Method for analyzing reliability of cutting pick under random load impact

Abstract

The invention provides a method for analyzing the reliability of a cutting pick under random load impact, which is characterized in that coal rock is regarded as a normal working load, gangue and a fault are regarded as random impact loads, and the cutting pick is modeled as a competitive failure system in which soft failure caused by wear degradation and hard failure caused by random load impact compete with each other. Meanwhile, the influence of impact duration, variable-rate accelerated degradation and hard failure threshold value change on cutting tooth failure is comprehensively considered, a cutting tooth competitive failure reliability model under random load impact is constructed, sensitivity analysis is conducted on parameters in the constructed reliability model, and therefore the cutting tooth reliability is evaluated more accurately, and the reliability of the cutting tooth is improved. According to the method, weak links of the design are accurately pointed out, effective guidance is provided for design optimization and reliability improvement of the cutting pick, and a theoretical basis and a scientific basis are provided for reliability evaluation, maintenance and spare part management of a cutting pick system.

Description

Technical Field

[0001] The invention relates to the technical field of data analysis of roadheader picks, and in particular to a reliability analysis method of picks under random load impact. Background Art

[0002] Reliability refers to the ability of a product to complete a specified function within a specified time and under specified conditions, and describes the evolution of quality on a time scale. As the rock-breaking mechanism of a cantilevered roadheader, the pick system is in direct contact with coal and rock. The working conditions are extremely harsh and complex and changeable. It is in a high-stress, strong-impact state for a long time, and is subjected to complex alternating impact loads, making it very easy to fail. During the cutting process, the pick system rotates with the cutting motor, cutting coal and rock under the action of extremely strong thrust. The pick and coal and rock produce violent collisions and frictions, the temperature rises rapidly, the load increases rapidly, the wear increases rapidly, and the consumption is huge. During the operation of the roadheader, the downtime caused by the failure of the pick system accounts for 20% of the total operating time, and the economic losses caused account for 30% of the total economic losses. It can be seen that the reliability of the pick system plays a vital role in the safe, efficient and economical operation of the roadheader.

[0003] Although the current research on the reliability of the pick system has extensively studied the correlation between the pick degradation process and the impact strength, the pick degradation and hard failure threshold changes under the continuous impact of random loads caused by gangue and faults, which are the most common load impacts, have not been considered. Therefore, it is necessary to construct a technical solution that can simultaneously consider the effects of continuous impact, variable rate accelerated degradation, and hard failure threshold changes on pick failure, in order to address the problem of the operating reliability of the pick system under complex working conditions. Summary of the invention

[0004] In order to solve the above technical problem or at least partially solve the above technical problem, the present disclosure provides a reliability analysis method of a pick under random load impact.

[0005] According to a first aspect, a reliability analysis method for a pick under random load impact is provided, characterized in that the method comprises:

[0006] S1: Define the competing failure modes of the pick, which are the competition between soft failure caused by wear degradation and hard failure caused by random load impact;

[0007] S2: Construct an impact degradation model of the pick under competing failure modes, including modeling the natural wear degradation of the pick, random load impact, and degradation under random load impact;

[0008] S3: Based on the degradation modeling, a reliability model of the pick under the competitive failure mode is constructed, and a sensitivity analysis is performed on the parameters in the constructed reliability model, including but not limited to shape parameters and scale parameters corresponding to the objective attributes of pick wear, distribution parameters corresponding to the distribution of gangue in the coal seam, the first linear relationship constant and exponential relationship constant corresponding to the relationship between the pick and the gangue and fault, and the second linear relationship constant corresponding to the rate of decrease of the hard failure threshold.

[0009] Preferably, in step S2, the natural wear degradation of the pick is modeled, including defining a probability density function of the natural degradation rate.

[0010] Preferably, in step S2, the random load impact of the pick is modeled, including defining a probability density function of the number of impact arrivals per unit time, a probability density function of the impact amplitude, and a probability density function of the impact duration.

[0011] Preferably, in step S2, modeling the degradation of the pick under random load impact comprises the following steps:

[0012] Obtain the natural wear degradation amount, instantaneous degradation amount, accelerated degradation amount and hard failure threshold under the influence of degradation of the cutting pick to be tested during the impact process;

[0013] Based on the natural wear degradation amount, instantaneous degradation amount, accelerated degradation amount and hard failure threshold under the influence of degradation, the probability density function of the natural wear degradation amount, the probability density function of the instantaneous degradation amount, the probability density function of the accelerated degradation amount and the probability density function of the total degradation amount of the pick and the pick height are defined.

[0014] Preferably, the total wear degradation amount is obtained based on the natural wear degradation amount, instantaneous degradation amount, and accelerated degradation amount; the pick hard failure threshold is obtained based on the total wear degradation amount and the initial hard failure threshold of the pick; and the pick height is obtained based on the total wear degradation amount and the initial height of the pick.

[0015] Preferably, the hard failure threshold is inversely correlated with the total wear degradation amount, and the hard failure threshold is:

[0016]

[0017] Among them, V h (t) is the hard failure threshold, X(t) is the total degradation of pick wear, and ρ is the second linear relationship constant.

[0018] Preferably, the reliability modeling of the pick under hard failure includes:

[0019] Calculating the difference between the impact amplitude and the hard failure threshold, and obtaining a probability density function of the difference between the impact amplitude and the hard failure threshold;

[0020] Based on the above probability density function, the reliability function of the pick reliability model under hard failure is obtained;

[0021] The reliability function is:

[0022]

[0023] Where N(t) represents the number of times the shock arrives before time t, is the probability density function of the difference between the impact amplitude and the hard failure threshold.

[0024] Preferably, the reliability modeling of the pick under soft failure includes:

[0025] Determine whether the cutter to be tested is subjected to random impact, and when it is not subjected to random impact, obtain the height change of the cutter to be tested under natural degradation, and construct a first soft failure reliability function based on the height change probability density function corresponding to the height change under natural degradation;

[0026] When subjected to random impact and the number of random impacts reaches a preset number, the height change of the tested pick under natural degradation, instantaneous degradation due to impact, and variable rate degradation is obtained, and a second soft failure reliability function is constructed based on the height change probability density function corresponding to the height change.

[0027] Preferably, the reliability modeling of the pick under hard failure includes:

[0028] Obtaining a probability density function of the difference between the impact amplitude and the hard failure threshold;

[0029] Based on the above probability density function, the reliability function of the pick reliability model under hard failure is obtained.

[0030] Preferably, based on the reliability functions of the pick reliability model under two soft failure conditions and the reliability function of the pick reliability model under hard failure, a reliability function of the pick competition failure is obtained.

[0031] Preferably, a sensitivity analysis is performed on each parameter in the constructed reliability model, specifically including:

[0032] Obtain the parameter data of the reliability model, lower the values ​​of each parameter contained in the parameter data until they are adjusted to the preset minimum value of the corresponding parameter, save the reliability change of the reliability model during the value reduction operation, and perform sensitivity analysis based on the reliability change.

[0033] Compared with the prior art, the technical solution provided by the embodiments of the present disclosure has the following advantages:

[0034] The present invention takes into account the influence of random load impact in the reliability modeling of picks, regards coal and rock as normal working loads, and gangue and faults as random impact loads, and models the picks as a competitive failure system in which soft failures caused by wear degradation and hard failures caused by random load impacts compete with each other. The impact of impact duration, variable rate accelerated degradation, and hard failure threshold changes on pick failure are comprehensively considered, and a reliability model of pick competitive failure under random load impact is constructed. The sensitivity analysis of each parameter in the constructed reliability model is performed to accurately evaluate the reliability of the picks, accurately point out the weak links in the design, and provide effective guidance for the optimization of pick design and reliability improvement. At the same time, it provides a theoretical basis and scientific basis for the reliability evaluation, maintenance and spare parts management of the pick system. BRIEF DESCRIPTION OF THE DRAWINGS

[0035] In order to more clearly illustrate the specific implementation methods of the present invention or the technical solutions in the prior art, the drawings required for use in the specific implementation methods or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are some implementation methods of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0036] Figure 1 is a schematic flow chart of a method for analyzing reliability of a pick under random load impact according to an embodiment of the present invention;

[0037] Figure 2 is a schematic diagram of competitive failure of picks according to an embodiment of the present invention;

[0038] Figure 3.1 is a schematic diagram of the change in pick height during the pick degradation process in a competitive failure mode according to an embodiment of the present invention;

[0039] Figure 3.2 is a schematic diagram of the relationship between the impact amplitude and the hard failure threshold value during the pick degradation process in the competitive failure mode according to an embodiment of the present invention;

[0040] Figure 4 is a pick reliability curve diagram of a verification experiment provided by the present invention;

[0041] Figure 5 is a schematic diagram of sensitivity analysis of pick reliability under different α values ​​in the verification experiment provided by the present invention;

[0042] Figure 6 is a schematic diagram of sensitivity analysis of pick reliability under different β values ​​in the verification experiment provided by the present invention;

[0043] Figure 7 is a schematic diagram of sensitivity analysis of pick reliability under different λ0 values ​​in the verification experiment provided by the present invention;

[0044] Figure 8 is a schematic diagram of sensitivity analysis of pick reliability under different λ1 values ​​in the verification experiment provided by the present invention;

[0045] Fig. 9 According to the verification experiment provided by the present invention, Schematic diagram of sensitivity analysis of pick reliability under 2000 values;

[0046] Fig.10 is a schematic diagram of sensitivity analysis of pick reliability under different θ values ​​in the verification experiment provided by the present invention;

[0047] Fig.11 is a schematic diagram of sensitivity analysis of pick reliability under different ρ values ​​according to the verification experiment provided by the present invention;

[0048] Fig.12 It is a schematic diagram of the hardware structure of a computer device according to an embodiment of the present invention. DETAILED DESCRIPTION

[0049] In conjunction with the drawings in the embodiments of the present invention, the technical solutions in the embodiments of the present invention are clearly and completely described. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other implementation methods obtained by ordinary technicians in this field without making creative work are within the scope of protection of the present invention.

[0050] It should be noted that the structures, proportions, sizes, etc. illustrated in the drawings of this specification are only used to match the contents disclosed in the specification so that people familiar with this technology can understand and read them. They are not used to limit the conditions under which the present invention can be implemented, and therefore have no substantive technical significance. Any structural modification, change in proportional relationship or adjustment of size should fall within the scope of the technical contents disclosed in the present invention without affecting the effects and purposes that can be achieved by the present invention. It should be noted that in this specification, relational terms such as first and second are only used to distinguish one entity from several other entities, and do not necessarily require or imply any actual relationship or order between these entities.

[0051] The working conditions of the pick are extremely harsh and complex. The hardness of the coal and rock is different under different working conditions, the loads borne by the pick are different, and the wear rate is different. During the cutting process, the pick wedges into the coal body at a certain angle, firstly generating an extrusion effect with the coal and rock to form a compaction area. With the expansion of the compaction area and the accumulation of energy, the coal and rock gradually form cracks and collapse. In this case, the pick interferes with the coal and rock that have not been cut off, causing the pick and the coal and rock to squeeze and rub against each other. Usually, the hardness of coal rock is low, and the hardness of its surface micro-protrusion particles is lower than the material hardness of the pick. In this case, the pick is constantly subjected to the reaction of coal rock, and natural wear and degradation occurs. At the same time, coal rock is also mixed with high-hardness gangue and faults, and the hardness of its surface micro-protrusion particles is higher than the material hardness of the pick. In this case, the pick is constantly subjected to impact cutting, and accelerated wear and degradation occurs. Moreover, due to the difference in volume and hardness of gangue and fault, the accelerated wear presents different durations and wear rates. When the hardness of gangue and fault is higher than a certain level, the pick will break, break the edge, and other instantaneous fatal impact failures. Therefore, the combined effect of wear and impact is the main factor leading to pick failure.

[0052] Therefore, coal rock is regarded as normal working load, and gangue and fault are regarded as impact load. Since the distribution, hardness and volume of gangue and fault are all random, they are also called random impact load. The cumulative effect of natural wear degradation under normal working load and accelerated wear degradation under random impact load will cause the pick to have wear soft failure, which accounts for 75%-90% of the total failure forms. When the random impact load is too large, it will cause the pick to have impact hard failure, which accounts for 10%-25% of the total failure forms. The failure of the pick is manifested as the competition result of these two types of failure forms.

[0053] In the process of cutting coal and rock with picks, gangue and faults are the most common load impacts, which not only have random distribution and hardness, but also have random volume. Therefore, the time required to cut gangue and faults can be defined as the impact duration. At the same time, since the hardness of gangue and faults is higher than that of coal and rock, the picks will degrade at a variable rate at different impact durations. Random load impact will not only cause the picks to degrade suddenly at the moment of its arrival, but also cause the picks to degrade at a variable rate at different impact durations. At the same time, due to the existence of the impact duration, the natural degradation time is correspondingly reduced.

[0054] In addition, in order to take into account the hardness and impact toughness of the pick, the hardness of the pick material is different at different heights. Usually, the hardness of the top material is the greatest, and as the height decreases, the hardness of the material gradually decreases. In the process of cutting coal and rock, as the degree of wear increases, the height of the pick gradually decreases, and the hardness of the material in contact with the coal and rock also gradually decreases. The hard failure threshold essentially represents the impact strength that the pick material can withstand. Therefore, the hard failure threshold of the pick will decrease with the increase in wear, and the pick's ability to resist fatal impacts will gradually weaken, and it will be more sensitive to hard failure. In summary, the above situations are all in line with the actual application of picks. If these processes are simplified, it will lead to inaccurate reliability assessment of the pick.

[0055] In early studies, in order to simplify the reliability model of competing failure systems, it is usually assumed that the natural degradation process and the random impact process are independent of each other, which will lead to inaccurate reliability assessment of competing failure systems. Although some research has been conducted on the reliability modeling of competing failure systems at this stage, no study has considered the impact of impact duration and variable rate accelerated degradation on system failure. Gangue and faults, as the most common load impacts, not only have random distribution and hardness, but also have random volume. Therefore, the random load impact caused by gangue and faults will last for a certain period of time and produce different acceleration effects on the wear of the pick under different hardness. Therefore, the present invention comprehensively considers the effects of continuous impact, variable rate accelerated degradation and changes in hard failure threshold on pick failure, and establishes a reliability model under the competition of soft failure and hard failure, so as to comprehensively analyze the reliability of picks under random load impact.

[0056] According to an embodiment of the present invention, a method for analyzing the reliability of a pick under random load impact is provided. It should be noted that the steps shown in the flowchart in the accompanying drawings can be executed in a computer system such as a set of computer executable instructions, and although a logical order is shown in the flowchart, in some cases, the steps shown or described can be executed in an order different from that shown here.

[0057] In this embodiment, a reliability analysis method for a pick under random load impact is provided, which can be used in mobile terminals such as mobile phones, tablet computers, etc. Figure 1 is a flow chart of a method for analyzing reliability of a pick under random load impact according to an embodiment of the present invention. Figure 1 As shown, the process includes the following steps:

[0058] Step S1, defining a competing failure mode of a pick, wherein the competing failure mode is a competition between a soft failure caused by wear degradation and a hard failure caused by random load impact.

[0059] Optionally, during the degradation process affected by random impact, soft failure occurs when the pick height is lower than a soft failure threshold; during the random impact process, hard failure occurs when the impact amplitude exceeds a hard failure threshold.

[0060] In this embodiment, the basic principle of the pick competition failure is as follows, based on the consideration of the degradation process, the impact process, and the correlation between degradation and impact. Figure 2 As shown. On the one hand, under the influence of random impact, the impact will cause a sudden increase in the amount of degradation at the moment of its arrival, and will also cause accelerated degradation at different impact durations. At the same time, due to the introduction of the impact duration, the natural degradation time is correspondingly shortened. Therefore, the degradation process under the influence of random impact includes long-term continuous natural wear degradation, instantaneous degradation caused by impact, and accelerated degradation at different impact durations. Soft failure occurs when the pick height is lower than the soft failure threshold. On the other hand, during the impact process, as the degradation amount gradually increases, the hard failure threshold gradually decreases, and hard failure occurs when the impact amplitude exceeds the hard failure threshold.

[0061] In this embodiment, the degradation process of the pick in the competition failure mode is as follows: Figure 3.1 and Figure 3.2 As shown. Where t represents the working time of the pick, Y(t) represents the height of the pick at time t, H s represents the soft failure threshold of the pick, V(t) represents the amplitude of the impact at time t, V h0 represents the initial hard failure threshold of the pick, V h (t) represents the hard failure threshold of the pick at time t, TS i is the duration of the ith impact.

[0062] Optionally, the total wear degradation amount is obtained based on the natural wear degradation amount, instantaneous degradation amount, and accelerated degradation amount; the pick hard failure threshold is obtained based on the total wear degradation amount and the initial hard failure threshold of the pick; and the pick height is obtained based on the total wear degradation amount and the initial height of the pick.

[0063] In this embodiment, during the wear process, the ability of the pick to resist fatal impact gradually weakens. Figure 3.2 As shown in Figure 2, as the total degradation amount continues to increase, the hard failure threshold gradually decreases, that is, the hard failure threshold of the pick is inversely correlated with the total degradation amount. Assuming that the hard failure threshold is linearly related to the total degradation amount, at time t, the hard failure threshold V of the pick is h (t) is:

[0064]

[0065] Among them, V h (t) is the hard failure threshold, X(t) is the total degradation of pick wear, and ρ is the second linear relationship constant.

[0066] like Figure 3.1 As shown in FIG. 1 , the main evaluation index of pick failure caused by wear is height, and the change of pick height during wear is a gradually decreasing process. Therefore, this embodiment adopts a linear decreasing model to describe the change of pick height to intuitively describe the wear degradation process of the pick. The expression is:

[0067] Y(t)=Y(0)-X(t)

[0068] Among them, Y(0) represents the initial height of the pick, and X(t) represents the total degradation of the pick due to wear.

[0069] If the pick is subjected to m impacts before time t, the total degradation X(t) is the sum of the natural wear degradation X1(t), the instantaneous degradation S1(t) caused by the impact, and the accelerated degradation S2(t) during the impact duration. The expression is:

[0070] X(t)=X1(t)+S1(t)+S2(t)

[0071] Step S2, constructing an impact degradation model of the pick under the competitive failure mode, including modeling the natural wear degradation, random load impact, and degradation under random load impact of the pick.

[0072] Optionally, natural wear degradation of the pick is modeled, including defining a probability density function for the natural degradation rate.

[0073] In this embodiment, the main form of pick degradation is wear. Different cantilever tunneling machines have different cutting speeds, cutting torques, cutting trajectories and cutting drum forms, and the picks used also have different external structures, material hardness, processing technology and installation positions. The differences in the above factors will have different effects on pick wear. Therefore, considering the individual differences of picks in the natural wear process, and the wear increments per unit time are independent non-negative values, the Gamma process can be used to describe the continuous natural wear degradation process of picks with independent monotonic non-negative increments. Therefore, this embodiment assumes that the natural degradation rate δ0 (wear amount per unit time) of pick wear obeys the Gamma distribution, that is, δ0:Gamma(α,β), and its probability density function is:

[0074]

[0075] Among them, δ0≥0, α is the shape parameter, β is the scale parameter, and a is the random variable set by the probability density function of the natural degradation rate.

[0076] Optionally, random load impact modeling of the pick includes defining a probability density function of the number of impact arrivals per unit time, a probability density function of the impact amplitude, and a probability density function of the impact duration.

[0077] In this embodiment, the geological conditions of the coalfield are complex and changeable, and there are irregular high-hardness gangue and faults in the coal rock, which causes the pick to be subjected to random impact of alternating loads when cutting the coal rock. Since the random impact caused by high-hardness gangue and faults is unpredictable, it arrives suddenly, and the probability of a small number of arrivals per unit time is high, and the probability of a large number of arrivals is low. Therefore, this embodiment assumes that the random impact arrives at a constant rate λ0 following a homogeneous Poisson process, and its probability density function is:

[0078]

[0079] Where N(t) represents the number of times the shock arrives before time t, and λ0 is the random shock rate, which is the distribution parameter under the predetermined number of shock arrivals.

[0080] In this embodiment, in the same coal seam, the coal rock properties are relatively stable, and the fluctuation range of coal rock hardness is small. Due to the concentration, symmetry and uniform variability of normal distribution, this embodiment assumes that the hardness of coal rock is independent of each other and obeys the same normal distribution. Since the hardness of gangue and fault is relatively high, this embodiment uses truncated normal distribution to describe them, that is, V i :N(μ,σ 2 ), its probability density function is:

[0081]

[0082] Among them, μ is the mean, σ is the standard deviation, V i is the impact amplitude, i.e. the hardness of the gangue and faults mixed in the coal rock, k s is the minimum value of the shock amplitude, TN is the identifier of the truncated normal distribution, and b is the random variable set by the probability density function of the shock amplitude.

[0083] In this embodiment, the high-hardness waste rock and fault have a certain volume, and the time required to cut such hard rock is defined as the impact duration TS. i In actual working process, the probability of short impact duration is high, and the probability of long impact duration is low, and the durations of different impacts are independent of each other. Therefore, this embodiment assumes that the duration of impact TS i It obeys the exponential distribution with parameter λ1, that is, TS i :E(λ1), its probability density function is:

[0084]

[0085] Among them, TS i ≥0; λ1 is the mean, which is the distribution parameter under the impact duration.

[0086] Optionally, modeling the degradation of the pick under random load impacts includes the following steps:

[0087] Step S21: obtaining the natural wear degradation amount, instantaneous degradation amount, accelerated degradation amount and hard failure threshold under the influence of degradation of the cutting pick to be tested during the impact process;

[0088] In this embodiment, during the process of cutting coal and rock, except for the accelerated degradation stage caused by high-hardness gangue and faults during the impact duration, the pick is in the natural wear degradation stage caused by coal and rock. If the pick is subjected to m impacts before time t, the natural wear degradation amount X1(t) is:

[0089]

[0090] In addition, if the shock duration TS i If a new impact occurs during this period, the current impact process will be terminated.

[0091] In this embodiment, due to the sudden change in the hardness of coal rock, the random load impact will cause the pick to suddenly degrade at the moment of its arrival, thereby causing a sudden increase in the degradation amount. According to engineering practice, the higher the hardness of the gangue and fault, the greater the impact amplitude, and the greater the instantaneous degradation amount caused, that is, the instantaneous degradation amount of the pick is positively correlated with the impact amplitude, so it is assumed that the instantaneous degradation amount and the impact amplitude are in a linear relationship, that is If the pick is subjected to m impacts before time t, the instantaneous degradation S1(t) is:

[0092]

[0093] Among them, V i is the amplitude of the ith impact, is the first linear relationship constant.

[0094] In this embodiment, since the gangue and fault have a certain volume and are harder than coal rock, the pick will degrade rapidly during the impact duration caused by them. The accelerated degradation rate at different durations is δ i =η i δ0,η i is the degradation rate acceleration factor, where η i>1. According to engineering practice, the higher the hardness of the gangue and fault, the greater the impact amplitude, and the greater the degradation rate acceleration factor, that is, the degradation rate acceleration factor of the pick during the impact duration is positively correlated with the impact amplitude. With the increase of the hardness of the gangue and fault, the accelerated degradation rate of the pick wear increases exponentially; at the same time, since the function value of the exponential function is always greater than 1 when the independent variable is a positive number, it can reflect the accelerated effect of the degradation rate acceleration factor on the degradation of the pick under different impact amplitudes, so it is assumed that the degradation rate acceleration factor η i =exp(θV i ). If the pick is subjected to m impacts before time t, the accelerated degradation amount S2(t) during the impact duration is:

[0095]

[0096] Here, θ is the exponential relationship constant.

[0097] Step S22: Based on the natural wear degradation amount, instantaneous degradation amount, accelerated degradation amount and hard failure threshold under the influence of degradation, define the probability density function of the natural wear degradation amount, the probability density function of the instantaneous degradation amount, the probability density function of the accelerated degradation amount and the probability density function of the total degradation amount of the pick and the pick height.

[0098] In this embodiment, it can be known from the natural degradation amount X1(t) that, assuming that before time t, the pick has been subjected to m impacts, the total degradation amount of its natural wear is Assume that the sum of the duration of m shocks is T1, that is, Since the distribution of the sum of m independent and identically distributed exponential distributions is Gamma distribution, its parameters are the number of exponential distributions m and the parameter λ1 of the exponential distribution, that is, T1:Gamma(m,λ1), its probability density function is for:

[0099]

[0100] Where c is the random variable set by the probability density function of the natural degradation amount.

[0101] Assume that the natural wear degradation time under random load impact is T2, then T2 = t-T1, and its probability density function is It can be deduced that:

[0102]

[0103] The total amount of natural wear degradation of the pick is X1(t) = δ0T2, and its probability density function is It can be deduced that:

[0104]

[0105] In this embodiment, it can be known from the instantaneous degradation amount S1(t) that, assuming that before time t, the pick has been subjected to m impacts, the total instantaneous degradation amount is Assume the sum of the m impact amplitudes is W1, that is, Since the distribution of the sum of m independent and identically distributed normal distributions is still a normal distribution, its parameters are m times the expectation and variance of the original normal distribution, so W1:N(mμ,mσ 2 ). The total amount of instantaneous degradation of the pick Since the normal distribution and the constant The product of is still a normal distribution, and its parameters are the expectation and variance of the original normal distribution. Double times, so S1: Because the impact amplitude V i ≥k s , so the instantaneous degradation This embodiment uses a truncated normal distribution to describe it, and its probability density function It can be deduced that:

[0106]

[0107] Where e is the random variable set by the probability density function of the instantaneous degradation amount.

[0108] In this embodiment, it can be known from the accelerated degradation amount S2(t) that, assuming that before time t, the pick has been subjected to m impacts, the total amount of accelerated degradation at different impact durations is Let W2 = θV i Since the product of a normal distribution and a constant θ is still a normal distribution, its parameters are θ times the expectation and variance of the original normal distribution and θ 2 times, so W2:N(θμ,θ 2 σ 2 ). Since θ>0, V i ≥k s , then W2 ≥ θk s , this embodiment uses the truncated normal distribution to describe it, and its probability density function It can be deduced that:

[0109]

[0110] Where g is the random variable set by the probability density function of the total amount of accelerated degradation.

[0111] Assume that the degradation rate acceleration factor W3 = exp(W2), its probability density function It can be deduced that:

[0112]

[0113] Assume that the accelerated degradation rate W4 = δ0W3, its probability density function It can be deduced that:

[0114]

[0115] Assume that the acceleration degradation amount during a single impact duration is W5 = TS i W4, its probability density function It can be deduced that:

[0116]

[0117] The total amount of accelerated degradation of the pick under different impact durations is: Then its probability density function is the probability density function of W5 The m-fold convolution can be derived as follows:

[0118]

[0119] In this embodiment, the total degradation amount of the pick in the competitive failure mode is X(t)=X1(t)+S1(t)+S2(t). If the pick is subjected to m impacts before time t, the probability density function f of the total degradation amount X(t) of the pick is X (x|N(t)=m) can be derived as follows:

[0120]

[0121] In this embodiment, the height of the pick in the competitive failure mode is Y(t)=Y(0)-X(t). If the pick is subjected to m impacts before time t, the probability density function f of the pick height Y(t) is Y (y|N(t)=m) can be derived as follows:

[0122]

[0123] Starting from the degradation characteristics of pick teeth, the present invention describes the failure mode of pick teeth in detail by considering the effects of continuous impact, variable rate accelerated degradation and hard failure threshold change on pick teeth failure, and constructs an impact degradation model of pick teeth under competitive failure mode to reflect the mutual influence of wear degradation and random load impact. And on the basis of degradation modeling, a reliability model of pick teeth under competitive failure mode is constructed to reflect the comprehensive influence of wear degradation and random load impact on pick teeth reliability, so as to more accurately evaluate the reliability of pick teeth.

[0124] The failure of the pick is the result of the competition between the soft failure caused by wear and degradation and the hard failure caused by random load impact. First, the reliability of the pick under different failure modes needs to be analyzed separately, and then the reliability modeling of the pick under the competition failure mode is performed. Therefore, an embodiment of the present invention provides a method for analyzing the reliability of the pick under random load impact. The reliability modeling of the pick under soft failure includes:

[0125] Determine whether the cutter to be tested is subjected to random impact, and when it is not subjected to random impact, obtain the height change of the cutter to be tested under natural degradation, and construct a first soft failure reliability function based on the height change probability density function corresponding to the height change under natural degradation;

[0126] When subjected to random impact and the number of random impacts reaches a preset number, the height change of the tested pick under natural degradation, instantaneous degradation due to impact, and variable rate degradation is obtained, and a second soft failure reliability function is constructed based on the height change probability density function corresponding to the height change.

[0127] In this embodiment, when the height Y(t) of the pick is lower than the soft failure threshold H s When , the pick fails softly. Considering the influence of random load impact, soft failure and hard failure are interdependent, and the wear degradation process is different when different numbers of impacts arrive, the probability of the pick surviving soft failure when a fixed number of impacts arrive is derived for the following two cases.

[0128] Case 1: The number of shocks is 0, that is, there is no random shock.

[0129] When no random impact occurs before time t, that is, N(t) = 0, the height Y(t) of the pick is only affected by long-term continuous natural degradation. When only soft failure is considered, the reliability function of the pick at time t is:

[0130]

[0131] Case 2: The number of impacts is not 0, that is, it is subjected to random impacts.

[0132] When the number of impacts before time t is N(t) = m, m = 1, 2..., the height Y(t) of the pick is affected by long-term continuous natural degradation, instantaneous degradation caused by impact, and accelerated degradation at different impact durations. When only soft failure is considered, the reliability function of the pick at time t is:

[0133]

[0134] Optional, hard failure cutter reliability modeling includes:

[0135] Obtaining a probability density function of the difference between the impact amplitude and the hard failure threshold;

[0136] Based on the above probability density function, the reliability function of the pick reliability model under hard failure is obtained.

[0137] In this embodiment, when the amplitude V of the impact on the pick is i Higher than the hard failure threshold V of the pick at time t h (t), that is, V i -V h When (t)>0, the pick fails. The hard failure threshold V h (t) It can be seen that the hard failure threshold of the pick in the competitive failure mode is Assume the impact amplitude V i and hard failure threshold V h The difference of (t) is V S (t), its expression is:

[0138]

[0139] set up If the pick is subjected to m impacts before time t, its probability density function is It can be deduced that:

[0140]

[0141] Impact amplitude V i and hard failure threshold V h (t) difference V S (t) = V i +W6, if the pick has been subjected to m impacts before time t, then V S The probability density function of (t) It can be deduced that:

[0142]

[0143] According to the above embodiment, the probability of a pick surviving a random impact is Therefore, when the number of impact arrivals N(t) = m, m = 1, 2…, only hard failure is considered, and the reliability function of the pick at time t is:

[0144]

[0145] Optionally, based on the reliability functions of the pick reliability model under two soft failure conditions and the reliability function of the pick reliability model under hard failure, a reliability function of the pick competitive failure is obtained.

[0146] In this embodiment, the failure of the pick is the result of the competition between soft failure and hard failure. In the above embodiment, the reliability function of soft failure and hard failure under a fixed number of random impacts is derived. Therefore, the reliability function of the failure of the pick at time t can be derived as follows:

[0147]

[0148] The embodiment of the present invention describes the failure mode of the pick by fully considering the correlation between natural wear degradation and random load impact, that is, the degradation process of the pick under random load impact includes long-term continuous natural wear degradation, instantaneous degradation caused by impact, and variable rate accelerated degradation within different impact durations, and its failure is the result of the competition between soft failure caused by wear degradation and hard failure caused by random load impact. Secondly, using the degradation-based reliability modeling method, the natural wear degradation, random load impact, and degradation under random load impact of the pick are modeled respectively, and the impact degradation model of the pick under the competitive failure mode is constructed. On this basis, the reliability model of the pick under the competitive failure mode is constructed. The pick competitive failure reliability model of the present invention, which comprehensively considers continuous impact, variable rate accelerated degradation, and hard failure threshold changes, is more in line with engineering practice, provides effective guidance for the design optimization and reliability improvement of the pick, and provides a theoretical basis and scientific basis for the reliability assessment, maintenance and spare parts management of the pick system.

[0149] In order to further verify the effectiveness and feasibility of the method of the present invention, the reliability of the pick is analyzed based on the engineering data of a hard rock cantilever tunnel boring machine.

[0150] The present invention selects a small-section rock tunnel boring machine with a cutting power of 260kW from a certain tunnel boring machine manufacturing company for cutting test. The tunnel boring machine adopts a longitudinal axis pick system, and the assembly materials during the test period are picks of different qualities such as 35CrMnSi, 42CrMo, 40Cr, 40Mn2B, and 35MnP. Picks of different materials have different wear resistance and wear degradation rates. The cross section of the tunnel is rectangular, 4.4m wide, 2.9m high, and the cross-sectional area is 12.76m 2 The lithology of the tunnel is mainly grayish white medium-grained sandstone and grayish black sandy mudstone, with an average Proctor hardness of f6.8, accompanied by high-hardness gangue and faults.

[0151] First, in the cutting test, the coal-rock online identification system was used to obtain the random load impact related data caused by gangue and faults. The system includes 16 online monitoring sensors that reflect load changes, as well as a dynamic signal analyzer, an industrial computer and a display. By using multi-sensor characteristic signal samples to learn and train the RBF neural network, the intelligent analysis of dynamic loads is completed, thereby realizing online monitoring and accurate identification of the hardness, arrival times and duration of gangue and faults. The method used to obtain the data related to pick wear is offline detection combined with the measurement results of the coal-rock identification system. Secondly, based on a large amount of test data and combined with expert experience, data fitting software is used to perform maximum likelihood estimation of multiple distributions for each random variable. At the same time, the best distribution is selected through the goodness of fit test, so that the best distribution is used as the distribution of each random variable in this paper. For the parameters in the relationship model, linear and nonlinear regression are used. The data measured in the test include the natural degradation of the pick, instantaneous degradation, accelerated degradation, total degradation, hardness of gangue and fault, number of arrivals and duration, etc. The measuring equipment, measurement methods and fitting parameters of various data are shown in Table 1.

[0152] Table 1

[0153]

[0154] In order to consider the influence of different factors on pick failure and verify the universality of the model, the established pick reliability model was transformed into a pick reliability model considering different factors by setting different parameter values. Table 2 compares the main factors considered by these models.

[0155] Table 2

[0156]

[0157] In order to illustrate the influence of instantaneous impact, impact duration, variable rate degradation within different impact durations, and hard failure threshold changes on the reliability of the pick, the reliability curves of the five models listed in Table 2 are plotted on the Figure 4 From this, we can draw the following conclusions:

[0158] (1) The effect of instantaneous impact on the failure process of pick can be obtained by comparing the reliability curves of Model 1 and Model 2. Figure 4It can be seen that the pick considering only natural degradation has a high stable reliability stage of 7 days, that is, the pick will not fail within 7 days, which is very different from the actual application of the pick. The pick considering natural degradation and instantaneous impact competition failure directly enters the rapid degradation stage and does not have a high stable reliability stage. The reliability of the pick at 7 days is only 0.44, that is, the probability that the pick will not fail within 7 days is only 44%, which is in line with the actual degradation trend of the pick. This is because random impact will cause the pick to suddenly degenerate at the moment of its arrival, accelerating the degradation process of the pick, and considering the soft failure and hard failure competition failure modes, any failure will cause the pick to fail, so the model considering natural degradation and instantaneous impact competition failure has a lower reliability level than the model considering only natural degradation failure.

[0159] (2) The effect of impact duration on the failure process of pick can be obtained by comparing the reliability curves of Model 2 and Model 3. Figure 4 It can be seen that the reliability of the pick without considering the impact duration is 0.34 at 10 days, and the reliability of the pick considering the impact duration is 0.19 at 10 days, and the probability of pick failure increases by 15%. This is because if the impact duration is not considered, the impact will only cause the pick to suddenly degrade at the moment of its arrival. However, when the impact duration is considered, the pick will accelerate degradation at different impact durations, and the natural degradation time will be shortened accordingly. When the impact duration exists, ignoring its impact on the pick failure process will overestimate the reliability of the pick, so the model considering the impact duration always has a lower reliability level than the model not considering the impact duration.

[0160] (3) The influence of variable rate degradation on the failure process of pick cutters at different impact durations can be obtained by comparing the reliability curves of Model 3 and Model 4. Figure 4 It can be seen that the reliability of the pick without considering the variable rate is 0.19 at 10 days, and the reliability of the pick considering the variable rate is 0.16 at 10 days, and the probability of pick failure increases by 3%. This is because the assumption that the degradation rate of the pick is constant during different impact durations is actually a simplification of the degradation process during the impact duration. In practice, the degradation rate of the pick wear during the impact duration is related to the amplitude of the impact. Ignoring the variable rate degradation process will lead to inaccurate calculation of the pick reliability. Therefore, within different impact durations, the model considering variable rate degradation always has a more accurate reliability level than the model not considering variable rate degradation.

[0161] (4) The influence of the change of hard failure threshold on the failure process of pick can be obtained by comparing the reliability curves of Model 4 and Model 5. Figure 4It can be seen that the reliability of the pick without considering the change of the hard failure threshold is 0.16 at 10 days, and the reliability of the pick considering the change of the hard failure threshold is 0.13 at 10 days, and the probability of pick failure increases by 3%. This is because as the degree of wear increases, the hard failure threshold of the pick decreases with the increase of the total degradation amount, the ability of the pick to resist fatal impact decreases, and it is more sensitive to hard failure, and the probability of hard failure increases. Ignoring the impact of the change of the hard failure threshold on the failure process of the pick will overestimate the reliability of the pick. Therefore, the model considering the change of the hard failure threshold always has a lower reliability level than the model not considering the change of the hard failure threshold.

[0162] (5) The reliability curve of the competitive failure of the pick under the combined action of natural wear degradation and random load impact is shown in Model 5. Figure 4 It can be seen that there is no high stable reliability stage in the entire life cycle of the pick. From the beginning of cutting coal and rock, it enters the rapid degradation stage under the competitive failure mode, and the reliability curve shows a sharp decline. The reliability of the pick that can be used for 5 days is only 0.5, which is an important reason why the pick is the most consumed and most expensive spare part of the tunnel boring machine. Therefore, according to the results of the reliability evaluation of the pick, when the wear state of the pick exceeds the acceptable range, necessary replacement and reasonable reserve of spare parts are required.

[0163] A sensitivity analysis is performed on each parameter in the constructed reliability model, specifically including: obtaining the parameter data of the reliability model, lowering the values ​​of each parameter contained in the parameter data until they are adjusted to the preset minimum value of the corresponding parameter, saving the reliability change of the reliability model during the value reduction operation, and performing sensitivity analysis based on the reliability change.

[0164] In this embodiment, in order to verify the pick reliability model constructed by the present invention and guide the pick optimization design to improve reliability, it is necessary to analyze the sensitivity of the model parameters to study their influence on reliability. θ and ρ are key parameters that reflect the natural wear degradation of the pick and the impact of random loads. Therefore, based on the engineering data, the sensitivity of the above parameters is analyzed, and the specific parameter ranges are shown in Table 3.

[0165] Table 3

[0166]

[0167] The sensitivity analysis of the shape parameter α and scale parameter β of the natural degradation rate δ0 is as follows: Figure 5 and Figure 6As shown in the figure. When the value of α decreases from 5 to 1, the reliability of the pick at 5 days increases by 0.12; when the value of β decreases from 0.2 to 0.04, the reliability of the pick at 10 days increases by 0.15. Therefore, the reliability of the pick is improved by reducing the value of α or β, and the effect becomes more obvious as the working time of the pick increases, which shows that the contribution of soft failure to pick failure is gradually increasing.

[0168] In practice, α and β represent the objective properties that affect the wear of the pick, namely the material composition, shape structure, processing technology and installation angle of the pick. Therefore, the pick can have better wear resistance and reduce the natural degradation rate of the pick by strengthening the material strength of the pick, optimizing the structural design of the pick, improving the heat treatment process of the pick, adjusting the installation angle of the pick and using surface strengthening technology. This will reduce the natural wear and accelerated wear degradation of the pick, reduce the possibility of soft failure and improve the reliability of the pick.

[0169] The sensitivity analysis of the distribution parameter λ0 of the number of shock arrivals and the distribution parameter λ1 of the shock duration is as follows: Figure 7 and Figure 8 As shown. When the value of λ0 decreases from 0.15 to 0.03, the reliability of the pick at 10 days increases by 0.31; when the value of λ1 decreases from 0.4 to 0.2, the reliability of the pick at 10 days increases by 0.15. Therefore, the reliability of the pick is improved by reducing the value of λ0 or λ1. λ0 has a significant impact on the reliability of the pick in the initial stage of its operation, and the impact is large; λ1 has a small impact on its reliability in the initial stage of the pick, and the impact gradually appears after the initial stage. This shows that the frequency of impact arrival has a greater impact on the reliability of the pick than the impact duration.

[0170] In practice, λ0 represents the position distribution of gangue and faults in the coal seam. Therefore, a detailed geological survey of the coal seam should be carried out before actual production to understand the layout of gangue and faults in the coal rock. At the same time, real-time coal rock identification technology should be applied in the production process, the properties and changes of coal rock should be monitored online, the construction layer of the roadheader should be dynamically adjusted, and the cutting trajectory of the roadheader should be planned so that the picks can avoid frequent encounters with high-hardness gangue and faults in a short period of time as much as possible, thereby reducing the instantaneous degradation and accelerated degradation caused by random load impact; λ1 represents the volume of gangue and faults. Therefore, the cutting capacity of the roadheader should be improved or the rock breaking method should be changed in the design, and a roadheader suitable for hard rock cutting should be developed to cut the same volume of gangue and faults in a shorter time, reduce the impact duration, thereby reducing the accelerated degradation within the impact duration, and improve the reliability of the picks.

[0171] The first linear relationship constant between the instantaneous degradation and the impact amplitude The sensitivity analysis of the exponential relationship constant θ between the degradation rate acceleration factor and the shock amplitude during the shock duration is as follows: Fig. 9 and Fig.10 As shown. When the value of θ decreases from 0.08 to 0.04, the reliability of the pick at 10 days increases by 0.06; when the value of θ decreases from 0.4 to 0.2, the reliability of the pick at 10 days increases by 0.13. or θ, the reliability of the pick is improved. In the initial stage of the pick working, θ and θ have little effect on its reliability. After the initial stage, the influence gradually appears, and the influence of θ is more obvious. This shows that in the process of pick degradation, the influence of variable rate accelerated degradation on the reliability of the pick is greater than that of instantaneous degradation.

[0172] In practice θ represents the relationship between the pick, gangue and fault. Therefore, the contact angle, contact area, contact load characteristics and relative motion characteristics of the pick, gangue and fault can be optimized to reduce the influence of gangue and fault on the degradation of the pick, thereby reducing the instantaneous degradation and accelerated degradation caused by random load impact and improving the reliability of the pick.

[0173] The sensitivity analysis of the second linear relationship constant ρ between the pick hard failure threshold and the total degradation is as follows: Fig.11 As shown. When the value of ρ decreases from 0.05 to 0.01, the reliability of the pick at 10 days increases by 0.03. Therefore, the reliability of the pick is improved by reducing the value of ρ. The influence of ρ on the reliability of the pick in the initial stage of its operation is negligible, and its influence on its reliability gradually increases after the initial stage. The larger the value of ρ, the faster the hard failure threshold of the pick decreases, and the greater the possibility of hard failure of the pick. This shows that hard failure is not its main failure mode in the initial stage of the pick's operation. After the initial working stage, the contribution of hard failure to the failure of the pick gradually increases.

[0174] In practice, ρ represents the rate at which the pick's ability to resist fatal impact decreases. Therefore, on the basis of ensuring impact toughness, the hardness of the material at different heights of the pick can be homogenized to reduce the influence of wear on the hard failure threshold of the pick, thereby reducing the possibility of hard failure of the pick and improving the reliability of the pick.

[0175] The sensitivity verification process of the present invention shows that the competitive failure modes of soft failure caused by wear degradation and hard failure caused by random load impact have a significant impact on the reliability evaluation of the pick, and can obtain more accurate reliability evaluation results. In addition, it is also possible to carry out optimization design with the goal of improving reliability and considering cost constraints, helping relevant practitioners to select appropriate model parameters and effectively improve the reliability of the pick.

[0176] See also Fig.12 , Fig.12is a schematic diagram of the structure of a computer device provided by an optional embodiment of the present invention, such as Fig.12 As shown, the computer device includes: one or more processors 10, a memory 20, and interfaces for connecting various components, including high-speed interfaces and low-speed interfaces. Various components are connected to each other using different buses for communication, and can be installed on a common mainboard or installed in other ways as needed. The processor can process the instructions executed in the computer device, including instructions stored in or on the memory to display the graphical information of the GUI on an external input / output device (such as, a display device coupled to the interface). In some optional embodiments, if necessary, multiple processors and / or multiple buses can be used together with multiple memories and multiple memories. Similarly, multiple computer devices can be connected, and each device provides some necessary operations (for example, as a server array, a group of blade servers, or a multi-processor system). Fig.12 A processor 10 is taken as an example.

[0177] The processor 10 may be a central processing unit, a network processor or a combination thereof. The processor 10 may further include a hardware chip. The hardware chip may be a dedicated integrated circuit, a programmable logic device or a combination thereof. The programmable logic device may be a complex programmable logic device, a field programmable gate array, a general purpose array logic or any combination thereof.

[0178] The memory 20 stores instructions executable by at least one processor 10, so that the at least one processor 10 executes the method shown in the above embodiment.

[0179] The memory 20 may include a program storage area and a data storage area, wherein the program storage area may store an operating system, an application required for at least one function; the data storage area may store data created according to the use of the computer device, etc. In addition, the memory 20 may include a high-speed random access memory, and may also include a non-transient memory, such as at least one disk storage device, a flash memory device, or other non-transient solid-state storage device. In some optional embodiments, the memory 20 may optionally include a memory remotely arranged relative to the processor 10, and these remote memories may be connected to the computer device via a network. Examples of the above-mentioned network include, but are not limited to, the Internet, an intranet, a local area network, a mobile communication network, and combinations thereof.

[0180] The memory 20 may include a volatile memory, such as a random access memory; the memory may also include a non-volatile memory, such as a flash memory, a hard disk or a solid state drive; the memory 20 may also include a combination of the above types of memory.

[0181] The computer device further comprises a communication interface 30 for the computer device to communicate with other devices or a communication network.

[0182] The embodiment of the present invention also provides a computer-readable storage medium. The method according to the embodiment of the present invention can be implemented in hardware, firmware, or can be implemented as a computer code that can be recorded in a storage medium, or can be implemented as a computer code that is originally stored in a remote storage medium or a non-temporary machine-readable storage medium and will be stored in a local storage medium through a network download, so that the method described herein can be stored in such software processing on a storage medium using a general-purpose computer, a dedicated processor, or programmable or dedicated hardware. Among them, the storage medium can be a magnetic disk, an optical disk, a read-only storage memory, a random access memory, a flash memory, a hard disk or a solid-state hard disk, etc.; further, the storage medium can also include a combination of the above types of memories. It can be understood that a computer, a processor, a microprocessor controller, or programmable hardware includes a storage component that can store or receive software or computer code. When the software or computer code is accessed and executed by a computer, a processor, or hardware, the method shown in the above embodiment is implemented.

[0183] Although the embodiments of the present invention have been described in conjunction with the accompanying drawings, those skilled in the art may make various modifications and variations without departing from the spirit and scope of the present invention, and such modifications and variations are all within the scope defined by the appended claims.

Claims

1. A reliability analysis method for pick under random load impact, characterized in that: The method comprises: S1: Define the competing failure modes of the pick, which are the competition between soft failure caused by wear degradation and hard failure caused by random load impact; S2: Construct an impact degradation model of the pick under competing failure modes, including modeling the natural wear degradation of the pick, random load impact, and degradation under random load impact; S3: Based on the degradation modeling, a reliability model of the pick under the competitive failure mode is constructed, and a sensitivity analysis is performed on the parameters in the constructed reliability model, including but not limited to shape parameters and scale parameters corresponding to the objective attributes of pick wear, distribution parameters corresponding to the distribution of gangue in the coal seam, the first linear relationship constant and exponential relationship constant corresponding to the relationship between the pick and the gangue and fault, and the second linear relationship constant corresponding to the rate of decrease of the hard failure threshold.

2. The reliability analysis method of a pick under random load impact according to claim 1, characterized in that: In step S2, the natural wear degradation of the pick is modeled, including defining a probability density function of the natural degradation rate.

3. The reliability analysis method of a pick under random load impact according to claim 2, characterized in that: In step S2, the random load impact of the pick is modeled, including defining the probability density function of the number of impact arrivals per unit time, the probability density function of the impact amplitude, and the probability density function of the impact duration.

4. The reliability analysis method of a pick under random load impact according to claim 3 is characterized by: In step S2, the degradation of the pick under the impact of random load is modeled, including the following steps: Obtain the natural wear degradation amount, instantaneous degradation amount, accelerated degradation amount and hard failure threshold under the influence of degradation of the cutting pick to be tested during the impact process; Based on the natural wear degradation amount, instantaneous degradation amount, accelerated degradation amount and hard failure threshold under the influence of degradation, the probability density function of the natural wear degradation amount, the probability density function of the instantaneous degradation amount, the probability density function of the accelerated degradation amount and the probability density function of the total degradation amount of the pick and the pick height are defined.

5. The method for analyzing reliability of pick under random load impact according to claim 4, characterized in that: The total wear degradation amount is obtained based on the natural wear degradation amount, the instantaneous degradation amount and the accelerated degradation amount, the pick hard failure threshold is obtained based on the total wear degradation amount, and the pick height is obtained based on the total wear degradation amount and the initial height of the pick.

6. The reliability analysis method of a pick under random load impact according to claim 5, characterized in that: The hard failure threshold is inversely correlated with the total wear degradation amount, and the hard failure threshold is: Vh(t)=Vh0-ρX(t) Among them, V h (t) is the hard failure threshold, X(t) is the total degradation of pick wear, and ρ is the second linear relationship constant.

7. The reliability analysis method of a pick under random load impact according to claim 6, characterized in that: The reliability modeling of pick under hard failure includes: Calculating the difference between the impact amplitude and the hard failure threshold, and obtaining a probability density function of the difference between the impact amplitude and the hard failure threshold; Based on the above probability density function, the reliability function of the pick reliability model under hard failure is obtained; The reliability function is: Where N(t) represents the number of times the shock arrives before time t, is the probability density function of the difference between the impact amplitude and the hard failure threshold.

8. The reliability analysis method of a pick under random load impact according to claim 1, characterized in that: The reliability modeling of pick under soft failure includes: Determine whether the cutter to be tested is subjected to random impact, and when it is not subjected to random impact, obtain the height change of the cutter to be tested under natural degradation, and construct a first soft failure reliability function based on the height change probability density function corresponding to the height change under natural degradation; When subjected to random impact and the number of random impacts reaches a preset number, the height change of the tested pick under natural degradation, instantaneous degradation due to impact, and variable rate degradation is obtained, and a second soft failure reliability function is constructed based on the height change probability density function corresponding to the height change.

9. The reliability analysis method of a pick under random load impact according to claim 1, characterized in that: Based on the reliability functions of the pick reliability model under two soft failure conditions and the reliability function of the pick reliability model under hard failure, the reliability function of the pick competition failure is obtained.

10. The reliability analysis method of a pick under random load impact according to claim 1, characterized in that: Conduct sensitivity analysis on each parameter in the constructed reliability model, including: Obtain the parameter data of the reliability model, lower the values ​​of each parameter contained in the parameter data until they are adjusted to the preset minimum value of the corresponding parameter, save the reliability change of the reliability model during the value reduction operation, and perform sensitivity analysis based on the reliability change.

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