CN112668867A - Equipment fault rate evaluation method and system based on field data volume - Google Patents
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Abstract
The invention discloses a method and a system for evaluating equipment fault rate based on field data volume, and belongs to the technical field of power equipment state evaluation. The method comprises the following steps: determining a fault mode corresponding to the state quantity of the target equipment and determining a probability distribution density function f of the field measured value of the state quantity of the whole equipmentCA(x) (ii) a Determining a probability distribution density function f of a field measured value of a state quantity of a faulty deviceCB(x) (ii) a Probability distribution density function f according to overall equipment state quantity valueCA(x) Probability distribution density function f of fault equipment state quantity valueCB(x) Determining a probability distribution density function f of a failure threshold corresponding to the state quantityD(y,μ,σ2) And solving the expected value mu and variance sigma of the function2(ii) a Calculating the individual equipment by using the current value of the state quantity of the individual equipment and the probability density distribution function of the fault threshold valueThe failure rate of (c).
Description
Technical Field
The invention relates to the technical field of power equipment state evaluation, in particular to an equipment fault rate evaluation method and system based on field data volume.
Background
The health state of the power transmission and transformation equipment is crucial to the safe operation of the power grid, and the power equipment with poor health state seriously threatens the safe operation level of the power grid and even causes power grid accidents. How to accurately evaluate the quality of the power equipment, find potential defects of the power equipment in time, avoid accidents, improve the availability of the equipment to the maximum extent and prolong the service life of the equipment has become an important subject of the power industry.
Currently, for the evaluation of the equipment state, the evaluation of the current state of the equipment is mainly focused, and then a corresponding maintenance strategy is adopted according to the evaluation result of the current state. At present, in the maintenance work of the power equipment, the state evaluation guide rule is a direct phenomenon which quantitatively reflects the running condition of the equipment or an indirect parameter obtained by a test means, and is compared with an attention value in the guide rule or a regulation, and finally an evaluation result is obtained through a preset grading model. For example, in the "Q/GDW 169-2008 oil-immersed transformer (reactor) state evaluation guide", the state of the transformer is divided into four states, namely a normal state, an attention state, an abnormal state, a severe state, and the like, and it is specified how to deduct the values of various current state parameters of the transformer, and further, the current state of the transformer is evaluated according to the deducted values of the state of the transformer.
The state evaluation system commonly used in the field at present has the following main defects:
1) the device status is characterized by a deduction value. The deduction value depends on the importance degree of the fault mode corresponding to the equipment state quantity, and the relative size between the numerical value of the state quantity and the diagnosis threshold value; the deduction value is selected in a grading mode (namely, the deduction value is roughly divided into normal level, I level, II level, III level and IV level according to the severity of the degradation of the equipment state quantity), and the deduction value is not fine and accurate enough;
2) dividing the severity of the equipment state quantity deterioration into a standard and a diagnosis threshold value, and also taking related standards and specifications as bases, wherein most of the diagnosis threshold values in the standards come from expert experience and are not accurate enough, and a diagnosis criterion based on an actual data mathematical statistic result of the equipment is lacked;
3) in the existing standard, the deducted values are used for representing the equipment states, the physical significance of the deducted values is not exact enough, the equivalence among the deducted values caused by different state quantities is not obvious enough, and the equipment state evaluation result based on the deducted values is not accurate enough.
Disclosure of Invention
In order to solve the problems, the invention provides an equipment fault rate evaluation method based on field data volume, which comprises the following steps:
aiming at a class of target equipment of which the state quantity is related to a certain fault mode of the equipment and the probability of the equipment having the fault is higher as the field measured data of the state quantity is larger, determining the fault mode corresponding to the state quantity of the target equipment and determining the probability distribution density function f of the field measured value of the state quantity of the whole equipmentCA(x)；
Determining a probability distribution density function f of a field measured value of a state quantity of a faulty deviceCB(x)；
Probability distribution density function f according to overall equipment state quantity valueCA(x) Probability distribution density function f of fault equipment state quantity valueCB(x) Determining a probability distribution density function f of a failure threshold corresponding to the state quantityD(y,μ,σ2) And solving the expected value mu and variance sigma of the function2；
And calculating the failure rate of the individual equipment by using the current value of the state quantity of the individual equipment and the probability density distribution function of the failure threshold value.
Optionally, the fault threshold and the state quantity have the same dimension.
Optionally, the probability distribution density function f of the field actual measurement value of the state quantity of the whole equipmentCA(x) The method is obtained from a sample set A by using the existing mathematical statistical method:
for the target equipment of the type, equipment in various states is randomly selected, and specific numerical values of state quantities of the equipment are actually measured on site to form a sample set A; where n is the total number of device samples in sample set a and m is the number of failed device samples in sample set a.
Optionally, the probability distribution density function f of the field measured value of the state quantity of the fault equipmentCB(x) The following were obtained from sample set B using existing mathematical statistical methods:
for the target equipment of the type, equipment in a fault state is randomly selected, and specific numerical values of state quantities of the equipment are measured on site to form a sample set B.
Optionally, probability distribution density function f of fault thresholdD(y,μ,σ2) Determining as a function f of the probability distribution density of the state quantities of the entire installationCA(x) Have the same functional form.
Optionally, solving the probability distribution density function f of the fault thresholdD(y,μ,σ2) Expected value μ and variance σ of2The following formula is used:
wherein y is a specific numerical value of the fault threshold, and x is a specific numerical value of the state quantity.
Optionally, the determination formula of the individual failure rate of the device is as follows:
where λ is the failure rate, x0Measured data of the state quantity of the individual equipment.
The invention also provides an equipment failure rate evaluation system based on the field data volume, which comprises the following steps:
the first modeling module is used for determining a fault mode corresponding to the state quantity of the target equipment and determining a probability distribution density function f of the state quantity field actual measurement value of the whole equipment according to the target equipment of the type that the state quantity is related to a certain fault mode of the equipment and the probability of the equipment having the fault is higher when the field actual measurement data of the state quantity is largerCA(x)；
A second modeling module for determining the field measured value of the state quantity of the faulty equipmentProbability distribution density function fCB(x)；
A calculation module for calculating a probability distribution density function f according to the values of the state quantities of the whole equipmentCA(x) Probability distribution density function f of fault equipment state quantity valueCB(x) Determining a probability distribution density function f of a failure threshold corresponding to the state quantityD(y,μ,σ2) And solving the expected value mu and variance sigma of the function2；
And the evaluation module is used for calculating the fault rate of the equipment individual by utilizing the current value of the state quantity of the equipment individual and the probability density distribution function of the fault threshold.
Optionally, the fault threshold and the state quantity have the same dimension.
Optionally, the probability distribution density function f of the field actual measurement value of the state quantity of the whole equipmentCA(x) The method is obtained from a sample set A by using the existing mathematical statistical method:
for the target equipment of the type, equipment in various states is randomly selected, and specific numerical values of state quantities of the equipment are actually measured on site to form a sample set A; where n is the total number of device samples in sample set a and m is the number of failed device samples in sample set a.
Optionally, the probability distribution density function f of the field measured value of the state quantity of the fault equipmentCB(x) The following were obtained from sample set B using existing mathematical statistical methods:
for the target equipment of the type, equipment in a fault state is randomly selected, and specific numerical values of state quantities of the equipment are measured on site to form a sample set B.
Optionally, probability distribution density function f of fault thresholdD(y,μ,σ2) Determining as a function f of the probability distribution density of the state quantities of the entire installationCA(x) Have the same functional form.
Optionally, solving the probability distribution density function f of the fault thresholdD(y,μ,σ2) Expected value μ and variance σ of2The following formula is used:
wherein y is a specific numerical value of the fault threshold, and x is a specific numerical value of the state quantity.
Optionally, the determination formula of the individual failure rate of the device is as follows:
where λ is the failure rate, x0Measured data of the state quantity of the individual equipment.
Compared with the prior art, the determination result is more accurate.
Drawings
FIG. 1 is a flow chart of the method of the present invention;
FIG. 2 is a graph of stress-intensity interference relationships according to an embodiment of the present invention;
fig. 3 is a block diagram of the system of the present invention.
Detailed Description
The exemplary embodiments of the present invention will now be described with reference to the accompanying drawings, however, the present invention may be embodied in many different forms and is not limited to the embodiments described herein, which are provided for complete and complete disclosure of the present invention and to fully convey the scope of the present invention to those skilled in the art. The terminology used in the exemplary embodiments illustrated in the accompanying drawings is not intended to be limiting of the invention. In the drawings, the same units/elements are denoted by the same reference numerals.
Unless otherwise defined, terms (including technical and scientific terms) used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention belongs. Further, it will be understood that terms, such as those defined in commonly used dictionaries, should be interpreted as having a meaning that is consistent with their meaning in the context of the relevant art and will not be interpreted in an idealized or overly formal sense.
The invention provides an equipment fault evaluation method based on field data volume, as shown in figure 1, comprising the following steps:
aiming at a class of target equipment of which the state quantity is related to a certain fault mode of the equipment and the probability of the equipment having the fault is higher as the field measured data of the state quantity is larger, determining the fault mode corresponding to the state quantity of the target equipment and determining the probability distribution density function f of the field measured value of the state quantity of the whole equipmentCA(x)；
Determining a probability distribution density function f of a field measured value of a state quantity of a faulty deviceCB(x)；
Probability distribution density function f according to overall equipment state quantity valueCA(x) Probability distribution density function f of fault equipment state quantity valueCB(x) Determining a probability distribution density function f of a failure threshold corresponding to the state quantityD(y,μ,σ2) And solving the expected value mu and variance sigma of the function2；
And calculating the failure rate of the individual equipment by using the current value of the state quantity of the individual equipment and the probability density distribution function of the failure threshold value.
Optionally, the fault threshold and the state quantity have the same dimension.
Optionally, the probability distribution density function f of the field actual measurement value of the state quantity of the whole equipmentCA(x) The method is obtained from a sample set A by using the existing mathematical statistical method:
for the target equipment of the type, equipment in various states is randomly selected, and specific numerical values of state quantities of the equipment are actually measured on site to form a sample set A; where n is the total number of device samples in sample set a and m is the number of failed device samples in sample set a.
Optionally, the probability distribution density function f of the field measured value of the state quantity of the fault equipmentCB(x) The following were obtained from sample set B using existing mathematical statistical methods:
for the target equipment of the type, equipment in a fault state is randomly selected, and specific numerical values of state quantities of the equipment are measured on site to form a sample set B.
Optionally, probability distribution density function f of fault thresholdD(y,μ,σ2) Determining as a function f of the probability distribution density of the state quantities of the entire installationCA(x) Have the same functional form.
Optionally, solving the probability distribution density function f of the fault thresholdD(y,μ,σ2) Expected value μ and variance σ of2The following formula is used:
wherein y is a specific numerical value of the fault threshold, and x is a specific numerical value of the state quantity.
Optionally, the determination formula of the individual failure rate of the device is as follows:
where λ is the failure rate, x0Measured data of the state quantity of the individual equipment.
The invention is further illustrated by the following examples:
suppose that: for a certain type of equipment, the state quantity C of the equipment is related to a certain fault mode of the equipment, the field measured data of the state quantity C is larger than zero, and the larger x is, the larger the probability of the equipment having the fault is; the value x of the state quantity C of the field equipment has a probability distribution density function fCA(x)；
Suppose that: for the equipment and the fault mode, a fault threshold value D exists, and the fault threshold value D and the state quantity C have the same dimension; when the field measured value x of the equipment state quantity C is larger than the specific numerical value y of the fault threshold value D, the equipment breaks down; of each apparatusThe specific value y of the fault threshold value D has randomness, and the probability distribution density function of the fault threshold value D is fD(y,μ,σ2) Where the unknown number mu is an expected value, the unknown number sigma 2 is a variance, and fD(y,μ,σ2) And fCA(x) Have the same functional form;
for the types of equipment, equipment in various states is randomly selected, and a sample set A is established; utilizing the existing mathematical statistics theory to carry out statistical analysis on the field actual measurement values x of the state quantities C of all the equipment in the sample set A to obtain the probability distribution density function f of the field actual measurement values x of the state quantities C of the equipmentCA(x)；
For the types of equipment, equipment in a fault state is randomly selected, and a sample set B is established; carrying out statistical analysis on the field actual measurement values x of the state quantities C of all the equipment in the sample set B by utilizing the existing mathematical statistical theory to obtain the probability distribution density function f of the state quantity C of the fault equipmentCB(x)；
Establishing equations (1) and (2), and simultaneously solving unknown parameters mu and sigma by adopting the existing calculation method2：
Wherein n is the total number of the device samples in the set A, and m is the number of the failed device samples in the set A;
calculating the fault rate lambda of a specific device E in the class of devices by using the formula (3):
wherein x0Is the specific measured data of the state quantity C of this specific device E.
The fundamental reason for the insulation breakdown fault of the power equipment is that the insulation system has defects, the insulation performance is reduced, and the power equipment cannot bear normal working voltage or lightning overvoltage and operation overvoltage with strong randomness. Breakdown failure occurs when the withstand voltage of the insulation system is stepped down below the voltage to which the insulation system is subjected, otherwise operation can continue. For power equipment in actual operation, uncertainty exists in the insulation strength of a defective part due to differences and randomness in manufacturing process, operation age, aging rate, defective part and the like; due to manufacturing process variations and randomness in the defect sites and over-voltages, uncertainty exists in the electrical stress experienced by the defect sites.
Based on the theory, the insulation strength S at the defect position of the equipment is regarded as a random variable with a probability density function f (S), the electrical stress S at the defect position is also regarded as a random variable with a probability density function h (S), the probability that the electrical stress is greater than the insulation strength (namely, the probability that the insulation is broken down) is obtained through convolution operation, the stress-strength interference relation is shown in FIG. 2, and the shaded part in the graph represents a stress-strength interference area; p(s) is an insulation fault probability density distribution function, and an expression formula is shown as a formula (C-1); the area under the curve p(s) is numerically equal to the insulation failure probability F, and the calculation formula is as shown in formula (C-2).
The theory has universality, and can also be used for describing the failure probability of mechanical, thermal, chemical corrosion and the like of the power equipment, and the mechanical failure probability, the thermal failure probability and other types of failure probability of the equipment can be calculated by (C-2) by selecting a proper strength, namely a stress variable.
In field practice, it is difficult to find a characterization parameter that directly characterizes the strength S of the defect site, and only the state quantity C obtained by various detection means. For a state quantity capable of effectively representing equipment defect or performance degradation state, the defect part strength S can be assumed to be a monotonic function G (x) of the state quantity C, wherein x is a specific numerical value of the state quantity; and G (x) may result in, the presence of, the inverse function g (x):
S＝G(x)，x＝g(S) (C-3)
then, the formula can be solved according to the probability degree of the random variable function, and the probability density distribution function f of the state quantity C is utilizedC(x) Estimating the probability density function f (S) of the defect site intensity S:
it is further assumed that the stress S corresponding to this intensity S is also functionally related to a parameter D called "failure threshold", i.e.:
s＝G(y)，y＝g(s) (C-5)
where y is the specific value of the fault threshold D. Then, the formula can be solved according to the probability degree of the random variable function, and the probability density distribution function f of the fault threshold value D is utilizedD(y) estimating a probability density function h(s) of the stress s at the defect site:
then (C-2) may be changed to:
that is, the device failure probability F can be calculated using a probability density distribution function of the state quantity C and the failure threshold D, and when the state quantity C is negatively correlated with the intensity S (i.e., G' (y) < 0) and the value of C exceeds the value of the failure threshold D, the device fails.
Since the strength S and the stress S should be physical quantities having the same dimension at the time of calculating (C-2), the state quantity C and the failure threshold value D should also be physical quantities having the same dimension.
Probability distribution density function f of state quantity CC(x) From the statistical analysis of the numerical value of the state quantity C actually measured on site, the statistical analysis method can adopt a very mature distribution model hypothesis test method or a nonparametric hypothesis test method, wherein when a large number of zero values exist in the numerical value of the state quantity C, the zero values and the non-zero values are distinguished to be respectively counted, probability distribution density function calculation is carried out on the non-zero values, then the probability distribution density function calculation is combined with zero value probability, and for the state quantity which is difficult to meet the known probability distribution model, a fitting function of the empirical distribution function of a sample can be used as an accumulated probability function of the state quantity, and then the probability density distribution function is obtained.
Given that the statistical distribution of stress s at the time of failure of the device is unknown, and that the functional relationship g (x) or g (x) between stress s and failure threshold D is also unknown, the probability density distribution function of failure threshold D is now determined based on the failure probability statistical data of the device. Probability density distribution function f assuming fault threshold DD(y) probability distribution density function f of state quantity CC(x) With the same functional form. Thus, when f is determinedC(x) After the functional form of (c), f is determinedD(y) functional form, the next step is to find fD(y) unknown parameters in the function. To solve for fDThe known condition of the unknown parameter in the (y) function is the distribution f of the parameters to a certain state quantityC(x) In the corresponding sample set a, the total number of samples is n, wherein the number of failed samples is m, then:
for example, assume fC(x) Is expected value of μxVariance of
Is normally distributed, then fD(x) Is expected value of μyVariance of
Is normally distributed. Establishing a sample set A for all the devices, wherein the sample set A comprises the devices in a normal state, an attention state, an abnormal state and a serious state, and all or part of the devices in the serious state can be regarded as fault devices. μ of sample set AxAnd
obtained by existing mathematical statistical methods, while μyAnd
the calculation is carried out by using (C-8). In general case fDThe number of unknown parameters in the (y) function is not only 1, but it is not sufficient to find all the unknown parameters by means of only one known condition (C-8). Therefore, it is necessary to collect different state quantity sample sets, construct a plurality of equations based on (C-8), and solve the equations through an optimization algorithm to finally obtain fD(y) all unknowns in the function. And a sample set B is established for the fault equipment, and m/n in the corresponding formula (C-8) is equal to 1 for the sample set B.
Therefore, in the case of (C-8) in both cases, the unknown number [ mu ] can be obtained by using an optimization solving method such as the conventional search algorithmyAnd
the probability density distribution function f of the fault threshold DD(y) becomes a known function.
A particular device E has a defect that has a probability of causing a device failure. Sample data x of the state quantity C of the equipment is obtained0Then, it is required to be according to x0The size of (d) gives the failure rate of the device. Assume that in a sample set A consisting of all devices, there is a subset A of samples0。A0The sample data of the state quantity C of all the devices in the system is x0. Assume that A is in a unit time0The state quantity C of the device is substantially stable (i.e., the state quantity C is not significantly degraded), then (C-8) may be converted to (C-9) to calculate a0Failure rate of the device:
the invention provides a clear and complete mathematical model with theoretical support and available calculation for equipment failure rate, which is based on mathematical statistics and analysis theory and combines with the observed value of the state quantity of the field equipment, so that the calculation result is more accurate than the existing methods of deduction and the like;
in the mathematical model, the fault rate is taken as a characterization parameter of the equipment state, so that the state conditions among a plurality of equipment can be accurately equivalent and compared.
The invention is described in detail below in connection with the calculation of the fault rate of a transformer based on data on dissolved gas in transformer oil.
The dissolved gas in the transformer oil can reflect the electrical fault of a transformer oil-paper insulation system, the corresponding insulation strength S is the tolerant electric field strength of the oil-paper insulation of the defect part, the corresponding electrical stress S is the external electric field strength of the defect part, in field practice, the concentration of the dissolved gas in the oil can effectively represent the insulation defect of the transformer, the concentration value of the dissolved gas in the oil is greater than or equal to 0, the higher the concentration is, the lower the insulation strength is, the more serious the defect is, and the higher the failure rate of equipment is. At this time, the state quantity C of the transformer is the concentration of the gas dissolved in the oil, and the specific value x is the characteristic gas hydrogen (H)2) The concentration of (c) can be (acetylene, methane, etc.). The value of this state quantity C is assumed to be linear with the value of the intensity S at the defect, i.e.:
S＝k1x+k2……(k1＜0) (C-10)
then:
further assume that the value of stress S corresponding to this strength S is linear with a variable D named "failure threshold", the specific value of which is y, i.e.:
s＝k3y+k4……k3＜0 (C-12)
then:
then (C-2) may become (C-14):
the fault rate of the transformer can be calculated by using the probability density distribution function of the state quantity C and the corresponding fault threshold value D.
For insulation faults of power transformers, dissolving H in oil is selected2The concentration is the state quantity C of the device, the field measured data of the state quantity C is larger than zero, and the larger x is, the larger the probability of the fault of the device is; suppose that: the value x of the state quantity C of the field equipment has a probability distribution density function fCA(x)；
Suppose that: for the insulation fault mode of the transformer, a fault threshold value D exists, and the fault threshold value D and the state quantity C have the same dimension; when the field measured value x of the equipment state quantity C is larger than the specific numerical value y of the fault threshold value D, the equipment breaks down; the specific value y of the fault threshold value D of each device has randomness, and the probability distribution density function of the specific value y is fD(y,μ,σ2) Where the unknown number mu is an expected value and the unknown number sigma is2Is a variance, and fD(y,μ,σ2) And fCA(x) Have the same functional form;
randomly selecting power transformers in various states, and establishing a sample set A, wherein the total number n of the transformers is 617 and contains serious transformersThe number m of transformers with insulation defects is 3; utilizing the existing sampling theory, hypothesis verification, significance test and other mathematical statistical theories to carry out statistical analysis on the field actual measurement numerical value x of the state quantity C of all the equipment in the sample set A to obtain the probability distribution density function f of the state quantity C of the equipmentCA(x) Has a lognormal distribution form, such as the formula (C-15):
wherein the expected value mua2.69, standard deviation σa＝0.87；
Randomly selecting transformers with serious insulation defects, and establishing a sample set B; utilizing the existing sampling theory, hypothesis verification, significance test and other mathematical statistical theories to carry out statistical analysis on the field actual measurement numerical values x of the state quantities C of all the devices in the sample set B to obtain the probability distribution density function f of the state quantity C of the fault deviceCB(x) As shown in formula (C-16):
wherein the expected value mub5.07, standard deviation σb＝1.54；
Then, a probability density distribution function f of the fault threshold is determinedD(y,μ,σ2) Also in the form of a lognormal distribution. Further simultaneous equations (C-17) and (C-18) are solved by adopting a particle swarm optimization search algorithm to solve fD(y,μ,σ2) Unknown parameters mu and sigma2：
To obtain fD(y,μ,σ2) The function is as follows:
wherein, the expected value mu is 3.58, and the standard deviation sigma is 0.67;
for a specific individual transformer, the oil is dissolved with hydrogen H2Content x0The failure rate λ of the individual transformer was calculated using the formula (C-20) at 200 ppm:
the calculated result is λ 0.5%.
The invention provides an equipment fault evaluation system 200 based on field data volume, as shown in fig. 3, comprising:
the first modeling module 201 determines a fault mode corresponding to the state quantity of the target equipment and a probability distribution density function f of the field measured value of the state quantity of the whole equipment, aiming at the target equipment of which the state quantity is related to a certain fault mode of the equipment and the probability of the equipment generating the fault is higher when the field measured data of the state quantity is largerCA(x)；
A second modeling module 202 for determining a probability distribution density function f of the field measured values of the state quantities of the faulty deviceCB(x)；
A calculation module 203 for calculating a probability distribution density function f according to the values of the state quantity of the whole equipmentCA(x) Probability distribution density function f of fault equipment state quantity valueCB(x) Determining a probability distribution density function f of a failure threshold corresponding to the state quantityD(y,μ,σ2) And solving the expected value mu and variance sigma of the function2；
The evaluation module 204 calculates the failure rate of a certain individual device by using the current value of the state quantity of the individual device and the probability density distribution function of the failure threshold value.
Wherein the fault threshold and the state quantity have the same dimension.
Wherein, the probability distribution density function f of the field actual measurement value of the state quantity of the whole equipmentCA(x) The method is obtained from a sample set A by using the existing mathematical statistical method:
for the target equipment of the type, equipment in various states is randomly selected, and specific numerical values of state quantities of the equipment are actually measured on site to form a sample set A; where n is the total number of device samples in sample set a and m is the number of failed device samples in sample set a.
Wherein, the probability distribution density function f of the on-site actual measurement value of the state quantity of the fault equipmentCB(x) The following were obtained from sample set B using existing mathematical statistical methods:
for the target equipment of the type, equipment in a fault state is randomly selected, and specific numerical values of state quantities of the equipment are measured on site to form a sample set B.
Wherein the probability distribution density function f of the fault thresholdD(y,μ,σ2) Determining as a function f of the probability distribution density of the state quantities of the entire installationCA(x) Have the same functional form.
Wherein the probability distribution density function f of the fault threshold is solvedD(y,μ,σ2) Expected value μ and variance σ of2The following formula is used:
wherein y is a specific numerical value of the fault threshold, and x is a specific numerical value of the state quantity.
Optionally, the determination formula of the individual failure rate of the device is as follows:
where λ is the failure rate, x0Measured data of the state quantity of the individual equipment.
Compared with the prior art, the determination result is more accurate.
As will be appreciated by one skilled in the art, embodiments of the present application may be provided as a method, system, or computer program product. Accordingly, the present application may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, the present application may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, and the like) having computer-usable program code embodied therein. The scheme in the embodiment of the application can be implemented by adopting various computer languages, such as object-oriented programming language Java and transliterated scripting language JavaScript.
The present application is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the application. It will be understood that each flow and/or block of the flow diagrams and/or block diagrams, and combinations of flows and/or blocks in the flow diagrams and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means which implement the function specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
While the preferred embodiments of the present application have been described, additional variations and modifications in those embodiments may occur to those skilled in the art once they learn of the basic inventive concepts. Therefore, it is intended that the appended claims be interpreted as including preferred embodiments and all alterations and modifications as fall within the scope of the application.
It will be apparent to those skilled in the art that various changes and modifications may be made in the present application without departing from the spirit and scope of the application. Thus, if such modifications and variations of the present application fall within the scope of the claims of the present application and their equivalents, the present application is intended to include such modifications and variations as well.
Claims (14)
1. A method for device failure rate assessment based on field data volume, the method comprising:
aiming at a class of target equipment of which the state quantity is related to a certain fault mode of the equipment and the probability of the equipment having the fault is higher as the field measured data of the state quantity is larger, determining the fault mode corresponding to the state quantity of the target equipment and determining the probability distribution density function f of the field measured value of the state quantity of the whole equipmentCA(x)；
Determining a probability distribution density function f of a field measured value of a state quantity of a faulty deviceCB(x)；
Probability distribution density function f according to overall equipment state quantity valueCA(x) Probability distribution density function f of fault equipment state quantity valueCB(x) Determining a probability distribution density function f of a failure threshold corresponding to the state quantityD(y,μ,σ2) And solving the expected value mu and variance sigma of the function2；
And calculating the failure rate of the individual equipment by using the current value of the state quantity of the individual equipment and the probability density distribution function of the failure threshold value.
2. The method of claim 1, the fault threshold value having the same dimension as the quantity of states.
3. The method of claim 1, wherein the probability distribution density function f is a function of the measured field values of the plant state variablesCA(x) The method is obtained from a sample set A by using the existing mathematical statistical method:
for the target equipment of the type, equipment in various states is randomly selected, and specific numerical values of state quantities of the equipment are actually measured on site to form a sample set A; where n is the total number of device samples in sample set a and m is the number of failed device samples in sample set a.
4. The method of claim 1, wherein the probability distribution density function f is a function of the measured field values of the state quantities of the faulty deviceCB(x) The following were obtained from sample set B using existing mathematical statistical methods:
for the target equipment of the type, equipment in a fault state is randomly selected, and specific numerical values of state quantities of the equipment are measured on site to form a sample set B.
5. The method of claim 1, the probability distribution density function f of the fault threshold valueD(y,μ,σ2) Determining as a function f of the probability distribution density of the state quantities of the entire installationCA(x) Have the same functional form.
6. The method of claim 1, the probability distribution density function f solving for a fault threshold valueD(y,μ,σ2) Expected value μ and variance σ of2The following formula is used:
wherein y is a specific numerical value of the fault threshold, and x is a specific numerical value of the state quantity.
7. The method of claim 1, wherein the individual failure rate of the equipment is determined according to the following formula:
where λ is the failure rate, x0Measured data of the state quantity of the individual equipment.
8. A system for equipment failure rate assessment based on field data volume, the system comprising:
the first modeling module is used for determining a fault mode corresponding to the state quantity of the target equipment and determining a probability distribution density function f of the state quantity field actual measurement value of the whole equipment according to the target equipment of the type that the state quantity is related to a certain fault mode of the equipment and the probability of the equipment having the fault is higher when the field actual measurement data of the state quantity is largerCA(x)；
A second modeling module for determining probability distribution density function f of the field measured value of the state quantity of the fault equipmentCB(x)；
A calculation module for calculating a probability distribution density function f according to the values of the state quantities of the whole equipmentCA(x) Probability distribution density function f of fault equipment state quantity valueCB(x) Determining a probability distribution density function f of a failure threshold corresponding to the state quantityD(y,μ,σ2) And solving the expected value mu and variance sigma of the function2；
And the evaluation module is used for calculating the fault rate of the equipment individual by utilizing the current value of the state quantity of the equipment individual and the probability density distribution function of the fault threshold.
9. The system of claim 8, the fault threshold value having the same dimension as the quantity of states.
10. The system of claim 8, wherein the probability distribution density function f is a function of the measured field values of the state quantity of the whole plantCA(x) The method is obtained from a sample set A by using the existing mathematical statistical method:
for the target equipment of the type, equipment in various states is randomly selected, and specific numerical values of state quantities of the equipment are actually measured on site to form a sample set A; where n is the total number of device samples in sample set a and m is the number of failed device samples in sample set a.
11. The system of claim 8, wherein the probability distribution density function f is a function of the measured field values of the state quantities of the faulty deviceCB(x) The following were obtained from sample set B using existing mathematical statistical methods:
for the target equipment of the type, equipment in a fault state is randomly selected, and specific numerical values of state quantities of the equipment are measured on site to form a sample set B.
12. The system of claim 8, the probability distribution density function f of the failure threshold valueD(y,μ,σ2) Determining as a function f of the probability distribution density of the state quantities of the entire installationCA(x) Have the same functional form.
13. The system of claim 8, the probability distribution density function f solving for a failure threshold valueD(y,μ,σ2) Expected value μ and variance σ of2The following formula is used:
wherein y is a specific numerical value of the fault threshold, and x is a specific numerical value of the state quantity.
14. The system of claim 8, wherein the individual failure rate of the equipment is determined according to the following formula:
where λ is the failure rate, x0Measured data of the state quantity of the individual equipment.
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