Method for converting time series loads on the mast of rotary drilling rigs of different models

Abstract

The present invention discloses a method for converting the time series loads of the mast of a rotary drilling rig of different models, and belongs to the technical field of load spectrum collection and compilation of rotary drilling rigs of engineering machinery. The method comprises the following steps: (1) extracting the time series loads of the original model under rock entry working conditions, and performing rain flow counting on the time series loads, and statistically calculating the amplitude and mean of the load cycle; (2) fitting the load amplitude with a Gaussian mixture distribution; (3) calculating the limit error value of the probability density value of the load amplitude; (4) randomly reproducing the amplitude load within the limit error; (5) reproducing the mean load according to the parameters of the converted model; (6) correcting the amplitude load according to the upper limit of the maximum value of the converted model; (7) correcting the mean load according to the lower limit of the minimum value of the converted model; (8) performing rain flow reconstruction on the amplitude and mean of the corrected load cycle; and (9) qualitatively comparing the time series load data characteristics of the original model and the converted model. The method solves the problem of difficulty in repeatedly collecting the time series loads of the mast of a rotary drilling rig of different models.

Description

Technical Field

[0001] The present invention relates to the technical field of load spectrum collection and compilation of engineering machinery rotary drilling rigs, and in particular to a method for converting time-series loads of masts of rotary drilling rigs of different models. Background Art

[0002] Rotary drilling rigs can adapt to various strata, operate in a wide range of conditions, and require large engineering workloads and high workloads. As a key component of a rotary drilling rig, the mast provides a mounting base for the power head, drill rod, and luffing mechanism. It also provides vertical guidance for the drill bit during drilling and a pulley for tightening the wire rope during drill lifting. Therefore, the mast must withstand a variety of loads during operation. Collecting and compiling the operational time-series loads of a rotary drilling rig mast is a prerequisite for studying the mast's structural strength and stiffness, structural fatigue characteristics, and weld fatigue characteristics.

[0003] Because masts are subject to a variety of complex loads during operation, coupled with their inherent weight and complexity, collecting mast load spectra is challenging. Rotary drilling rigs require a variety of models to adapt to varying strata and operating environments. Each model experiences varying mast loads during operation, necessitating the collection and compilation of load spectra for each model. Consequently, repeated collection of sequential mast loads is required for calculating mast strength and stiffness, studying structural fatigue characteristics, and studying weld fatigue characteristics. This wastes significant manpower and resources, resulting in low efficiency. Summary of the Invention

[0004] In order to solve the problem of repeated difficulty in collecting the time-series loads of the masts of rotary drilling rigs of different models, the present invention proposes a method for converting the time-series loads of the masts of rotary drilling rigs of different models. Through this method, the time-series loads of the masts of another converted model can be obtained on the basis of the known time-series loads of the masts of the original model, thereby improving the collection efficiency of the mast loads and solving the problem of repeated difficulty in collecting the time-series loads of the masts of rotary drilling rigs of different models. At the same time, the method is simple and applicable and has high engineering application value.

[0005] To achieve the above object, the technical solution adopted by the present invention is:

[0006] The method for converting the time sequence load of the mast of different rotary drilling rig models includes the following steps:

[0007] (1) Count the rain flow of the original vehicle model's rock penetration time series load, and calculate the amplitude and mean of the load cycle;

[0008] The original vehicle model rock entry condition time series load represents the original vehicle model parameter T x The extreme load is the upper limit of the load parameter of the vehicle model. The maximum value of the original vehicle model's rock entry condition time series load is determined by the vehicle model parameter Tx Decide.

[0009] (2) Fitting the load amplitude with a Gaussian mixture distribution

[0010] First, the load amplitude T of the load cycle a Draw a histogram, divide it into n intervals, and then use Gaussian mixture distribution to fit the histogram. Select the number m of the best Gaussian sub-models based on the goodness of fit. The probability density function expression of Gaussian mixture distribution is as follows:

[0011]

[0012] Among them, T a represents the load cycle amplitude load; a i Represents the weight of each sub-model; μ i represents the mean of each sub-model; δ i Represents the standard deviation of each sub-model; i = 1 ~ m.

[0013] (3) Calculate the limit error value of the probability density value of the load amplitude

[0014] The specific steps for calculating the limit error value of the load amplitude probability density value are as follows:

[0015] (3.1) Calculate the absolute value of the difference between the actual probability density values ​​corresponding to the n histogram intervals in step (2) and the fitted Gaussian mixture theory probability density values;

[0016] (3.2) Fit the absolute values ​​of the differences between the n actual and theoretical probability density values ​​to a normal distribution and calculate the corresponding mean and variance;

[0017] (3.3) Calculate the limit error value of the load amplitude probability density value based on the calculated mean and standard deviation. The calculation formula of the limit error value is as follows:

[0018]

[0019] Where δ represents the limit error value of the probability density value of the load amplitude; σ represents the standard deviation of the normal distribution; μ represents the mean of the normal distribution; n represents the number of intervals of the histogram in step (2); Δt represents the probability of sampling error, and the maximum error value here is 2.1%.

[0020] (4) Randomly reproduce the amplitude load within the limit error

[0021] First, according to the limit error value δ calculated in step (3), a value of the load amplitude probability density value of the original vehicle model is randomly selected within (-δ, +δ), and then the corresponding load amplitude t is inversely calculated based on the randomly selected probability density value. a .

[0022] (5) Reproduce the mean load based on the parameters of the converted vehicle model

[0023] According to the load parameter upper limit value t of the conversion vehicle type x and the load cycle mean T of the original model m Reproduce the mean load of the converted vehicle model. The mean load calculation formula of the converted vehicle model is as follows:

[0024] t mi =T mi -t x (3)

[0025] Among them, t mi represents the mean load of each reproduced conversion model; T mi represents the mean load of each original vehicle type; t x Indicates the load parameter limit value of the converted vehicle model.

[0026] (6) Correct the amplitude load according to the maximum upper limit of the converted vehicle model

[0027] According to the maximum upper limit of the converted vehicle model, the amplitude load is corrected. When the maximum value t max Greater than the upper limit value t of this model x When the load amplitude of the converted vehicle model reproduced in step (4) is corrected using formula t a , the amplitude loads that do not meet the conditions remain unchanged.

[0028] That is, when the maximum value t of the converted vehicle model max =t m +t a >t x hour:

[0029]

[0030] in, Represents the load amplitude of the converted vehicle after correction; R(t x ) represents the random upper limit value of the conversion model, which is within the upper limit value t x Random reproduction within a fixed range, but less than the upper limit t x , the fixed range is determined by the gear difference of the power head reducer; t m represents the mean value of the load reproduced in step (5).

[0031] (7) Correct the mean load according to the minimum lower limit of the converted vehicle model

[0032] When the mean load is corrected according to the minimum lower limit of the converted vehicle model, when the minimum value t min When it is less than 0, use the formula to correct the mean load value t of the converted vehicle model reproduced in step (5)m , the mean loads that do not meet the conditions remain unchanged.

[0033] That is, when the minimum value t of the converted vehicle model min =t m -t a <0 o'clock:

[0034]

[0035] in, Represents the corrected mean load of the conversion model; R(t0) represents the random lower limit of the conversion model. This value is randomly reproduced within the fixed range of the conversion model lower limit t0, but is greater than the lower limit t0. The fixed range is determined by the gear difference of the power head reducer. If the time series load is greater than 0, t0 can be 0; t a represents the load amplitude reproduced in step (4).

[0036] (8) Reconstruct the rain flow based on the corrected load cycle amplitude and mean

[0037] When reconstructing the rain flow of the corrected load cycle amplitude and mean, the load cycle is reconstructed according to the four-point rain flow counting method. The specific steps are as follows:

[0038] (8.1) First, calculate the minimum and maximum values ​​of the load cycles of all converted models and arrange them in the order of minimum, maximum, and minimum, as shown in the following matrix:

[0039] [t min1 t max2 t min3 ...t min(3m-2) t max(3m-1) t min(3m) ]

[0040] Where m represents the number of load cycles.

[0041] (8.2) Then compare t min(3i) and t min(3i+1) Load size:

[0042] If t min(3i) ≤t min(3i+1) , then retain t min(3i) , remove t min(3i+1) , while t min(3i+1) Subsequent loads are shifted forward one time unit;

[0043] Otherwise, keep t min(3i+1) , remove t min(3i) , while t min(3i) Subsequent loads are shifted forward one time unit.

[0044] Where i = 1, 2, ..., m-1, and the last two loads t max(3m-1) and t min(3m) Fill it in directly.

[0045] (8.3) Follow the above steps to reconstruct the time series load of the converted vehicle model.

[0046] (9) Qualitative comparison of the time series load data characteristics of the original model and the converted model

[0047] The time series loads of the original vehicle model and the converted vehicle model are counted and the cumulative probability distribution graphs are drawn respectively. The mathematical distribution characteristics of the time series loads of the original vehicle model and the converted vehicle model are qualitatively compared to see whether they are consistent. If they are consistent, it means that the time series loads of the converted vehicle model are reasonable. If they are inconsistent, repeat steps (4) to (8) until the mathematical distribution characteristics of the time series loads of the vehicle model before and after the conversion are consistent.

[0048] The beneficial effects of the present invention are:

[0049] (1) The amplitude load of the converted vehicle model of the present invention is randomly reproduced within the limit error of the amplitude probability density of the original vehicle model, retaining the mathematical characteristics of the amplitude of the original vehicle model, and the reproduced amplitude load conforms to the operating characteristics of the mast.

[0050] (2) The present invention corrects the amplitude load according to the maximum upper limit of the converted vehicle model, so that the maximum value of the load after reproduction will not exceed the upper limit of the converted vehicle model; and corrects the mean load according to the minimum lower limit of the converted vehicle model, so that the minimum value of the load after reproduction will not exceed the lower limit of the converted vehicle model.

[0051] (3) The present invention can realize the mutual conversion of mast sequential loads between different rotary drilling rig models. The mast sequential load of another model can be reproduced through the known mast sequential load of the original model, and the reproduced mast sequential load of the converted model will not exceed the maximum upper limit and minimum lower limit of the load. It is simple and reliable, suitable for engineering practice, and has wide engineering application value. BRIEF DESCRIPTION OF THE DRAWINGS

[0052] Figure 1 This is a flow chart of the time sequence load conversion method for different types of rotary drilling rig masts;

[0053] Figure 2 This is the torque time series load diagram of the original model mast entering rock working condition;

[0054] Figure 3 It is the load cycle diagram after counting the mast torque time series load rain flow;

[0055] Figure 4 It is a Gaussian mixture distribution fitting diagram for the amplitude load;

[0056] Figure 5 It is a normal distribution fitting diagram of the absolute value of the difference between the actual and theoretical probability density values;

[0057] Figure 6 It is a comparison chart of the amplitude load of the converted model after reproduction and the amplitude load of the original model;

[0058] Figure 7 It is a schematic diagram of the maximum value of the conversion model exceeding the upper limit value;

[0059] Figure 8 It is a schematic diagram of the minimum value of the conversion model being lower than the lower limit value;

[0060] Figure 9 This is the principle diagram of rain flow reconstruction based on load cycle amplitude and mean;

[0061] Figure 10 This is the timing diagram after converting the vehicle model’s torque load and reconstructing the rain flow;

[0062] Figure 11 This is a comparison chart of torque time series loads between the original model and the converted model;

[0063] Figure 12 This is a comparison chart of torque time series load penetration counts of the original model and the converted model;

[0064] Figure 13 It is a comparison chart of the cumulative probability distribution of torque time series load of the original model and the converted model. DETAILED DESCRIPTION

[0065] The invention discloses a method for converting time-sequential loads of masts of rotary drilling rigs of different vehicle types, which selects the time-sequential torque loads of mast power heads to perform load conversion on different vehicle types.

[0066] The present invention will be further described below with reference to the embodiments and drawings, but the present invention is not limited thereto.

[0067] The present invention provides a method for converting the time series load of the mast of a rotary drilling rig of different models. A certain type of rotary drilling rig is used as the original model, and the model parameter is T x =360kN.m, collect a section of rock penetration torque time series load. Take the same model of rotary drilling rig as the conversion model, the model parameter is t x =240kN.m. Taking the original model parameter 360kN.m and the converted model parameter 240kN.m as an example, the process of converting the time series load of the mast of different models of rotary drilling rigs is as follows: Figure 1 As shown, the following steps are included:

[0068] (1) Count the rain flow of the original vehicle model's rock penetration time series load and calculate the amplitude and mean of the load cycle

[0069] The original model rock entry condition time series load is the parameter T of the original model. x The maximum load is determined by the vehicle model parameter T x Decide, Figure 2 This is a time series load of torque collected during the rock entry condition of a mast of an original vehicle model; Figure 3 It is the load cycle calculated by rain flow counting of the collected mast torque time series load, including the minimum-maximum value and amplitude-mean value of the load cycle. a represents the amplitude load of the load cycle, T m Represents the mean load of the load cycle.

[0070] (2) Fitting the load amplitude with a Gaussian mixture distribution

[0071] First, the amplitude load T a Draw a histogram (divide into 50 intervals), then use Gaussian mixture distribution to fit the histogram, and select the best number of Gaussian sub-models based on the goodness of fit. Here, 10 Gaussian sub-models are used. Figure 4 is the amplitude load T a Perform a Gaussian mixture distribution fitting graph. The probability density function expression of the Gaussian mixture distribution is as follows:

[0072]

[0073] Among them, T a represents the load cycle amplitude; a i Represents the weight of each sub-model; μ i represents the mean of each sub-model; δ i Represents the standard deviation of each sub-model. The Gaussian mixture distribution fitting parameters are shown in Table 1:

[0074] Table 1

[0075]

[0076] (3) Calculate the limit error value of the probability density value of the load amplitude

[0077] The specific steps for calculating the limit error value of the load amplitude probability density value are as follows:

[0078] (3.1) Calculate the absolute value of the difference between the actual probability density values ​​corresponding to the 50 histogram intervals in step (2) and the fitted Gaussian mixture theory probability density values;

[0079] (3.2) Fit the absolute values ​​of the differences between the 50 actual and theoretical probability density values ​​to a normal distribution and calculate the corresponding mean and variance;

[0080] Figure 5This is a normal distribution fitting diagram of the absolute value of the difference between 50 actual and theoretical probability density values; Table 2 is the normal distribution fitting parameters:

[0081] Table 2

[0082] Fitting parameters Mean μ Standard deviation σ 0.016 0.018

[0083] (3.3) Calculate the limit error value of the load amplitude probability density value based on the calculated mean and standard deviation. The calculation formula of the limit error value is as follows:

[0084]

[0085] Where δ represents the limit error value of the load amplitude probability density value; σ represents the standard deviation of the normal distribution; μ represents the mean of the normal distribution; n represents the number of intervals of the histogram in step (2); Δt represents the probability of sampling error, and the maximum error value here is 2.1%; Substituting the fitting parameter values ​​in Table 2 into formula (2) yields the limit error value δ = 0.0034.

[0086] (4) Randomly reproduce the amplitude load within the limit error

[0087] First, according to the limit error value δ=0.0034 calculated in step (3), a value of the load amplitude probability density value of the original vehicle model is randomly selected within (-0.0034, +0.0034), and then the corresponding load amplitude t is inversely calculated based on the randomly selected probability density value. a , Figure 6 This is a comparison chart of the reproduced amplitude load of the converted model and the amplitude load of the original model. The amplitude load of the converted model is randomly reproduced within the amplitude probability density limit error of the original model, preserving the mathematical characteristics of the original model amplitude.

[0088] (5) Reproduce the mean load based on the parameters of the converted vehicle model

[0089] According to the load parameter upper limit value t of the conversion vehicle type x and the load cycle mean T of the original model m The mean load of the reconstructed vehicle model is calculated using the following formula:

[0090] t mi =T mi -t x (3)

[0091] Among them, t mi represents the mean load of each reproduced conversion model; T mi represents the mean load of each original vehicle model, obtained by rain flow counting in step (1); t x Indicates the load parameter limit value of the conversion vehicle type, where tx =240kN.m.

[0092] (6) Correct the amplitude load according to the maximum upper limit of the converted vehicle model

[0093] According to the maximum upper limit of the converted vehicle model, the amplitude load is corrected. When the maximum value t max Greater than the upper limit value t of this model x When the load amplitude of the converted vehicle model reproduced in step (4) is corrected using formula t a , the amplitude load that does not meet the conditions remains unchanged, Figure 7 Schematic diagram showing that the maximum value of the converted vehicle type exceeds the upper limit value.

[0094] That is, when the maximum value t of the converted vehicle model max =t m +t a >240 hours:

[0095]

[0096] in, Represents the load amplitude of the converted vehicle after correction; R(t x ) represents the random upper limit value of the conversion model, which is within the upper limit value t x Random reproduction within a fixed range, but less than the upper limit t x The fixed range is determined by the gear difference of the power head reducer, here we take 50kN.m; t m represents the mean value of the load reproduced in step (5).

[0097] (7) Correct the mean load according to the minimum lower limit of the converted vehicle model

[0098] When the mean load is corrected according to the minimum lower limit of the converted vehicle model, when the minimum value t min When it is less than 0, use the formula to correct the mean load value t of the converted vehicle model reproduced in step (5) m , the mean loads that do not meet the conditions remain unchanged, Figure 8 Schematic diagram showing that the minimum value of the converted vehicle type is lower than the lower limit value.

[0099] That is, when the minimum value t of the converted vehicle model min =t m -t a <0 o'clock:

[0100]

[0101] in, Represents the corrected mean load of the conversion model; R(t0) represents the random lower limit of the conversion model. This value is randomly reproduced within the fixed range of the conversion model lower limit t0, but is greater than the lower limit t0. The fixed range is determined by the gear difference of the power head reducer, which is 50kN.m here. Since the torque time series load is a value greater than 0, t0 is taken as 0 here; t a represents the load amplitude reproduced in step (4).

[0102] (8) Reconstruct the rain flow based on the corrected load cycle amplitude and mean

[0103] When reconstructing the rain flow of the corrected load cycle amplitude and mean, the load cycle is reconstructed according to the four-point rain flow counting method. The specific steps are as follows:

[0104] (8.1) First, calculate the minimum and maximum values ​​of the load cycles of all converted models and arrange them in the order of minimum, maximum, and minimum, as shown in the following matrix:

[0105] [t min1 t max2 t min3 ...t min(3m-2) t max(3m-1) t min(3m) ]

[0106] Where m represents the number of load cycles, and here m is 1951 load cycles.

[0107] (8.2) Then compare t min(3i) and t min(3i+1) The load size;

[0108] If t min(3i) ≤t min(3i+1) , then retain t min(3i) , remove t min(3i+1) , while t min(3i+1) Subsequent loads are shifted forward one time unit;

[0109] Otherwise, keep t min(3i+1) , remove t min(3i) , while t min(3i) Subsequent loads are shifted forward one time unit.

[0110] Where i = 1, 2, ..., m-1, and the last two loads t max(3m-1) and t min(3m) Fill it in directly.

[0111] Figure 9 Schematic diagram showing the principle of rainflow reconstruction using load cycle amplitude and mean.

[0112] (8.3) Reconstruct the torque time series load of the converted vehicle model according to the above steps;

[0113] Figure 10 This is the time series diagram of the torque load of the converted vehicle model after rain flow reconstruction. It can be seen from the figure that the torque time series load of the converted vehicle model is within the upper and lower limit values. Figure 11 This is a comparison chart of the torque time series load of the original model and the converted model. It can be seen from the figure that the torque time series load is related to the parameters of each model.

[0114] (9) Qualitative comparison of the time series load data characteristics of the original model and the converted model

[0115] The cross-level counting and cumulative probability distribution graphs of the time series loads of the original model and the converted model are performed to qualitatively compare whether the mathematical distribution characteristics of the time series loads of the original model and the converted model are consistent. Figure 12 This is a comparison chart of the torque time series load of the original model and the torque time series load of the converted model. It can be seen from the figure that the torque time series load of the original model and the torque time series load of the converted model have the same mathematical characteristics in load classification; Figure 13 This is a comparison chart of the cumulative probability distribution of the torque time series load of the original model and the torque time series load of the converted model. It can be seen from the figure that the models before and after the conversion have the same cumulative probability distribution characteristics, indicating that the time series load of the converted model is reasonable.

[0116] The present invention can realize the mutual conversion of mast time-series loads between different rotary drilling rig models through the method for converting time-series loads of masts of different rotary drilling rig models. The reproduced time-series load of the converted model will not exceed the maximum upper limit and minimum lower limit of the load. It is simple and reliable, improves the collection efficiency of the mast load, and provides load support for structural strength and stiffness calculation, structural fatigue characteristics research and weld fatigue characteristics research of the mast. It is suitable for engineering practice and has wide engineering application value.

Claims

1. The method for converting time series loads on the masts of rotary drilling rigs of different models is characterized by: The method comprises the following steps: (1) Count the rain flow of the original vehicle model’s rock entry condition time series load, and calculate the amplitude and mean of the load cycle; the original vehicle model’s rock entry condition time series load represents the original vehicle model parameter T x The extreme load is the upper limit of the load parameter of the vehicle model. The maximum value of the original vehicle model's rock entry condition time series load is determined by the vehicle model parameter T x Decide; (2) Fitting the load amplitude of the load cycle with a Gaussian mixture distribution; (3) Calculate the limit error value of the probability density value of the load amplitude; (4) Randomly reproduce the amplitude load within the limit error; According to the limit error value δ calculated in step (3), a value of the load amplitude probability density value of the original vehicle model is randomly selected within (-δ, +δ), and then the corresponding load amplitude t is inversely calculated based on the randomly selected probability density value. a ; (5) Reproduce the mean load according to the parameters of the converted vehicle model; According to the load parameter upper limit value t of the conversion vehicle type x and the load cycle mean T of the original model m Reproduce the mean load of the converted vehicle model. The mean load calculation formula of the converted vehicle model is as follows: t mi =T mi -t x (3) Among them, t mi represents the mean load of each reproduced conversion model; T mi represents the mean load of each original vehicle type; t x Indicates the load parameter limit value of the conversion vehicle type; (6) Correct the amplitude load according to the maximum upper limit of the converted vehicle model; According to the maximum upper limit of the converted vehicle model, the amplitude load is corrected. When the maximum value t max Greater than the upper limit value t of this model x When the load amplitude of the converted vehicle model reproduced in step (4) is corrected using formula t a , the amplitude load that does not meet the conditions remains unchanged; that is, when the maximum value t of the converted vehicle model max =t m +t a >t x hour: in, Represents the load amplitude of the converted vehicle after correction; R(t x ) represents the random upper limit value of the conversion model, which is within the upper limit value t x Random reproduction within a fixed range, but less than the upper limit t x , the fixed range is determined by the gear difference of the power head reducer; t m represents the mean value of the load reproduced in step (5); (7) Correct the mean load according to the minimum lower limit of the converted vehicle model; When the mean load is corrected according to the minimum lower limit of the converted vehicle model, when the minimum value t min When it is less than 0, use the formula to correct the mean load value t of the converted vehicle model reproduced in step (5) m , the mean load that does not meet the conditions remains unchanged; that is, when the minimum value t of the converted vehicle model min =t m -t a <0 o'clock: in, Represents the corrected mean load of the conversion model; R(t0) represents the random lower limit of the conversion model. This value is randomly reproduced within the fixed range of the conversion model lower limit t0, but is greater than the lower limit t0. The fixed range is determined by the gear difference of the power head reducer. If the time series load is greater than 0, t0 can be 0; t a represents the load amplitude reproduced in step (4); (8) Reconstruct the rain flow based on the corrected load cycle amplitude and mean; (9) The time series loads of the original vehicle model and the converted vehicle model are counted and the cumulative probability distribution graphs are drawn respectively, and the mathematical distribution characteristics of the time series loads of the original vehicle model and the converted vehicle model are qualitatively compared to see whether they are consistent. If they are consistent, it means that the time series loads of the converted vehicle model are reasonable. If they are inconsistent, repeat steps (4) to (8) until the mathematical distribution characteristics of the time series loads of the vehicle models before and after the conversion are consistent.

2. The method for converting time series loads of masts of rotary drilling rigs of different models according to claim 1 is characterized in that: The steps for fitting the load amplitude with a Gaussian mixture distribution in step (2) are as follows: First, the load amplitude T of the load cycle a Draw a histogram, divide it into n intervals, and then use Gaussian mixture distribution to fit the histogram. Select the number m of the best Gaussian sub-models based on the goodness of fit. The probability density function expression of Gaussian mixture distribution is as follows: Among them, T a represents the load cycle amplitude load; a i Represents the weight of each sub-model; μ i represents the mean of each sub-model; δ i Represents the standard deviation of each sub-model; i = 1 ~ m.

3. The method for converting time sequence loads of masts of rotary drilling rigs of different models according to claim 2 is characterized in that: The steps for calculating the limit error value of the probability density value of the load amplitude in step (3) are as follows: (3.1) Calculate the absolute value of the difference between the actual probability density values ​​corresponding to the n histogram intervals in step (2) and the fitted Gaussian mixture theory probability density values; (3.2) Fit the absolute values ​​of the differences between the n actual and theoretical probability density values ​​to a normal distribution and calculate the corresponding mean and variance; (3.3) Calculate the limit error value of the load amplitude probability density value based on the calculated mean and standard deviation. The calculation formula of the limit error value is as follows: Where δ represents the limit error value of the probability density value of the load amplitude; σ represents the standard deviation of the normal distribution; μ represents the mean of the normal distribution; n represents the number of intervals of the histogram in step (2); Δt represents the probability of sampling error, and the maximum error value here is 2.1%.

4. The method for converting time sequence loads of masts of rotary drilling rigs of different models according to claim 2 is characterized in that: When the corrected load cycle amplitude and mean are reconstructed in rain flow in step (8), the load cycle is reconstructed according to the four-point rain flow counting method. The specific steps are as follows: (8.1) First, calculate the minimum and maximum values ​​of the load cycles of all converted models and arrange them in the order of minimum, maximum, and minimum, as shown in the following matrix: [t min1 t max2 t min3 ... t min(3m-2) t max(3m-1) t min(3m) ] Where m represents the number of load cycles; (8.2) Then compare t min(3i) and t min(3i+1) Load size: If t min(3i) ≤t min(3i+1) , then retain t min(3i) , remove t min(3i+1) , while t min(3i+1) Subsequent loads are shifted forward one time unit; Otherwise, keep t min(3i+1) , remove t min(3i) , while t min(3i) Subsequent loads are shifted forward one time unit; Where i = 1, 2, ..., m-1, and the last two loads t max(3m-1) and t min(3m) Directly fill in; (8.3) Follow the above steps to reconstruct the time series load of the converted vehicle model.

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