimen stiffness is obtained by a spring dynamometer and used as the observation amount;

[0015] Step 3: The degradation model is combined with the entire historical observation data, and the estimated value of the state quantity is obtained by using the Kalman filter. The maximum likelihood estimate of the unknown parameters in the degradation model is obtained based on the EM algorithm of the nested RTS smoothing algorithm, and the state quantity of the degradation model is updated;

[0016] Step 4: Obtain the remaining life of the spring specimen based on the state quantity of the degradation model and the remaining life prediction model.

[0017] Compared with the prior art, the present invention has the following beneficial technical effects:

[0018] 1. The spring impact test device of the present invention can use the gravity of the pressure block to perform work to realize the impact test of the spring. By changing the mass of the pressure block or adjusting the height of the pressure block, different impact speeds and impact forces can be achieved, thereby increasing the test effect of the device.

[0019] 2. The spring impact test device of the present invention is provided with an anti-rebound mechanism to avoid secondary impact on the spring test piece during the falling process of the pressure block, thereby minimizing the impact of the secondary impact and improving the detection effect of the device;

[0020] 3. In the preferred embodiment of the spring impact test device of the present invention, a guide shaft is provided to constrain the pressing block, so that the spring specimen is only subjected to a vertical downward force, which facilitates the analysis of the test results;

[0021] 4. The remaining life analysis method of the spring in the present invention takes into account the differences in drift parameters of a product at different degradation moments, combines historical observation data, uses a random walk model to update the drift parameters, obtains an estimated value of the state quantity through Kalman filtering, and uses the EM algorithm based on the nested RTS smoothing algorithm to obtain the maximum likelihood estimate of the unknown parameters in the degradation model, and updates the state quantity of the degradation model. The proposed degradation model is more in line with the actual degradation process and makes full use of the observation data, thereby effectively improving the accuracy of parameter vector estimation and improving the accuracy of the remaining life analysis of the spring. BRIEF DESCRIPTION OF THE DRAWINGS

[0022] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on the structures shown in these drawings without paying any creative work.

[0023] Figure 1 Schematic diagram of the overall structure of a spring impact test device in an embodiment of the present invention;

[0024] Figure 2An enlarged schematic diagram of a switch structure in an embodiment of the present invention;

[0025] Figure 3 is an enlarged schematic diagram of the anti-rebound mechanism in an embodiment of the present invention;

[0026] Figure 4 This is a structural diagram of a spring impact test device in a working state according to an embodiment of the present invention;

[0027] Figure 5 This is an enlarged schematic diagram of the anti-rebound mechanism in the working state according to an embodiment of the present invention;

[0028] Figure 6 Flowchart of the spring remaining life analysis method in an embodiment of the present invention.

[0029] Figure numbers: 1-base, 2-positioning plate, 3-guide shaft, 4-guide rod, 5-top plate, 6-mounting hole, 7-stop block, 8-block switch, 9-linear bearing, 10-pressure block, 11-spring test piece.

[0030] The purpose, features and advantages of the present invention will be further described with reference to the accompanying drawings and in conjunction with the embodiments. DETAILED DESCRIPTION

[0031] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. All other embodiments obtained by ordinary technicians in this field based on the embodiments of the present invention without making any creative efforts shall fall within the scope of protection of the present invention.

[0032] It should be noted that all directional indications (such as up, down, left, right, front, back, etc.) in the embodiments of the present invention are only used to explain the relative position relationship, movement status, etc. between the various components under a certain specific posture (as shown in the accompanying drawings). If the specific posture changes, the directional indication will also change accordingly.

[0033] In addition, the terms "first," "second," and so on, used in this disclosure are for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the number of the technical features being referenced. Thus, a feature specified as "first" or "second" may explicitly or implicitly include at least one such feature. In the description of this disclosure, "plurality" means at least two, such as two or three, unless otherwise specifically defined.

[0034] In the present invention, unless otherwise specified or limited, the terms "connection" and "fixation" should be understood in a broad sense. For example, "fixation" can mean fixed connection, detachable connection, or integration; it can mean mechanical connection, electrical connection, physical connection, or wireless communication connection; it can mean direct connection or indirect connection through an intermediate medium; it can mean internal communication between two elements or interaction between two elements, unless otherwise specified. For those skilled in the art, the specific meanings of the above terms in the present invention can be understood according to specific circumstances.

[0035] In addition, the technical solutions between the various embodiments of the present invention can be combined with each other, but it must be based on the fact that ordinary technicians in this field can implement it. When the combination of technical solutions is mutually contradictory or cannot be implemented, it should be deemed that such a combination of technical solutions does not exist and is not within the scope of protection required by the present invention.

[0036] Example 1

[0037] like Figure 1 The figure shows a spring impact test device disclosed in this embodiment, which mainly includes a base 1, a top plate 5, a guide rod 4, a pressure block 10 and a positioning plate 2. The top plate 5 is located directly above the base 1, and the guide rod 4 and the positioning plate 2 are connected between the top plate 5 and the base 1. The spring specimen 11 is sleeved on the guide rod 4 and falls on the base 1. The pressure block 10 is sleeved on the guide rod 4, and the pressure block 10 has the freedom to move along the length direction of the guide rod 4, so that the pressure block 10 can fall freely under the action of gravity to impact the spring specimen 11. Among them, both ends of the spring specimen 11 are tightened and ground flat, and the guide rod 4, the positioning plate 2 and the top plate 5, the base 1 are fixed by bolts to facilitate the replacement of the pressure block 10 and the spring specimen 11. There is a clearance fit between the pressure block 10 and the guide rod 4, so that the pressure block 10 can fall freely under the action of gravity, thereby impacting the spring specimen 11.

[0038] Positioning plate 2 is equipped with a switch mechanism to secure pressure block 10 at a set position on guide rod 4, allowing pressure block 10 to be dropped from the same height multiple times, ensuring the accuracy of test data. Furthermore, positioning plate 2 is equipped with an anti-rebound mechanism to prevent secondary impact on spring specimen 11 during the dropping process of pressure block 10, minimizing the impact of secondary impact and improving test results.

[0039] In this embodiment, the switch mechanism includes a block switch 8 provided on the positioning plate 2. The block switch 8 has a sliding stroke on the positioning plate 2 in the direction of sliding toward or away from the pressure block 10, and the end of the block switch 8 has a support portion that can support the bottom end of the pressure block 10. In the specific implementation process, a slide groove is provided on the positioning plate 2, and the block switch 8 is installed on the slide groove by a thick-rod half-thread bolt, so that the block switch 8 can slide in the slide groove. At the same time, there is a groove on the top of the block switch 8, and a thick-rod half-thread bolt corresponding to the groove is threadedly connected on the positioning plate 2. When the block switch 8 slides along the slide groove until the support portion is supported at the bottom of the pressure block 10, the groove and the corresponding thick-rod half-thread bolt are embedded and matched, thereby fixing the block switch 8 to prevent the pressure block 10 from falling, that is, Figure 2 Preferably, a plurality of switch mechanisms can be arranged on the positioning plate 2 at intervals along the height direction, so that the baffle can be dropped from different heights to achieve different impact speeds and impact forces, thereby increasing the test effect of the device.

[0040] It is worth noting that in practical applications, the switch mechanism is not limited to the structure shown in the figure, and other structures with the same function may also be used. For example, a horizontal through hole may be directly formed in the positioning plate 2, and the stopper switch 8 may be slidably connected to the through hole to achieve the purpose of limiting or releasing the pressure block 10.

[0041] In this embodiment, the anti-rebound mechanism includes a stop block 7, a first baffle and a second baffle provided on the positioning plate 2. The stop block 7 is rotationally connected to the positioning plate 2, the first baffle is located below the stop block 7, the second baffle is located above the stop block, and the second end of the stop block 7 is heavier than the first end. When the lower side of the stop block 7 abuts the first baffle, the upper side of the stop block 7 is separated from the second baffle, and the first end of the stop block 7 is located in the falling path of the pressure block 10. When the upper side of the stop block 7 abuts the second baffle, the lower side of the stop block 7 is separated from the first baffle, and the first end of the stop block 7 avoids the falling path of the pressure block 10. Preferably, grooves are provided on the upper and lower sides of the stop block 7 to cooperate with the first baffle and the second baffle, that is, Figure 3 shown.

[0042] During specific application, the position of the stop block 7 needs to be calibrated. The process is as follows: first, the pressure block 10 is dropped on the spring test piece 11, so that the elastic force of the spring test piece 11 is the same as the gravity of the pressure block 10. Then, the stop block 7, the first stop bar and the second stop bar are installed on the positioning plate 2, so that the lower side of the first end of the stop block 7 contacts the top of the pressure block 10, and the lower side of the stop block 7 abuts the first stop bar. At this point, the position calibration of the stop block 7 is completed. During the falling process of the pressure block 10, the stop block 7 rotates on the positioning plate 2 under the impact of the pressure block 10, so that the lower side of the stop block 7 is separated from the first stop bar and the upper side of the stop block 7 abuts the second stop bar. At the same time, the first end of the stop block 7 avoids the falling direction of the pressure block 10, so that the pressure block 10 smoothly impacts the spring test piece 11. Due to the falling impact force, the spring specimen 11 is compressed to a length shorter than the calibrated length. When the spring specimen 11 is compressed to the shortest length, it begins to rebound. The second end of the stop block 7 is heavier than the first end, so that the stop block 7 can be quickly reset, thereby reducing the impact of the secondary impact. Figure 4 、 Figure 5 shown.

[0043] In a specific implementation, two rows of mounting holes 6 can be installed on the positioning plate 2. The stop block 7 can be rotatably connected to the inner mounting hole 6 via a thick-shank, half-thread bolt. At the same time, a thick-shank, half-thread bolt is provided in the mounting hole 6 above or below the stop block 7, respectively, to serve as the first and second stop bars. By varying the heights of the mounting holes 6 for the stop block 7, the first and second stop bars, the height of the anti-rebound mechanism can be adjusted, thereby adapting to different types of springs and pressure blocks 10.

[0044] As a preferred embodiment, the upper side of the first end of the stop block 7 is a rounded structure to reduce the blocking effect of the stop block 7 on the pressing block 10 falling.

[0045] As a preferred embodiment, there are two positioning plates 2, and the two positioning plates 2 are symmetrically located on both sides of the guide rod 4. At the same time, the two positioning plates 2 are also provided with symmetrical switching mechanisms and anti-rebound mechanisms, thereby improving the falling release and anti-rebound stability of the pressure block 10.

[0046] As a preferred embodiment, the spring impact test device also includes two guide shafts 3, which are connected between the top plate 5 and the base 1. The two guide shafts 3 are symmetrically located on both sides of the guide rod 4, and the pressure block 10 and the guide shaft 3 are slidably fitted through linear bearings 9 to constrain the pressure block 10 so that the spring specimen 11 is only subjected to a vertical downward force, which facilitates the analysis of the test results.

[0047] The working principle and working process of the spring impact test device in this embodiment are described below.

[0048] When in use, the original length of the spring specimen 11 is known , stiffness coefficient and work schedule , the height of the pressing block 10 , if the impact velocity of spring specimen 11 is required to be , according to the uniformly accelerated linear motion formula:

[0049] ;

[0050] in, represents the acceleration due to gravity, is the falling time of the briquette 10;

[0051] According to the formula of free fall motion:

[0052] ;

[0053] in, is the distance between the position of the stop switch 8 and the upper surface of the spring test piece 11;

[0054] From the law of conservation of energy we can get:

[0055] ;

[0056] in, is the total mass of the linear bearing 9 and the pressure block 10;

[0057] From the two-force balance formula we get:

[0058] ;

[0059] The distance between the stop block 7 and the base 1 is .

[0060] After the above parameters are set, the test is carried out. Place the pressure block 10 at the stop block switch 8, pull the stop block switch 8, and the pressure block 10 falls downward under the action of gravity. During the descent, it passes through the stop block 7. Under the action of the pressure block 10, the upper groove of the stop block 7 contacts the thick rod half-thread bolt. After the pressure block 10 is released, the stop block 7 returns to its original state. Then the pressure block 10 moves at a speed of Impact spring specimen 11, spring specimen 11 is compressed to At this position, the pressing block 10 moves upward under the action of the elastic force, and is stopped at the equilibrium position by the stop block 7. At this time, the secondary impact of the spring is minimal.

[0061] Example 2

[0062] Based on the spring impact test device in Example 1, this embodiment discloses a method for analyzing the remaining life of a spring, which analyzes and predicts the remaining life of a spring specimen by measuring the test time of the spring impact test device. Figure 6 The spring remaining life analysis method in this embodiment specifically includes the following steps.

[0063] Step 1: Establish a degradation model consisting of an observation equation and a state equation, and establish a spring remaining life prediction model;

[0064] Step 2: Using a spring impact test device, the spring specimen is impacted 50 times as one cycle. After one cycle, the degradation of the spring specimen stiffness is obtained by a spring dynamometer and used as an observation value;

[0065] Step 3: The degradation model is combined with the entire historical observation data, and the estimated value of the state quantity is obtained by using the Kalman filter. The maximum likelihood estimate of the unknown parameters in the degradation model is obtained by the EM algorithm based on the nested RTS smoothing algorithm, and the state quantity of the degradation model is updated;

[0066] Step 4: Based on the state quantity of the degradation model and the remaining life prediction model, the remaining life of the spring specimen is obtained.

[0067] In the specific implementation process, the construction process of the degradation model is as follows:

[0068] First, the degradation equation is established with a random process, using the Wiener process, which is:

[0069] (1)

[0070] in, is the degradation amount, is the drift parameter, is the diffusion coefficient, For Brownian motion Moment and The difference in time;

[0071] In order to combine the observed historical data, the random walk model To update the drift parameters ,in The mean is 0 and the variance is Q of The noise, spring degradation trend is determined by the drift parameter Determines the diffusion coefficient The noise that affects it can be considered as a constant;

[0072] Therefore, the degradation equation can be reconstructed through the linear state space model as:

[0073] (2)

[0074] (3)

[0075] Among them, formula (2) is the system equation, formula (3) is the observation equation, 、 For the i sequence i -The state quantity after 1 cycle (i.e. drift parameter), 、 For the i sequence i -The observed value after 1 cycle, 、 For the i sequence i -The time after 1 cycle, For Brownian motion Moment and The difference in time; , , , which can be obtained from the properties of Brownian motion .

[0076] Assuming the initial drift parameters The mean is , the variance is Normal distribution, then the drift parameter is considered to be a hidden "state" and can be calculated according to The observed value is estimated and expressed as degraded data (i.e. from 0 to Therefore, the model establishes the connection between drift parameters and historical observation data. In formula (1), Can be updated by recursive filters. Definition Represented as based on Estimated , Variance .

[0077] To calculate and , need to know the known hour, The probability density function of Recursive estimation ,for:

[0078] (4)

[0079] in, for When known, The probability density function of for When known, The probability density function of for When known, The probability density function of for and When known, The joint probability density function of for When known, The probability density function of for When known, The probability density function of

[0080] From formula (2) and formula (3), we can see that in formula (4) The mean is , the variance is The Gaussian distribution of can be calculated by Kalman filtering. Use the entire historical observation data to recursively update This is the advantage of the state space. The process of Kalman filtering is:

[0081] Step 1), Initialization 、 ;

[0082] Step 2), State estimation at time:

[0083] (5)

[0084] (6)

[0085] (7)

[0086] Step 3), variance update:

[0087] (8)

[0088] in, Based on Estimated , is the Kalman filter gain.

[0089] In the specific implementation process, the construction process of the spring remaining life prediction model is as follows:

[0090] When the degradation The stiffness degradation threshold is reached for the first time When the system is considered to have reached the end of its service life, this embodiment will RUL at time Defined as:

[0091] (9)

[0092] in, for The remaining life at time for The time difference between the moment-to-moment degradation amount reaching the threshold;

[0093] From formula (1), we can get At the moment The probability density function of and distribution function ,for:

[0094] (10)

[0095] (11)

[0096] in, for The cumulative function value of the normal distribution is for The cumulative function value of the normal distribution;

[0097] In formula (10), replace , however, in formula (2), the drift parameter is a random variable, so using To deduce The remaining life prediction model of the spring can be obtained by the distribution of

[0098] (12)

[0099] in, is the mean, From 1 to The historical observation of the moment, for The observed quantity at time, is the integration variable, For about Dawson points.

[0100] According to formula (12), to solve the remaining life of the spring based on the remaining life prediction model, it is necessary to first calculate and According to the aforementioned Kalman filtering process, we can calculate and The process has unknown parameters 、 、 Q 、 In this embodiment, let the parameter vector , and estimate the parameter vector based on the EM algorithm of the nested RTS smoothing algorithm .

[0101] As a parameter vector, once a new degenerate observation is generated , which can be estimated by maximum likelihood estimation , when the degradation data is hour, The maximum likelihood estimate of can be written as:

[0102] (13)

[0103] in, is the maximum likelihood estimate, For degraded data The joint probability density of

[0104] definition For Under the conditions The maximum likelihood estimate of is:

[0105] (14)

[0106] because It is a hidden variable, so the maximum likelihood estimation cannot be used directly for parameter estimation. Therefore, a more effective method needs to be found to deal with this problem. The idea of ​​the EM algorithm is to replace the hidden variable with the expectation of the observed data. Then the parameter estimation can be expressed as maximizing the joint likelihood function, which is:

[0107] (15)

[0108] in, Expressed as Known The log-log probability density of , Expressed as a set of drift coefficients;

[0109] Then, take the expectation operator on both sides of equation (15), and for ,have:

[0110] (16)

[0111] in, Expressed as The mean of Expressed as The mean of

[0112] Finally, through formula (16), we can get:

[0113] (17)

[0114] in, For the previous moment , for The maximum likelihood estimate of , for The mean of for The mean of

[0115] In formula (17), the positivity of the last term is given by and The KL divergence between is implicit. Obviously, if ,So Established. This can be achieved through the EM algorithm , and it is estimated that It can satisfy formula (14). Therefore, the EM algorithm The estimation is divided into two steps:

[0116] Step E: Calculation ,in, For Under the conditions of Step parameter estimation;

[0117] Step M: Calculation ;

[0118] Iterate the E-step and M-step until the convergence criterion is met. The convergence criterion can be set to reach a maximum number of iterations.

[0119] In the calculation process of the EM algorithm, the joint log-likelihood function can be expressed as:

[0120] (18)

[0121] in, for When known Probability, for When known Probability, for Known The probability of for Known probability;

[0122] From formula (2) and formula (3), we can see that , , , substituting into equation (18), we get:

[0123] (19)

[0124] To calculate , formula (19) can be written as:

[0125] (20)

[0126] Obviously, in order to calculate the expectation of formula (20), we need to get the following information based on the given historical observation data: Expectations , , In this embodiment, the RTS smoothing algorithm is used to obtain , , The best estimate includes:

[0127] Step 1: forward iteration of Kalman filter;

[0128] Step 2, backward iteration:

[0129] (twenty one)

[0130] (twenty two)

[0131] (twenty three)

[0132] in, is the variance gain, for The variance of the moment, Based on Estimated , Based on The obtained smoothing filter drift coefficient is for The drift parameter estimate at time , Based on Obtained The moment smoothing filter variance, Based on Obtained Step 3 of moment smoothing filter variance: initialization :

[0133] (twenty four)

[0134] in, Expressed as and The covariance between

[0135] Step 4: Backward iterative smoothing of the covariance:

[0136] (25)

[0137] in, is the smoothed covariance;

[0138] at last:

[0139] (26)

[0140] (27)

[0141] (28)

[0142] in, for The mean of for The mean of for The mean of

[0143] based on 、 、 The best estimate of Then, proceed to the Mth step of the EM algorithm. Estimated parameters in step The result can be expressed as:

[0144] (29)

[0145] (30)

[0146] (31)

[0147] (32)

[0148] in, Expressed as The mean of Represents the initial value of the drift parameter, Expressed as The mean of

[0149] Iterate the E and M steps of the EM algorithm until the convergence criteria are met, that is, the estimation of the parameter vector is completed;

[0150] Finally, the parameter estimation results obtained by the EM algorithm are substituted into formula (12) to predict the remaining service life of the spring after the current cycle. At the same time, the probability density function of the reliability of the predicted remaining service life can be calculated by formula (10).

[0151] The above description is only a preferred embodiment of the present invention and does not limit the patent scope of the present invention. All equivalent structural transformations made by using the contents of the present invention description and drawings under the inventive concept of the present invention, or direct / indirect application in other related technical fields are included in the patent protection scope of the present invention.

Claims

1. A spring impact test device, characterized in that: It includes a base, a top plate, a guide rod, a pressure block and a positioning plate. The top plate is located above the base. The guide rod and the positioning plate are connected between the top plate and the base. The spring test piece is sleeved on the guide rod and falls on the base. The pressing block is sleeved on the guide rod, and has the freedom to move along the length direction of the guide rod, so that the pressing block can fall freely to impact the spring test piece; The positioning plate is provided with a switch mechanism for fixing the pressing block at a set position on the guide rod; The positioning plate is provided with an anti-rebound mechanism to avoid secondary impact on the spring test piece during the falling process of the pressing block; The anti-rebound mechanism includes a stop block, a first stop rod and a second stop rod provided on the positioning plate; The stop block is rotatably connected to the positioning plate, the first stop rod is located below the stop block, and the second stop rod is located above the stop block; When the lower side of the stop block abuts the first baffle, the upper side of the stop block is separated from the second baffle, and the first end of the stop block is located in the falling path of the pressure block; when the upper side of the stop block abuts the second baffle, the lower side of the stop block is separated from the first baffle, and the first end of the stop block avoids the falling path of the pressure block.

2. The spring impact test device according to claim 1, characterized in that: The switch mechanism includes a block switch provided on the positioning plate; The stopper switch has a travel on the positioning plate for sliding close to or away from the pressing block, and an end portion of the stopper switch has a supporting portion for supporting the bottom end of the pressing block.

3. The spring impact test device according to claim 1, characterized in that: The upper side of the first end of the stop block is a rounded structure.

4. The spring impact test device according to any one of claims 1 to 3, characterized in that: There are two positioning plates, and the two positioning plates are symmetrically located on both sides of the guide rod.

5. The spring impact test device according to any one of claims 1 to 3, characterized in that: Also included are two guide shafts; The guide shaft is connected between the top plate and the base, the two guide shafts are symmetrically located on both sides of the guide rod, and the pressure block is slidably matched with the guide shaft.

6. A method for analyzing the remaining life of a spring, characterized in that: The spring impact test device according to any one of claims 1 to 5 comprises the following steps: Step 1: Establish a degradation model consisting of an observation equation and a state equation, and establish a spring remaining life prediction model; Step 2: Using the spring impact test device, the spring specimen is subjected to N0 impacts as one cycle, and after one cycle, the degradation amount of the spring specimen stiffness is obtained by a spring dynamometer and used as the observation amount; Step 3: The degradation model is combined with the entire historical observation data, and the estimated value of the state quantity is obtained by using Kalman filtering. The maximum likelihood estimate of the unknown parameters in the degradation model is obtained based on the EM algorithm of the nested RTS smoothing algorithm, and the state quantity of the degradation model is updated; Step 4: Obtain the remaining life of the spring specimen based on the state quantity of the degradation model and the remaining life prediction model.

7. The method for analyzing the remaining life of a spring according to claim 6, wherein: In step 1, the degradation model is: in, 、 For the i sequence i -The state quantity after 1 cycle, Obey the mean , the variance is The normal distribution of for noise, 、 For the i sequence i -The observed value after 1 cycle, 、 For the i sequence i -The time after 1 cycle, is the diffusion coefficient, For Brownian motion Moment and The difference in time; The remaining life prediction model is: in, is the mean, is the remaining life, From 0 to The historical observation of the moment, is the stiffness degradation threshold, From 1 to The historical observation of the moment, for The observed quantity at time, Based on Estimated The variance of Based on Estimated , is the integration variable, For about Dawson points.

8. The method for analyzing the remaining life of a spring according to claim 7, wherein: In step 3, the Kalman filtering process is: initialization 、 ; State estimation at time: 、 、 ,in, Based on Estimated , for , is the Kalman filter gain; Variance update: .

9. The method for analyzing the remaining life of a spring according to claim 8, characterized in that: In step 3, the EM algorithm based on the nested RTS smoothing algorithm obtains the maximum likelihood estimate of the unknown parameters in the degradation model, specifically: Define the parameter vector , and define For Under the conditions The maximum likelihood estimate of ; In the E step of the EM algorithm: calculate ,in, For Under the conditions of The parameter estimation of the step includes: Will Written as: in, for Known The logarithmic probability density of is the set of drift coefficients; Use RTS smoothing algorithm to get Expectations 、 、 The best estimate of is: Forward iteration of Kalman filter; Backward iteration: 、 、 ,in, is the variance gain, for The variance of the moment, Based on Estimated , Based on The obtained smoothing filter drift coefficient is for The drift parameter estimate at time , Based on Obtained The moment smoothing filter variance, Based on Obtained Time smoothing filter variance; initialization : ,in, for and The covariance between Backward iterative smoothing of the covariance: ,in, for and The covariance between is the smoothed covariance; in, for The mean of for The mean of for The mean of based on 、 、 The best estimate of Then, the M-step of the EM algorithm is: in, , , ; Iterate the E-step and M-step of the EM algorithm until the convergence criterion is met, that is, the estimation of the parameter vector is completed.

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