Tower state maintenance judgment method based on fault tree-Monte Carlo theory

Abstract

The invention discloses a tower state maintenance judgment method based on a fault tree-Monte Carlo theory. The method comprises the following steps: S1, constructing a tower fault tree according to tower fault data; s2, acquiring original data of the fault rate of each sub-component of the tower, expanding the original data by adopting a Bootstrap method to obtain an expanded sample, and estimating a Weibull distribution parameter of each bottom event in the tower fault tree according to the expanded sample and by adopting a least square method; s3, establishing a structure function of the tower fault tree, performing Monte Carlo simulation to obtain a tower reliability index and a failure probability, obtaining a reliability curve of the tower according to the tower reliability index and the Weibull distribution parameters, and obtaining a fault probability time distribution diagram of the tower according to the failure probability; and S4, obtaining the maximum probability fault time point of each sub-component of the tower according to the reliability curve of the tower and the fault probability time distribution diagram, and making a maintenance plan. The method is accurate in fitting result under small sample data.

Description

Technical Field

[0001] The invention relates to an improvement of a tower condition maintenance technology, belongs to the electric power field, and in particular to a tower condition maintenance determination method based on a fault tree-Monte Carlo theory. Background Art

[0002] Under the action of natural forces, gravity, stress and other external forces, the foundation of the tower is prone to deformation, causing the tower to tilt, even collapse and disconnection, paralyzing the transmission line and causing significant economic losses. Therefore, it is necessary to regularly inspect the status of the tower. However, in the existing life reliability test and engineering application of electromechanical products, the scarcity of samples poses a challenge to the accurate evaluation of product reliability. To overcome this problem, the academic community has actively explored a variety of engineering methods, including Bayesian method, expansion method and Monte Carlo method. Specifically, some researchers have used Bayesian theory to construct a reliability evaluation model for small samples; other scholars have solved the problem of solving the product life distribution parameters with the help of expansion methods, and calculated the reliability index in combination with traditional methods; and some studies have used Monte Carlo method to analyze the reliability of CNC systems with small sample fault data. Although Bayesian method is widely used in Weibull distribution parameter estimation, it is highly dependent on prior information, which is usually derived from experience and is easily affected by subjective judgment. As the overall reliability of products continues to improve, failure event data is becoming increasingly sparse, and many reliability studies are faced with the problem of severe lack of prior information.

[0003] However, due to the limited factors that cause tower failures, the sample data is relatively small, which limits the applicability of traditional Bayesian methods in small sample reliability assessment scenarios. Therefore, the effective strategy for dealing with small sample reliability assessment is mainly to expand the reliability data and transform the small sample problem into a large sample problem. The Monte Carlo method can infer the distribution shape and estimate parameters based on the existing small sample data, and generate additional sample data accordingly, thereby reducing the dependence on the original small sample. However, this method may lose some data characteristics, and the accuracy of the generated sample data is not easy to guarantee.

[0004] The Chinese patent application with application number CN201310583041.3 and application date of November 19, 2013 discloses that the present invention is a method for evaluating the probability of failure of power equipment, including the following steps: equipment failure probability evaluation; quantitative evaluation of power grid equipment failure factors, mainly evaluating the operation risk of the power grid in abnormal modes such as equipment maintenance, equipment abnormality, or in a certain period of time under specific circumstances, and proposing a method for predicting the failure rate of power equipment in the current state and the failure rate after equipment maintenance, so as to provide a scientific basis for subsequent optimization of equipment status maintenance decisions. However, the above technology does not solve the problem of inaccurate fitting results under small sample data.

[0005] The information disclosed in this background technology section is only intended to increase the understanding of the overall background of this patent application, and should not be regarded as acknowledging or suggesting in any form that the information constitutes the prior art already known to ordinary technicians in this field. Summary of the invention

[0006] The purpose of the present invention is to overcome the problem of inaccurate fitting results under small sample data in the prior art, and to provide a tower condition inspection and maintenance determination method based on the fault tree-Monte Carlo theory with accurate fitting results under small sample data.

[0007] To achieve the above objectives, the technical solution of the present invention is: a tower condition inspection and maintenance determination method based on fault tree-Monte Carlo theory, comprising:

[0008] S1. Construct a tower fault tree according to tower fault data;

[0009] S2. Obtain the original data of the failure rate of each subcomponent of the tower, expand the original data by using the Bootstrap method to obtain an expanded sample, and estimate the Weibull distribution parameters of each bottom event in the tower fault tree by using the least square method based on the expanded sample;

[0010] S3, establishing the structure function of the pole tower fault tree, performing Monte Carlo simulation, obtaining the pole tower reliability index and failure probability, and obtaining the pole tower reliability curve according to the pole tower reliability index and Weibull distribution parameters, and obtaining the pole tower failure probability time distribution diagram according to the failure probability;

[0011] S4. According to the reliability curve of the tower and the failure probability time distribution diagram, the maximum probability failure time point of each sub-component of the tower is obtained, and a maintenance plan is formulated.

[0012] The method of using the Bootstrap method to expand the original data to obtain an expanded sample, and estimating the Weibull distribution parameter of each bottom event in the tower fault tree according to the expanded sample and using the least square method, includes:

[0013] Create an expansion formula:

[0014]

[0015] Where t[n] is the time when the event occurs under the random data set, n is the original data sample size, e[n] is a random data set uniformly distributed between 0 and 1, and α and β are the scale parameter and shape parameter of the Weibull distribution parameter respectively;

[0016] Obtain m groups of expanded samples through the expansion formula;

[0017] By expanding the formula, we can get:

[0018]

[0019]

[0020] Where F(t) is the two-parameter Weibull cumulative failure function; f(t) is the probability density function;

[0021] Simplifying the above equation and linearizing it into the form of y=Bx+A, we can get:

[0022] y = βx - βlnα;

[0023] in

[0024] The fault data t in the m groups of augmented samples i Sort by time from small to large, and use the two-parameter Weibull function to calculate F(t i ), and then calculate m groups x i and i :

[0025] x i =ln(t i );

[0026]

[0027] Among them, x i ,y i They are all intermediate variables in the linearization process of the two-parameter Weibull function;

[0028] According to m group x i Hehey i Calculate h xx ,h yy ,h xy ;

[0029]

[0030]

[0031]

[0032] Among them, h xx 、h yy 、h xy All are intermediate variables;

[0033] Calculate the scale parameter α and shape parameter β;

[0034]

[0035]

[0036] The above method is used to calculate the Weibull distribution parameters of other bottom events in the tower fault tree.

[0037] The structure function of the tower fault tree is:

[0038] X(t)=[b 1 (t), b 2 (t), ..., b n (t)];

[0039] Among them, b i (t) is the binary state variable of the ith bottom event in the tower fault tree. The value range of i is the number of bottom events. is the state variable of the tower failure event at time t. When a tower failure occurs at time t, When there is no tower failure at time t,

[0040] The Monte Carlo simulation includes:

[0041] The Monte Carlo method is used to randomly sample the failure time of each sub-component of the tower, and the sampling value is is the inverse function of the fault distribution function of each bottom event, ηηij is a random number generated in the interval [0, 1] corresponding to the bottom event and the sampling number;

[0042] The state variable of the i-th bottom event of the tower fault tree at the j-th sampling time t can be expressed as:

[0043]

[0044] Substituting the above formula into the structure function of the tower fault tree, we can get the tower state function of the jth sampling at time t;

[0045] In the jth simulation, the time when the tower fails is t kj , using the structure function of the top event of the tower fault tree, the state variable of the top event is defined as:

[0046]

[0047] The failure time of n subcomponents in the tower is t 1j , t 2j , ..., t ij , ..., t nj , according to the order of failure occurrence, a new sequence t is obtained f1 , t f2 , ..., t fk , ..., t fn , the corresponding tower fault tree bottom event sequence is z 1 , z 2, …, z n , where n is the number of bottom events;

[0048] At t = t f1 Invalidate the bottom event at all times, while other events remain normal. At this time, observe whether the top event of the tower is invalid;

[0049] if Then set the bottom event to fail until At this time, the top event of the tower is in a failure state. In the jth simulation, the sampling value of the tower failure occurrence time is t kj =t fk ,At this point, the jth sampling of the tower fault tree is completed, and the above simulation process is repeated to obtain the tower fault time series.

[0050] The tower reliability index is:

[0051]

[0052]

[0053] in, is the reliability index, is the unreliability index, t r is the failure time, φ j (t) is the state variable of the top event;

[0054] The failure probability is:

[0055]

[0056] Where N is the number of simulations, m r is the sum of the state variables of the top events in N simulations.

[0057] A tower condition inspection and maintenance determination system based on fault tree-Monte Carlo theory, comprising:

[0058] A fault tree construction module is used to construct a tower fault tree according to tower fault data;

[0059] The data processing module is used to obtain the original data of the failure rate of each subcomponent of the tower, expand the original data by using the Bootstrap method to obtain an expanded sample, and estimate the Weibull distribution parameter of each bottom event in the tower fault tree by using the least square method based on the expanded sample;

[0060] Monte Carlo simulation module, used to establish the structure function of the tower fault tree, perform Monte Carlo simulation, obtain the tower reliability index and failure probability, and obtain the tower reliability curve based on the tower reliability index and Weibull distribution parameters, and obtain the tower failure probability time distribution diagram based on the failure probability;

[0061] The maintenance plan formulation module is used to obtain the maximum probability failure time point of each sub-component of the tower according to the reliability curve of the tower and the failure probability time distribution diagram, and formulate a maintenance plan.

[0062] The method of using the Bootstrap method to expand the original data to obtain an expanded sample, and estimating the Weibull distribution parameter of each bottom event in the tower fault tree according to the expanded sample and using the least square method, includes:

[0063] Create an expansion formula:

[0064]

[0065] Where t[n] is the time when the event occurs under the random data set, n is the original data sample size, e[n] is a random data set uniformly distributed between 0 and 1, and α and β are the scale parameter and shape parameter of the Weibull distribution parameter respectively;

[0066] Obtain m groups of expanded samples through the expansion formula;

[0067] By expanding the formula, we can get:

[0068]

[0069]

[0070] Where F(t) is the two-parameter Weibull cumulative failure function; f(t) is the probability density function;

[0071] Simplifying the above equation and linearizing it into the form of y=Bx+A, we can get:

[0072] y = βx - βlnα;

[0073] in

[0074] The fault data t in the m groups of augmented samples i Sort by time from small to large, and use the two-parameter Weibull function to calculate F(t i ), and then calculate m groups x i and i :

[0075] x i =ln(t i );

[0076]

[0077] Among them, x i , yi are both intermediate variables in the linearization process of the two-parameter Weibull function;

[0078] According to m group x i and i Calculate h xx ,h yy ,h xy ;

[0079]

[0080]

[0081]

[0082] Among them, h xx 、h yy 、h xy All are intermediate variables;

[0083] Calculate the scale parameter α and shape parameter β;

[0084]

[0085]

[0086] The above method is used to calculate the Weibull distribution parameters of other bottom events in the tower fault tree.

[0087] The structure function of the tower fault tree is:

[0088] X(t)=[b 1 (t), b 2 (t), ..., b n (t)];

[0089] Among them, b i (t) is the binary state variable of the ith bottom event in the tower fault tree. The value range of i is the number of bottom events. is the state variable of the tower failure event at time t. When a tower failure occurs at time t, When there is no tower failure at time t,

[0090] The Monte Carlo simulation includes:

[0091] The Monte Carlo method is used to randomly sample the failure time of each sub-component of the tower, and the sampling value is is the inverse function of the failure distribution function for each bottom event, η ij is a random number generated in the interval [0, 1] corresponding to the bottom event and sampling number;

[0092] The state variable of the i-th bottom event of the tower fault tree at the j-th sampling time t can be expressed as:

[0093]

[0094] Substituting the above formula into the structure function of the tower fault tree, we can get the tower state function of the jth sampling at time t;

[0095] In the jth simulation, the time when the tower fails is t kj , using the structure function of the top event of the tower fault tree, the state variable of the top event is defined as:

[0096]

[0097] The failure time of n subcomponents in the tower is t 1j , t 2j , ..., t ij , ..·, t nj , according to the order of failure occurrence, a new sequence t is obtained f1 , t f2 , ..., t fk , ..., t fn , the corresponding tower fault tree bottom event sequence is z 1 , z 2 , …, z n , where n is the number of bottom events;

[0098] At t = t f1 Invalidate the bottom event at all times, while other events remain normal. At this time, observe whether the top event of the tower is invalid;

[0099] if Then set the bottom event to fail until At this time, the top event of the tower is in a failure state. In the jth simulation, the sampling value of the tower failure occurrence time is t kj =t fk ,At this point, the jth sampling of the tower fault tree is completed, and the above simulation process is repeated to obtain the tower fault time series.

[0100] The tower reliability index is:

[0101]

[0102]

[0103] in, is the reliability index, is the unreliability index, tr is the failure time, φ j (t) is the state variable of the top event;

[0104] The failure probability is:

[0105]

[0106] Where N is the number of simulations, m r is the sum of the state variables of the top events in N simulations.

[0107] A tower condition inspection and maintenance judgment device based on fault tree-Monte Carlo theory, comprising a memory and a processor

[0108] The memory is used to store computer program code and transmit the computer program code to the processor;

[0109] The processor is used to execute the above-mentioned tower condition inspection and maintenance determination method based on the fault tree-Monte Carlo theory according to the instructions in the computer program code.

[0110] A computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the method for determining the state of a tower based on fault tree-Monte Carlo theory is implemented.

[0111] Compared with the prior art, the present invention has the following beneficial effects:

[0112] 1. In a tower state inspection and determination method based on the fault tree-Monte Carlo theory of the present invention, a transmission tower reliability assessment scheme that integrates the Monte Carlo method and the expansion method is used. The obtained reliability model can be more closely matched to the actual model of field data fitting, so that the accuracy of small sample fault data is improved, which has significant practical value for accurately evaluating the service life of transmission towers and effectively improving their operational reliability, and can provide data support for later equipment health status evaluation, inspection and maintenance decision-making. Therefore, the fitting result of the present invention under small sample data is accurate.

[0113] 2. In the tower condition inspection and determination method based on the fault tree-Monte Carlo theory of the present invention, data expansion can adopt a variety of means, such as adding random noise to the original data, performing data transformation, using a model-based method to generate new data, etc., to simulate more possible samples, so that the model is exposed to more diverse data during the training process, thereby improving the model's ability to process unseen data. Therefore, the present invention improves the model's ability to process data.

[0114] 3. The tower status inspection and determination method based on the fault tree-Monte Carlo theory of the present invention can provide a solid data foundation for the health status evaluation of the transmission tower, and provide powerful guidance for the subsequent equipment inspection and operation and maintenance decision-making. It can help the power industry to more accurately predict equipment failures and reasonably plan the inspection and maintenance cycle, thereby effectively improving the operating efficiency and safety of the power grid, which is of great significance for ensuring the stable operation of the power system. Therefore, the present invention accurately predicts equipment failures and reasonably plans the inspection and maintenance cycle. BRIEF DESCRIPTION OF THE DRAWINGS

[0115] Figure 1 It is a flow chart of the present invention.

[0116] Figure 2 It is a schematic diagram of the fault tree in the present invention.

[0117] Figure 3 It is the reliability curve of the transmission tower of the present invention.

[0118] Figure 4 This is a probability distribution diagram of transmission tower failures of the present invention.

[0119] Figure 5 It is a structural block diagram of the system of the present invention.

[0120] Figure 6 It is a structural block diagram of the device of the present invention. DETAILED DESCRIPTION

[0121] The present invention is further described in detail below in conjunction with the accompanying drawings and specific implementation methods.

[0122] Embodiment 1:

[0123] See also Figure 1 to Figure 2 , a tower condition maintenance determination method based on fault tree-Monte Carlo theory, including

[0124] S1. Construct a tower fault tree according to tower fault data;

[0125] S2. Obtain the original data of the failure rate of each subcomponent of the tower, expand the original data by using the Bootstrap method to obtain an expanded sample, and estimate the Weibull distribution parameters of each bottom event in the tower fault tree by using the least square method based on the expanded sample;

[0126] S3, establishing the structure function of the pole tower fault tree, performing Monte Carlo simulation, obtaining the pole tower reliability index and failure probability, and obtaining the pole tower reliability curve according to the pole tower reliability index and Weibull distribution parameters, and obtaining the pole tower failure probability time distribution diagram according to the failure probability;

[0127] S4. According to the reliability curve of the tower and the failure probability time distribution diagram, the maximum probability failure time point of each sub-component of the tower is obtained, and a maintenance plan is formulated.

[0128] When applied: the original data set is expanded using the expansion method, and the obtained reliability model can be closer to the actual model fitted by the field data, which solves the problem of inaccurate fitting results of the traditional Monte Carlo method under small sample data, and can perform reliability evaluation on the working condition of the transmission tower.

[0129] Embodiment 2:

[0130] Embodiment 2 is substantially the same as Embodiment 1, except that:

[0131] The method of using the Bootstrap method to expand the original data to obtain an expanded sample, and estimating the Weibull distribution parameter of each bottom event in the tower fault tree according to the expanded sample and using the least square method, includes:

[0132] Create an expansion formula:

[0133]

[0134] Among them, t[n] is the time when the event occurs under the random data set, n is the original data sample size, e[n] is a random data set uniformly distributed between 0 and 1, and the random data set is a uniformly distributed set. The random data set is brought into the expansion formula containing the original scale parameter and shape parameter to solve the expanded event occurrence time set. α and β are the scale parameter and shape parameter of the Weibull distribution parameter respectively;

[0135] Obtain m groups of expanded samples through the expansion formula;

[0136] By expanding the formula, we can get:

[0137]

[0138]

[0139] Where F(t) is the two-parameter Weibull cumulative failure function; f(t) is the probability density function;

[0140] Simplifying the above equation and linearizing it into the form of y=Bx+A, we can get:

[0141] y = βx - βlnα;

[0142] in x = lnt;

[0143] The fault data t in the m groups of augmented samples i Sort by time from small to large, and use the two-parameter Weibull function to calculate F(ti ), and then calculate m groups x i and i :

[0144] x i =ln(t i );

[0145]

[0146] Among them, x i , yi are both intermediate variables in the linearization process of the two-parameter Weibull function;

[0147] According to m group x i and i Calculate h xx ,h yy ,h xy ;

[0148]

[0149]

[0150]

[0151] Among them, h xx 、h yy 、h xy All are intermediate variables;

[0152] Calculate the scale parameter α and shape parameter β;

[0153]

[0154]

[0155] The above method is used to calculate the Weibull distribution parameters of other bottom events in the tower fault tree.

[0156] When applied: construct a fault tree, then expand the original fault data from the actual data of the tower in the past, and then use theoretical symbols and logical reasoning to systematically decompose and analyze the factors that cause the top fault event layer by layer, and then calculate the Weibull distribution parameters of all time to provide a basis for the subsequent reliability curve.

[0157] Embodiment 3:

[0158] Embodiment 3 is substantially the same as Embodiment 1, except that:

[0159] The structure function of the tower fault tree is:

[0160] X(t)=[b 1 (t), b2 (t), ..., b n (t)];

[0161] Among them, b i (t) is the binary state variable of the ith bottom event in the tower fault tree. The value range of i is the number of bottom events. is the state variable of the tower failure event at time t. When a tower failure occurs at time t, When there is no tower failure at time t,

[0162] The Monte Carlo simulation includes:

[0163] The Monte Carlo method is used to randomly sample the failure time of each sub-component of the tower, and the sampling value is is the inverse function of the failure distribution function for each bottom event, η ij is a random number generated in the interval [0, 1] corresponding to the bottom event and sampling number;

[0164] The state variable of the i-th bottom event of the tower fault tree at the j-th sampling time t can be expressed as:

[0165]

[0166] Substituting the above formula into the structure function of the tower fault tree, we can get the tower state function of the jth sampling at time t;

[0167] In the jth simulation, the time when the tower fails is t kj , using the structure function of the top event of the tower fault tree, the state variable of the top event is defined as:

[0168]

[0169] The failure time of n subcomponents in the tower is t 1j , t 2j , ..., t ij , ..., t nj , according to the order of failure occurrence, a new sequence t is obtained f1 , t f2 , ..., t fk , ..., t fn , the corresponding tower fault tree bottom event sequence is z 1 , z2,…,z n , where n is the number of bottom events;

[0170] At t = t f1 Invalidate the bottom event at all times, while other events remain normal. At this time, observe whether the top event of the tower is invalid;

[0171] if Then set the bottom event to fail until At this time, the top event of the tower is in a failure state. In the jth simulation, the sampling value of the tower failure occurrence time is t kj =t fk At this point, the jth sampling of the tower fault tree is completed, and the above simulation process is repeated N (j = 1, 2, ..., N) times to obtain the tower fault time series.

[0172] The tower reliability index is:

[0173]

[0174]

[0175] in, is the reliability index, is the unreliability index, t r is the failure time, φ j (t) is the state variable of the top event;

[0176] See also Figure 3 , Figure 3 The reliability curve of the transmission tower shows the curve of the reliability of the transmission tower system changing with time (h), wherein the time interval is 2000h. In the first 6000h, the reliability is 1, indicating that the early system operates normally without failure. The reliability slowly decreases from 6000h to 7000h, and then decreases rapidly. After 10000h, it slowly decreases until it approaches 12000h, when the reliability is close to 0, indicating that the system eventually stops running due to a fault. The failure time of the transmission tower obtained by the present invention obeys the Weibull distribution α=5854, β=3.4;

[0177] The failure probability is:

[0178]

[0179] Where N is the number of simulations, m r is the sum of the state variables of the top events in N simulations;

[0180] See also Figure 4 , Figure 4 It is the transmission tower failure probability distribution diagram, which shows the distribution curve of the transmission tower failure probability density over time h; the failure probability density function is the failure probability of the system per unit time;

[0181] As the working time of each component of the transmission tower increases, its unreliability index increases. When the working time of a component is long enough, its unreliability index is equal to 1, which means it is 100% disabled.

[0182] When applied: By obtaining the reliability curve and failure probability time distribution of the tower, the time point with the highest probability of failure can be clearly obtained from the failure probability time distribution diagram. By calculating the reliability curve of each component, the maximum probability failure time point of each component can be obtained, so as to reasonably arrange the maintenance plan.

[0183] Embodiment 4:

[0184] See also Figure 5 , a tower condition inspection and maintenance judgment system based on fault tree-Monte Carlo theory, including:

[0185] A fault tree construction module is used to construct a tower fault tree according to tower fault data;

[0186] The data processing module is used to obtain the original data of the failure rate of each subcomponent of the tower, expand the original data by using the Bootstrap method to obtain an expanded sample, and estimate the Weibull distribution parameter of each bottom event in the tower fault tree by using the least square method based on the expanded sample;

[0187] Monte Carlo simulation module, used to establish the structure function of the tower fault tree, perform Monte Carlo simulation, obtain the tower reliability index and failure probability, and obtain the tower reliability curve based on the tower reliability index and Weibull distribution parameters, and obtain the tower failure probability time distribution diagram based on the failure probability;

[0188] The maintenance plan formulation module is used to obtain the maximum probability failure time point of each sub-component of the tower according to the reliability curve of the tower and the failure probability time distribution diagram, and formulate a maintenance plan.

[0189] The method of using the Bootstrap method to expand the original data to obtain an expanded sample, and estimating the Weibull distribution parameter of each bottom event in the tower fault tree according to the expanded sample and using the least square method, includes:

[0190] Create an expansion formula:

[0191]

[0192] Where t[n] is the time when the event occurs under the random data set, n is the original data sample size, e[n] is a random data set uniformly distributed between 0 and 1, and α and β are the scale parameter and shape parameter of the Weibull distribution parameter respectively;

[0193] Obtain m groups of expanded samples through the expansion formula;

[0194] By expanding the formula, we can get:

[0195]

[0196]

[0197] Where F(t) is the two-parameter Weibull cumulative failure function; f(t) is the probability density function;

[0198] The above formula is simplified to:

[0199] y = βx - βlnα;

[0200] The fault data t in the m groups of augmented samples i Sort by time from small to large, and use the two-parameter Weibull function to calculate F(t i ), and then calculate m groups x i and i :

[0201] x i =lnn(t i );

[0202]

[0203] Among them, x i , yi are both intermediate variables in the linearization process of the two-parameter Weibull function;

[0204] According to m group x i and i Calculate h xx ,h yy ,h xy ;

[0205]

[0206]

[0207]

[0208] Among them, h xx 、h yy 、h xy All are intermediate variables;

[0209] Calculate the scale parameter α and shape parameter β;

[0210]

[0211]

[0212] The above method is used to calculate the Weibull distribution parameters of other bottom events in the tower fault tree.

[0213] The structure function of the tower fault tree is:

[0214] X(t)=[b 1 (t), b2 (t), ..., b n (t)];

[0215] Among them, b i (t) is the binary state variable of the ith bottom event in the tower fault tree. The value range of i is the number of bottom events. is the state variable of the tower failure event at time t. When a tower failure occurs at time t, When there is no tower failure at time t,

[0216] The Monte Carlo simulation includes:

[0217] The Monte Carlo method is used to randomly sample the failure time of each sub-component of the tower, and the sampling value is is the inverse function of the failure distribution function for each bottom event, η ij is a random number generated in the interval [0, 1] corresponding to the bottom event and sampling number;

[0218] Then the state variable of the i-th bottom event of the tower fault tree at the j-th sampling time t can be expressed as:

[0219]

[0220] Substituting the above formula into the structure function of the tower fault tree, we can get the tower state function of the jth sampling at time t;

[0221] In the jth simulation, the time when the tower fails is t kj , using the structure function of the top event of the tower fault tree, the state variable of the top event is defined as:

[0222]

[0223] The failure time of n subcomponents in the tower is t 1j , t 2j , ..., t ij , ..., t nj , according to the order of failure occurrence, a new sequence t is obtained f1 , t f2 , ..., t fk , ..., t fn , the corresponding tower fault tree bottom event sequence is z 1 , z 2 , …, z n , where n is the number of bottom events;

[0224] At t = t f1 Invalidate the bottom event at all times, while other events remain normal. At this time, observe whether the top event of the tower is invalid;

[0225] if Then set the bottom event to fail until At this time, the top event of the tower is in a failure state. In the jth simulation, the sampling value of the tower failure occurrence time is t kj =t fk ,At this point, the jth sampling of the tower fault tree is completed, and the above simulation process is repeated to obtain the tower fault time series.

[0226] The tower reliability index is:

[0227]

[0228]

[0229] in, is the reliability index, is the unreliability index, tr is the failure time, φ j (t) is the state variable of the top event;

[0230] The failure probability is:

[0231]

[0232] Where N is the number of simulations, m r is the sum of the state variables of the top events in N simulations.

[0233] Embodiment 5:

[0234] See also Figure 6 , a tower condition inspection and maintenance determination device based on fault tree-Monte Carlo theory, comprising a memory and a processor;

[0235] The memory is used to store computer program code and transmit the computer program code to the processor;

[0236] The processor is used to execute the tower condition inspection and maintenance determination method based on the fault tree-Monte Carlo theory described in Example 1 according to the instructions in the computer program code.

[0237] Embodiment 6:

[0238] A computer-readable storage medium having a computer program stored thereon. When the computer program is executed by a processor, the method for determining a tower condition inspection and maintenance based on a fault tree-Monte Carlo theory described in Example 1 is implemented. The computer-readable storage medium stores computer instructions, and the computer instructions enable the computer to implement all or part of the steps of the method described in the embodiment of the present invention. The storage medium includes various media that can store program codes, such as a USB flash drive, a mobile hard disk, a read-only memory ROM, a random access memory RAM, a magnetic disk or an optical disk.

[0239] Generally speaking, the computer instructions for implementing the method of the present invention may be carried by any combination of one or more computer-readable storage media. Non-transitory computer-readable storage media may include any computer-readable media, except for the signal itself that is temporarily propagating.

[0240] The computer-readable storage medium may be, for example, but not limited to, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, device or device, or any combination thereof. A non-exhaustive list of more specific examples of computer-readable storage media includes: an electrical connection with one or more wires, a portable computer disk, a hard disk, a random access memory RAM, a read-only memory (ROM), an erasable programmable read-only memory (EKROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination thereof. In the present invention, a computer-readable storage medium may be any tangible medium containing or storing a program that may be used by or in conjunction with an instruction execution system, device, or device.

[0241] Computer program code for performing the operation of the present invention can be written in one or more programming languages ​​or a combination thereof, including object-oriented programming languages ​​such as Java, SMalltalk, C++, and conventional procedural programming languages ​​such as "C" or similar programming languages, in particular, Python suitable for neural network computing and platform frameworks based on TensorFlow, PyTorch, etc. can be used. The program code can be executed entirely on the user's computer, partially on the user's computer, as an independent software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In the case of a remote computer, the remote computer can be connected to the user's computer through any type of network, including a local area network (LAN) or a wide area network (WAN), or connected to an external computer, such as through the Internet using an Internet service provider.

[0242] The above-mentioned device and non-transitory computer-readable storage medium can refer to the specific description of a tower condition maintenance determination method based on fault tree-Monte Carlo theory and its beneficial effects, which will not be repeated here.

[0243] Although the embodiments of the present invention have been shown and described above, it should be understood that the above embodiments are exemplary and should not be construed as limitations on the present invention. A person skilled in the art may change, modify, replace and vary the above embodiments within the scope of the present invention.

Claims

1. A tower condition maintenance determination method based on fault tree-Monte Carlo theory, characterized in that: include S1. Construct a tower fault tree according to tower fault data; S2. Obtain the original data of the failure rate of each subcomponent of the tower, expand the original data by using the Bootstrap method to obtain an expanded sample, and estimate the Weibull distribution parameters of each bottom event in the tower fault tree by using the least square method based on the expanded sample; S3, establishing the structure function of the pole tower fault tree, performing Monte Carlo simulation, obtaining the pole tower reliability index and failure probability, and obtaining the pole tower reliability curve according to the pole tower reliability index and Weibull distribution parameters, and obtaining the pole tower failure probability time distribution diagram according to the failure probability; S4. According to the reliability curve of the tower and the failure probability time distribution diagram, the maximum probability failure time point of each sub-component of the tower is obtained, and a maintenance plan is formulated.

2. The tower condition inspection and maintenance determination method based on the fault tree-Monte Carlo theory according to claim 1 is characterized in that: The method of using the Bootstrap method to expand the original data to obtain an expanded sample, and estimating the Weibull distribution parameter of each bottom event in the tower fault tree according to the expanded sample and using the least square method, includes: Create an expansion formula: Where t[n] is the time when the event occurs under the random data set, n is the original data sample size, e[n] is a random data set uniformly distributed between 0 and 1, and α and β are the scale parameter and shape parameter of the Weibull distribution parameter respectively; Obtain m groups of expanded samples through the expansion formula; By expanding the formula, we can get: Where F(t) is the two-parameter Weibull cumulative failure function; f(t) is the probability density function; Simplifying the above equation and linearizing it into the form of y=Bx+A, we can get: y = βx - βlnα; in The fault data t in the m groups of augmented samples i Sort by time from small to large, and use the two-parameter Weibull function to calculate F(t i ), and then calculate m groups x i and i : x i =ln(t i ); Among them, x i , yi are both intermediate variables in the linearization process of the two-parameter Weibull function; According to m group x i and i Calculate h xx ,h yy ,h xy ; Among them, h xx 、h yy 、h xy All are intermediate variables; Calculate the scale parameter α and shape parameter β; The above method is used to calculate the Weibull distribution parameters of other bottom events in the tower fault tree.

3. The tower condition inspection and maintenance determination method based on the fault tree-Monte Carlo theory according to claim 1 is characterized in that: The structure function of the tower fault tree is: X(t)=[b1(t),b2(t),...,b n (t)]; Among them, b i (t) is the binary state variable of the ith bottom event in the tower fault tree. The value range of i is the number of bottom events. is the state variable of the tower failure event at time t. When a tower failure occurs at time t, When there is no tower failure at time t, The Monte Carlo simulation includes: The Monte Carlo method is used to randomly sample the failure time of each sub-component of the tower, and the sampling value is is the inverse function of the failure distribution function for each bottom event, η ij is a random number generated in the interval [0, 1] corresponding to the bottom event and sampling number; The state variable of the i-th bottom event of the tower fault tree at the j-th sampling time t can be expressed as: Substituting the above formula into the structure function of the tower fault tree, we can get the tower state function of the jth sampling at time t; In the jth simulation, the time when the tower fails is t kj , using the structure function of the top event of the tower fault tree, the state variable of the top event is defined as: The failure time of n subcomponents in the tower is t 1j , t 2j , ..., t ij , ..., t nj , according to the order of fault occurrence, a new sequence t is obtained f1 , t f2 , ..., t fk , ..., t fn , the corresponding tower fault tree bottom event sequence is z1, z2, …, z n , where n is the number of bottom events; At t = t f1 Invalidate the bottom event at all times, while other events remain normal. At this time, observe whether the top event of the tower is invalid; if Then set the bottom event to fail until At this time, the top event of the tower is in a failure state. In the jth simulation, the sampling value of the tower failure occurrence time is t kj =t fk ,At this point, the jth sampling of the tower fault tree is completed, and the above simulation process is repeated to obtain the tower fault time series.

4. The tower condition inspection and maintenance determination method based on the fault tree-Monte Carlo theory according to claim 3 is characterized in that: The tower reliability index is: in, is the reliability index, is the unreliability index, t r is the failure time, φ j (t) is the state variable of the top event; The failure probability is: Among them, N is the number of simulations, mm, and is the sum of the state variables of the top events in N simulations.

5. A tower condition inspection and maintenance judgment system based on fault tree-Monte Carlo theory, characterized in that: include: A fault tree construction module is used to construct a tower fault tree according to tower fault data; The data processing module is used to obtain the original data of the failure rate of each subcomponent of the tower, expand the original data by using the Bootstrap method to obtain an expanded sample, and estimate the Weibull distribution parameter of each bottom event in the tower fault tree by using the least square method based on the expanded sample; Monte Carlo simulation module, used to establish the structure function of the tower fault tree, perform Monte Carlo simulation, obtain the tower reliability index and failure probability, and obtain the tower reliability curve based on the tower reliability index and Weibull distribution parameters, and obtain the tower failure probability time distribution diagram based on the failure probability; The maintenance plan formulation module is used to obtain the maximum probability failure time point of each sub-component of the tower according to the reliability curve of the tower and the failure probability time distribution diagram, and formulate a maintenance plan.

6. A tower condition inspection and maintenance determination system based on fault tree-Monte Carlo theory according to claim 5, characterized in that: The method of using the Bootstrap method to expand the original data to obtain an expanded sample, and estimating the Weibull distribution parameter of each bottom event in the tower fault tree according to the expanded sample and using the least square method, includes: Create an expansion formula: Where t[n] is the time when the event occurs under the random data set, n is the original data sample size, e[n] is a random data set uniformly distributed between 0 and 1, and α and β are the scale parameter and shape parameter of the Weibull distribution parameter respectively; Obtain m groups of expanded samples through the expansion formula; By expanding the formula, we can get: Where F(t) is the two-parameter Weibull cumulative failure function; f(t) is the probability density function; Simplifying the above equation and linearizing it into the form of y=Bx+A, we can get: y = βx - βlnα; in The fault data t in the m groups of augmented samples i Sort by time from small to large, and use the two-parameter Weibull function to calculate F(t i ), and then calculate m groups x i and i : x i =ln(t i ); Among them, x i , yi are both intermediate variables in the linearization process of the two-parameter Weibull function; According to m group x i and i Calculate h xx ,h yy ,h xy ; Among them, h xx 、h yy 、h xy All are intermediate variables; Calculate the scale parameter α and shape parameter β; The above method is used to calculate the Weibull distribution parameters of other bottom events in the tower fault tree.

7. The tower condition inspection and maintenance determination system based on the fault tree-Monte Carlo theory according to claim 5 is characterized in that: The structure function of the tower fault tree is: X(t)=[b1(t),b2(t),...,b n (t)]; Among them, b i (t) is the binary state variable of the ith bottom event in the tower fault tree. The value range of i is the number of bottom events. is the state variable of the tower failure event at time t. When a tower failure occurs at time t, When there is no tower failure at time t, The Monte Carlo simulation includes: The Monte Carlo method is used to randomly sample the failure time of each sub-component of the tower, and the sampling value is is the inverse function of the failure distribution function for each bottom event, ηηi j is a random number generated in the interval [0, 1] corresponding to the bottom event and sampling number; Then the state variable of the i-th bottom event of the tower fault tree at the j-th sampling time t can be expressed as: Substituting the above formula into the structure function of the tower fault tree, we can get the tower state function of the jth sampling at time t; In the jth simulation, the time when the tower fails is t kj , using the structure function of the top event of the tower fault tree, the state variable of the top event is defined as: The failure time of n subcomponents in the tower is t 1j , t 2j , ..., t ij , ..., t nj , according to the order of failure occurrence, a new sequence t is obtained f1 , t f2 , ..., t fk , ..., t fn , the corresponding tower fault tree bottom event sequence is z1, z2, …, z n , where n is the number of bottom events; At t = t f1 Invalidate the bottom event at all times, while other events remain normal. At this time, observe whether the top event of the tower is invalid; if Then set the bottom event to fail until At this time, the top event of the tower is in a failure state. In the jth simulation, the sampling value of the tower failure occurrence time is t kj =t fk ,At this point, the jth sampling of the tower fault tree is completed, and the above simulation process is repeated to obtain the tower fault time series.

8. The tower condition inspection and maintenance determination system based on the fault tree-Monte Carlo theory according to claim 7 is characterized in that: The tower reliability index is: in, is the reliability index, is the unreliability index, t r is the failure time, φ j (t) is the state variable of the top event; The failure probability is: Where N is the number of simulations, m r is the sum of the state variables of the top events in N simulations.

9. A tower condition inspection and maintenance judgment device based on fault tree-Monte Carlo theory, characterized in that: including memory and processor; The memory is used to store computer program code and transmit the computer program code to the processor; The processor is used to execute a tower condition inspection and maintenance determination method based on the fault tree-Monte Carlo theory as described in any one of claims 1 to 4 according to the instructions in the computer program code.

10. A computer-readable storage medium having a computer program stored thereon, wherein when the computer program is executed by a processor, the method for determining the tower condition inspection and maintenance based on the fault tree-Monte Carlo theory according to any one of claims 1 to 4 is implemented.

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