CN114970215B - Internet of things equipment robustness analysis method based on normal distribution function - Google Patents
Internet of things equipment robustness analysis method based on normal distribution function Download PDFInfo
- Publication number
- CN114970215B CN114970215B CN202210902023.6A CN202210902023A CN114970215B CN 114970215 B CN114970215 B CN 114970215B CN 202210902023 A CN202210902023 A CN 202210902023A CN 114970215 B CN114970215 B CN 114970215B
- Authority
- CN
- China
- Prior art keywords
- equipment
- robustness
- function
- time
- accident
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Active
Links
Images
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F30/00—Computer-aided design [CAD]
- G06F30/20—Design optimisation, verification or simulation
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F17/00—Digital computing or data processing equipment or methods, specially adapted for specific functions
- G06F17/10—Complex mathematical operations
- G06F17/18—Complex mathematical operations for evaluating statistical data, e.g. average values, frequency distributions, probability functions, regression analysis
-
- G—PHYSICS
- G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
- G16Y—INFORMATION AND COMMUNICATION TECHNOLOGY SPECIALLY ADAPTED FOR THE INTERNET OF THINGS [IoT]
- G16Y40/00—IoT characterised by the purpose of the information processing
- G16Y40/20—Analytics; Diagnosis
Abstract
The invention relates to an Internet of things equipment robustness analysis method based on a normal distribution function, belongs to the technical field of Internet of things equipment management, and solves the problems of time consumption, labor consumption and high cost of the existing Internet of things equipment operation and maintenance. A robustness analysis method of Internet of things equipment based on a normal distribution function comprises the following steps: s1, acquiring basic data including delivery service life L, equipment installation and debugging time S, an installation and debugging breakage coefficient beta and an equipment accident type influence factor alpha; s2, solving the effective service life UL = L-beta S of the equipment; s3, obtaining t 0 To t m Total number m of accidents, time t of accidents i And the type of accident; s4, calculating an accumulated value of the damage rate lambda (t); s5, obtaining a related distribution value gamma of the damage rate lambda (t); s6, obtaining a damage density function f (t); s7, obtaining a robustness function; and S8, analyzing according to the robustness function. By the method, the problems of time consumption, labor consumption and high cost of equipment operation and maintenance are reduced, and the real-time performance and the accuracy of information are improved.
Description
Technical Field
The invention belongs to the technical field of equipment management of the Internet of things, and particularly relates to a method for analyzing robustness of equipment of the Internet of things based on a normal distribution function.
Background
The equipment is a basic setting related to an internet of things system, and along with the development of the internet of things, the function of the equipment is not only reflected on the field use, but also has a greater value that data is reported through a network, the data is sorted, filed and checked by an informatization system, and meanwhile, big data analysis is carried out on the collected data, so that a data basis is provided for analysis decision making, and a great effect is exerted on the informatization development of agriculture.
Due to the particularity of the agricultural Internet of things, after the existing equipment is installed, when the equipment is managed by operation and maintenance, the field condition of the equipment cannot be known in a remote mode, and the equipment can be maintained and replaced on site only after problems are found by people, so that the operation and maintenance cost is huge. Therefore, the equipment robustness analysis is very necessary, and the equipment robustness analysis result is provided for field project operation and maintenance personnel, so that the equipment robustness condition can be predicted in advance and prevented in advance, and the method is an effective measure for managing the equipment.
Disclosure of Invention
In view of the above analysis, the invention aims to provide a method for analyzing robustness of internet of things equipment based on a normal distribution function, so as to solve the problems of time consumption, labor consumption and high cost of the existing internet of things equipment during operation and maintenance.
The purpose of the invention is mainly realized by the following technical scheme:
the invention provides an Internet of things equipment robustness analysis method based on a normal distribution function, which comprises the following steps:
s1, acquiring basic data, wherein the basic data comprises the service life L of an initialized device when leaving a factory, the installation and debugging time length S of the device, an installation and debugging breakage coefficient beta and an equipment accident type influence factor alpha;
s2, calculating and obtaining the effective service life UL of the equipment according to the basic data obtained in the S1:
UL=L-βS （1）；
s3, acquiring the time point t when the slave equipment starts to be put into use 0 To the current query time point t m Total number of accidents m, time t of each accident i And the type of accident that occurred at each accident;
s4, calculating and obtaining the damage rate lambda (t) accumulated value of the equipment in time t;
in the above formula (2): m is the total number of accidents of the equipment in the time t, i is the ith accident, i is more than or equal to 1 and less than or equal to m, and alpha i The impact factor, t, corresponding to the ith accident type 0 Point in time for the start of the plant, t i For the time point of the ith accident of the equipment, t = t m -t 0 ；
S5, calculating to obtain a related distribution value gamma of the damage rate lambda (t); γ satisfies:
s6, obtaining a damage density function f (t); the damage density function f (t) satisfies:
and mu satisfies the following conditions:
μ=UL （5）
σ satisfies:
σ=γ （6）；
s7, obtaining a robustness function H (t) related to the device use time t,
and S8, carrying out robustness analysis on the Internet of things equipment according to the robustness function H (t).
Further, in step S1, the accident of the equipment is a failure and an accuracy correction.
Further, 1 is taken as an accident influence factor for precision correction; the damage degree of the fault to the equipment is equivalent to the precision correction, and the influence factor of the fault is 1; the damage degree of the fault to the equipment is weaker than that of precision correction, and the influence factor of the fault is larger than 0 and smaller than 1; the damage degree of the fault to the equipment is more serious than the precision correction, and the influence factor of the fault is more than 1.
Furthermore, in step S1, the mounting and debugging breaking coefficient is more than 0 and less than or equal to 1.
Further, in step S6, the damage density function f (t) follows a normal distribution with an expected μ and a standard deviation σ.
Further, in step S7, (7) wherein:
f (t) is the cumulative distribution function of the lesions.
Further, in step S8, an H-t graph is drawn according to the robustness function H (t), and the variation trend of robustness is determined according to the trend of the H-t graph.
Further, in step S8, the robustness function H (t) is substituted into a specific value of t, and a corresponding robustness function value is obtained, and the robustness function value is used as an index for evaluating robustness.
Furthermore, a robustness index is calculated by writing a java program, and a calculation result is drawn by utilizing an echarts third-party front-end plug-in.
Further, after step S7, there is the following step:
step S7': and (3) along with the increase of the operation time of the equipment and the accumulation of the data, performing data fitting for multiple times according to the steps from S1 to S7, and continuously adjusting the parameters mu and sigma of the function to obtain a robustness function H (t).
Compared with the prior art, the invention can at least realize one of the following technical effects:
(1) By the equipment robustness analysis method, the operation condition of the equipment of the Internet of things can be remotely evaluated, the problems of time consumption, labor consumption and high cost of equipment operation and maintenance are greatly reduced, the operation and maintenance period is shortened, and the real-time performance and the accuracy of information are greatly improved.
(2) The invention can master the conditions of potential safety hazards of the equipment in time by combining the normal distribution function with the influence factors such as the service life of the equipment leaving factory, the installation and debugging time length, the installation and debugging breakage coefficient, the equipment failure times, the precision correction times, the accident type influence shadow and the like, thereby having various capabilities of managing the operation of the equipment, analyzing and predicting the health condition of the equipment.
(3) The equipment robustness analysis method provided by the invention can be suitable for other non-Internet-of-things equipment, is an effective measure for scientifically, efficiently and timely monitoring and predicting the equipment robustness, ensures the normal use of the equipment, has a wide application coverage range, and has a huge application prospect.
(4) The technical idea of the equipment robustness analysis can also be applied to other equipment with service life or fault density distribution which is not based on a normal distribution function, and has important revelation significance.
Drawings
The drawings are for purposes of illustrating particular embodiments and are not to be construed as limiting the invention, like reference numerals being used for like parts throughout the drawings;
FIG. 1 is a logic diagram of the method of the present invention;
FIG. 2 is a schematic flow chart showing the steps of the method of the present invention;
FIG. 3 is a graph of the damage density function, the damage accumulation, and the robustness of the level gauge analysis of example 1;
FIG. 4 is a graph of the damage density function, the damage accumulation and the robustness of the valve controller analysis of example 2.
Detailed Description
The method for analyzing robustness of the internet of things equipment based on the normal distribution function is further described in detail with reference to specific embodiments, which are only used for comparison and explanation purposes, and the present invention is not limited to these embodiments.
Normal distribution is one of the more widely used distributions, and is suitable for the case of a substantially uniform cumulative effect, i.e., equipment accidents caused by cumulative loss, such as corrosion, wear, surface damage, and device aging. The failure mechanism is as follows: the random variable is considered to be normally distributed, i.e. the random variable is uniformly and slightly influenced by the sum of a plurality of independent and tiny random factors without leading factors. In addition, the equipment with the life data conforming to the normal distribution has obvious time characteristic, and the reliability of the equipment is degraded quickly after the equipment is used for a certain specific time, so that a reasonable maintenance plan can be made according to the reliability.
Aiming at the equipment of the Internet of things, the invention obtains the robustness function of the equipment by introducing equipment installation and debugging breakage coefficients, various types of accidents and accident type influence factors, and utilizes the robustness index to evaluate the running condition of the equipment in real time.
The invention aims at most monitoring equipment in an agricultural Internet of things, such as a soil moisture content instrument, a meteorological moisture content instrument, an input type liquid level meter, an input type flow meter, a radar liquid level meter, a radar flow meter, a valve controller and the like, and does not include equipment which is frequently used by people, such as a water fertilizer machine and the like.
The invention provides an Internet of things equipment robustness analysis method based on a normal distribution function, which comprises the following steps:
s1, acquiring basic data, wherein the basic data comprises an initial equipment delivery service life L, equipment installation and debugging duration S, an installation and debugging breakage coefficient beta and an equipment accident type influence factor alpha;
s2, calculating to obtain the effective service life UL of the equipment according to the factory service life L of the equipment, the installation and debugging time length S of the equipment and the installation and debugging breaking coefficient beta:
UL=L-βS （1）；
s3, acquiring the time point t when the slave equipment is put into use 0 To the current query time point t m Total number of accidents m, time t of each accident i And the type of accident that occurred at each accident;
s4, calculating and obtaining the damage rate lambda (t) accumulated value of the equipment in time t;
in the above formula (2): m is the total number of accidents of the equipment in the time t, i is the ith accident, i is more than or equal to 1 and less than or equal to m, and alpha i The impact factor, t, corresponding to the ith accident type 0 Point in time for the start of the plant, t i For the ith accident occurrence time point of the equipment, t = t m -t 0 ；
S5, calculating to obtain a related distribution value gamma of the damage rate lambda (t); the gamma satisfies:
s6, obtaining a damage density function f (t); the damage density function f (t) satisfies:
the mu satisfies the following conditions:
μ=UL （5）
the sigma satisfies:
σ=γ （6）；
s7, obtaining a robustness function H (t) related to the using time t of the equipment,
and S8, carrying out robustness analysis on the equipment of the Internet of things according to the robustness function H (t).
It should be noted that, in step S1, the basic data related to the device may be obtained by long-term trace record feedback during the debugging, operation and maintenance process of the staff, or may be obtained by a database of the device manufacturer.
Specifically, the service life of the equipment leaving factory is that a manufacturer is consulted when a file is established for the equipment, the result is manually filled, the equipment installation and debugging time length S is that when the equipment is formally put into use, operation and maintenance personnel maintain, update and fill the file, the installation and debugging breaking coefficient beta is obtained by the operation and maintenance personnel through calculation of the proportion of the breaking coefficient influence factors in the service life L leaving factory and is synchronously updated when the maintenance, update and debugging time length is long, and the equipment accident type influence factor alpha is obtained through comparison between the equipment accident repair time and the equipment accident type before repair time and is synchronously updated when the equipment accident type is maintained.
The accident refers to the fault and the precision correction of equipment. Regarding the types of equipment failures, such as corrosion, leakage, structural defects, void, scaling, abnormal power supply, appearance damage and the like, the equipment is restored after the failure is repaired and then put into use again, and the typical repair state includes two types, namely basic repair and complete repair. Different influence factors are set for different fault types because the continuous service time of the equipment is different after different fault types are repaired, namely the equipment damage degrees caused by different fault types are different.
The equipment precision correction is to determine whether precision correction is needed or not according to comparison between external measurement data and equipment reported data by operation and maintenance personnel. Within the analysis time range, the times of correction appear for many times, which shows that the reliability of the data reported by the equipment is deteriorated, and the unreliable data is a fatal fault and is an important factor influencing the robustness invisible to the naked eye.
In the present invention, the accident type influence factor for the precision correction of the equipment is set to 1, because generally after the precision correction, the equipment generally reaches the level before the correction.
Specifically, the idea of setting the fault type influence factor is to compare the damage degree of the fault type to the equipment with the damage degree of the precision correction to the equipment, and if the damage degree of the fault type to the equipment is equivalent to the precision correction, that is, the fault type just reaches the level before the equipment fault occurs after the equipment fault is repaired, the influence factor of the fault type is set to 1; if the damage degree of the fault type to the equipment is weaker than that of the fault type corrected with precision, the accident type influence factor is set to be a number between (0 and 1) according to the difference of the damage degree, such as appearance damage 0.3, void 0.7 and structure 0.8; if the damage degree of the fault type to the equipment is more serious than the precision correction, the accident type influence factor is generally set to be a number between (1, 2) according to the difference of the damage degree, such as corrosion 1.3, leakage 1.4 and structural defect 1.7; under special conditions, if the equipment is damaged deeply, such as at the later stage of the service life of the equipment or serious failure of the equipment, the effect after repair is poor, and the value of the effect is more than or equal to 2.
The more times of equipment failure in the analysis time range, the shorter the time for which the equipment can be used continuously, which is a main factor influencing the robustness of the equipment during the use process of the equipment.
It should be noted that, the occurrence of these events belongs to the natural consumption of the operation of the equipment, and is a peripheral dissociation factor affecting the robustness of the equipment, and only when the daily maintenance work is not done in place, the damage of the equipment is really caused, and the fault affecting the normal operation of the equipment occurs, the fault is understood as the accident of the present invention.
It should be noted that, in step S2, the installation and debugging of the device is required after the device leaves the factory, the installation and debugging of the device may cause a reduction in service life, and the main influencing factors include the time length for installation and debugging of the device and the reduction coefficient thereof, and the reduction coefficient is influenced by the environment (e.g., dry/wet) of the warehouse where the device is located during debugging, the operating time (e.g., debugging power on/off), the difference from the actual installation (e.g., power on), and other factors. In summary, the loss degree of the installation and debugging on the equipment is less than the loss of the equipment in actual operation, so the breaking coefficient is more than 0 and less than or equal to 1.
In step S3, the operation and maintenance personnel will record the time point t when the device starts to be used in the operation and maintenance process 0 Each time of failure t i Data such as the type of failure, the cause of the failure, etc.; the operation and maintenance personnel compare the data measured and calculated by the external instrument with the data reported by the equipment in the routine inspection process to correct the precision, corresponding data can be formed by the precision correction every time, and the data is subjected to grouping statistics to obtain the time point of the precision correction of the equipment every time. Finally, the accidents are sequenced according to the occurrence sequence to obtain the time point t of the equipment accident i And a total number m.
In step S4, the damage rate λ (t) represents the probability that the equipment will be damaged per unit time after the equipment is put into service and the first accident occurs, or between two accidents, and is referred to as the damage rate of the equipment. Generally, the damage rate is closely related to the accident rate of the equipment, the accident rate refers to the reciprocal of the time from the beginning of the equipment being put into use to the occurrence of the first accident or between two adjacent accidents, obviously, the size of the accident rate is related to the maintenance effect, so the invention introduces the accident type influence factor to evaluate the maintenance effect of different accident types.
Specifically, the cumulative value of the damage rate λ (t) of the device in time t is:
in the above formula (2): m is the total number of accidents of the equipment in the time t, i is the ith accident, i is more than or equal to 1 and less than or equal to m, and alpha is i The impact factor, t, corresponding to the ith accident type 0 Point in time for the start of the plant, t i For the ith accident occurrence time point of the equipment, t = t m -t 0 。
It should be noted that, in step S5, the accumulated degree of damage of the device over the entire life cycle with increasing usage time is related to the effective service life UL of the device and follows the equation:
γ is a constant related to the change in the damage rate λ (t) with time.
In step S6, the damage density function f (t) of the device represents the probability that a certain unit time of the damage density function of the device occupies the entire useful service life UL in the useful service life UL of the device. After various equipment accidents are adjusted through accident influence factors, no leading factor influencing the robustness of the equipment is considered, and the equipment is caused by a plurality of micro factors, so that the equipment robustness is analyzed by utilizing a normal distribution function.
Specifically, the damage density function f (t) obeys normal distribution with the expected μ and the standard deviation σ, that is:
（4）
with increasing device usage time t, the cumulative distribution function of the damage is:
in addition, the damage rate λ (t) and the damage density function f (t) have the following relationship:
it can therefore be deduced that:
therefore, the method comprises the following steps:
μ=UL（5）
σ=γ（6）
it should be noted that, in step S7, the device robustness function H (t) is a function of variable time t, reflecting the change of the robustness degree with the use time of the device, the robustness of the new device from the factory of the device is close to 1, and the robustness gradually decreases with the increase of the use time.
Specifically, the sum of the robustness function H (t) and the damage-accumulation distribution function F (t) is 1, that is:
F(t)+H(t)=1 （11）
so the larger the value 0 < H (t) < 1, the higher the robustness of the device.
It should be noted that, in step S8, the robustness analysis of the internet-of-things device is performed according to the robustness function, and the change trend of the robustness can be judged according to the trend of the H-t graph by drawing the graph H-t in which the robustness function H (t) changes along with the use time, and if the trend of the decrease in the robustness of the device in a certain period of time in the future is observed to be large, the device can be maintained in a targeted manner in the period of time; or substituting a specific time point t to obtain a corresponding robustness function value, and taking the robustness function value as an index for evaluating robustness. The robustness index reflects the robustness of the equipment, the robustness index is 1 when the equipment leaves a factory, the robustness index is 0 when the equipment is completely scrapped and discarded, the robustness index is 0.5 when the effective service life of the equipment is reached, and the robustness index is smaller and smaller along with the increase of the service time.
Specifically, the robustness index can be calculated and evaluated by writing a java program, and drawing is realized by using third-party plug-ins such as echarts and charts, for example, a column or a graph is formed; or converting the normal distribution function into a standard normal distribution function, and evaluating by referring to a standard normal distribution function table.
Further, after the step S7, there is a step S7': and (4) along with the increase of the running time of the equipment and the accumulation of the data, performing data fitting for multiple times according to the steps from S1 to S7, and continuously adjusting the parameters mu and sigma of the function.
It should be noted that, due to the limited operation time and accident data of the equipment, the parameters μ and σ of the robustness function obtained at the previous stage inevitably deviate from the actual condition of the equipment. With the increase of the running time of the equipment, the regularity of the expressed state of the equipment is stronger, and in order to better grasp the robust state of the equipment on the whole, the parameters mu and sigma of the function are continuously fine-tuned according to the increase of the fault and the precision adjustment times of the equipment and the accumulation of the data quantity, so that the mathematical expression of the robust function H (t) is closer to the actual running state of the equipment.
FIG. 1 is a logic diagram of the computing method of the present invention, and FIG. 2 is a flow chart of the steps of the method of the present invention.
The analysis method provided by the invention can realize remote evaluation on the operation condition of the equipment of the Internet of things, can timely master the conditions of potential safety hazards and the like of the equipment, greatly reduces the problems of time consumption, labor consumption and high cost of operation and maintenance of the equipment, shortens the operation and maintenance period, and greatly improves the real-time property and accuracy of information.
Example 1
Carrying out robustness analysis on a liquid level meter with the equipment number of 869074030229889 of the Internet of things, wherein the steps are as follows:
s1, initializing basic data, wherein the delivery time of the liquid level meter is 11 and 25 days in 2019, the delivery service life is 8 years, and the total service life is 2920 days, namely 11 and 25 days in 2027. The installation and debugging time is 1 month, the installation environment is consistent with the real environment, so the breakage coefficient is 1, the operation of the query equipment is stopped at 2022 years, 6 months and 5 days, and two fault types are involved: correcting the precision, wherein the influence factor is 1; the power supply is abnormal, and the influence factor is 1.2;
s2, calculating to obtain the effective service life UL =2920-30 multiplied by 1=2890 (days) according to the factory service life L of the equipment, the installation and debugging duration S of the equipment and the installation and debugging depreciation coefficient beta;
s3, 893 days are totally taken from 12 months and 25 days in 2019 to 6 months and 5 days in 2022, the total number of accidents is 5, the precision is corrected for 2 times, the power supply is abnormal for 3 times, and the time points of the accidents are listed as the following list 1:
s4, according to the formula
s5, according to the formula
Obtaining; γ =1384.978;
s6, obtaining a damage density function f (t),
；
s7, obtaining a robustness function H (t) related to the device use time t,
namely, it is
；
And S8, drawing curves of a damage density function F (t), a damage accumulation distribution function F (t) and a device robustness function H (t), and referring to FIG. 3.
As can be seen from fig. 3, the robustness index of the gauge decreases to 0.8 in 2024, to 0.5 in 2028, and to less than 0.2 in 2032, gradually decreasing with time of use.
Example 2
Carrying out robustness analysis on a valve controller with the Internet of things equipment number of 1803056071, and comprising the following steps:
s1, initializing basic data, wherein the delivery time of the valve controller is 03 and 17 in 2019, the service life of the valve controller is 6 years and 45 days, and 2235 days are totally achieved, namely 05 and 2 days in 2025. The installation and debugging time is 2 months, one month is debugged in a storehouse, the depreciation coefficient is 0.5, the one-month installation environment is consistent with the real environment, the depreciation coefficient is 1, 4 faults occur by 11 days of 2021 years, 08 months and 11 days through the operation of query equipment, and the three fault types are related to: void, impact factor 0.7; the power supply is abnormal, and the influence factor is 1.2; leakage, with an impact factor of 1.4;
s2, calculating to obtain the effective service life of the equipment according to the factory service life L of the equipment, the installation and debugging duration S of the equipment and the installation and debugging depreciation coefficient beta:
UL =2235-30 × 0.5-30 × 1=2190 (days);
s3, from 2019, 5, 17 days to 2021, 8, 11 days, 817 days, the total number of accidents is 4, wherein 1 time of leakage, 1 time of vacancy and 2 times of power supply abnormity occur, and the time points of the accidents are listed as follows:
s4, according to the formula
Calculating out the deviceCumulative value of damage rate λ (t) over time t;
s5, according to the formula
Solving the following steps: γ =647.8249;
s6, obtaining a damage density function f (t),
；
s7, obtaining a robustness function H (t) related to the using time t of the equipment,
namely, it is
；
And S8, drawing curves of a damage density function F (t), a damage accumulation distribution function F (t) and a device robustness function H (t), and referring to FIG. 4.
As can be seen from fig. 4, the robustness index of the valve controller decreases to 0.8 at 7 months of 2023, to 0.5 at 7 months of 2025, and to less than 0.2 at 1 month of 2027, gradually decreasing with time.
The above description is only for the preferred embodiment of the present invention, but the scope of the present invention is not limited thereto, and any changes or substitutions that can be easily conceived by those skilled in the art within the technical scope of the present invention are included in the scope of the present invention.
Claims (10)
1. A robustness analysis method of Internet of things equipment based on a normal distribution function is characterized by comprising the following steps:
s1, acquiring basic data, wherein the basic data comprises an initial equipment delivery service life L, equipment installation and debugging duration S, an installation and debugging breakage coefficient beta and an equipment accident type influence factor alpha;
s2, calculating and obtaining the effective service life UL of the equipment according to the basic data obtained in the S1:
UL=L-βS （1）；
s3, acquiring the time point t when the slave equipment is put into use 0 To the current query time point t m Total number of accidents m, time t of each accident i And the type of accident that occurred at each accident;
s4, calculating and obtaining the damage rate lambda (t) accumulated value of the equipment in time t;
in the above formula (2): m is the total number of accidents of the equipment in the time t, i is the ith accident, i is more than or equal to 1 and less than or equal to m, and alpha is i The impact factor, t, corresponding to the ith accident type 0 Point in time for the start of the plant, t i For the time point of the ith accident of the equipment, t = t m -t 0 ；
S5, calculating to obtain a related distribution value gamma of the damage rate lambda (t); the gamma satisfies:
s6, obtaining a damage density function f (t); the damage density function f (t) satisfies:
the mu satisfies the following conditions:
μ=UL （5）
the sigma satisfies:
σ=γ （6）；
s7, obtaining a robustness function H (t) related to the device use time t,
and S8, carrying out robustness analysis on the Internet of things equipment according to the robustness function H (t).
2. The method according to claim 1, wherein in the step S1, the equipment accident type is fault and accuracy correction.
3. The method of claim 2,
the influence factor of the precision correction is 1;
the damage degree of the fault to the equipment is equivalent to the precision correction, and the influence factor of the fault is 1; the damage degree of the fault to the equipment is weaker than the precision correction, and the influence factor of the fault is larger than 0 and smaller than 1; the damage degree of the fault to the equipment is more serious than the precision correction, and the influence factor of the accident is more than 1.
4. The method according to claim 1, wherein in step S1, the installation and commissioning break factor 0 < β ≦ 1.
5. The method according to claim 1, characterized in that in the step S6, the damage density function f (t) follows a normal distribution with a standard deviation of σ and is expected to be μm.
6. The method according to claim 1, wherein in step S7, in the formula (7):
the F (t) is a cumulative distribution function of the lesions.
7. The method according to claim 1, wherein in step S8, an H-t graph is plotted according to the robustness function H (t), and the variation trend of the robustness is judged according to the trend of the H-t graph.
8. The method according to claim 1, wherein in step S8, a specific value of t is substituted according to the robustness function H (t), and the corresponding robustness function value is obtained, and the robustness function value is used as an index for evaluating robustness.
9. The method of claim 8, wherein the robustness index is calculated by writing a java program, and the result of the calculation is graphed using echarts third party front end plug-ins.
10. The method according to claim 1, wherein the step S7 is followed by the steps of:
step S7': and (3) along with the increase of the operation time of the equipment and the accumulation of the data, performing data fitting for multiple times according to the steps from S1 to S7, and continuously adjusting the parameters mu and sigma of the function to obtain a robustness function H (t).
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202210902023.6A CN114970215B (en) | 2022-07-29 | 2022-07-29 | Internet of things equipment robustness analysis method based on normal distribution function |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202210902023.6A CN114970215B (en) | 2022-07-29 | 2022-07-29 | Internet of things equipment robustness analysis method based on normal distribution function |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN114970215A CN114970215A (en) | 2022-08-30 |
| CN114970215B true CN114970215B (en) | 2022-10-21 |
Family
ID=82969617
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202210902023.6A Active CN114970215B (en) | 2022-07-29 | 2022-07-29 | Internet of things equipment robustness analysis method based on normal distribution function |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN114970215B (en) |
Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN104537133A (en) * | 2014-05-12 | 2015-04-22 | 中国人民解放军空军工程大学 | Method for predicting remaining lifetime of single airplane based on airplane structural life envelope principle |
| CN110823575A (en) * | 2019-11-09 | 2020-02-21 | 北京工业大学 | Bearing life prediction method based on performance degradation dictionary structure and similarity |
| CN112396215A (en) * | 2020-10-22 | 2021-02-23 | 国网浙江省电力有限公司嘉兴供电公司 | Intelligent prediction method for self-adaptive interval of residual life of equipment |
| CN112966441A (en) * | 2021-03-08 | 2021-06-15 | 中国人民解放军海军航空大学 | Equipment residual life evaluation method based on continuous Weiner process damage |
Family Cites Families (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20070088570A1 (en) * | 2005-10-18 | 2007-04-19 | Honeywell International, Inc. | System and method for predicting device deterioration |
| US7889840B2 (en) * | 2007-01-10 | 2011-02-15 | The United States Of America As Represented By The Secretary Of The Navy | System and method for predicting material fatigue and damage |
-
2022
- 2022-07-29 CN CN202210902023.6A patent/CN114970215B/en active Active
Patent Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN104537133A (en) * | 2014-05-12 | 2015-04-22 | 中国人民解放军空军工程大学 | Method for predicting remaining lifetime of single airplane based on airplane structural life envelope principle |
| CN110823575A (en) * | 2019-11-09 | 2020-02-21 | 北京工业大学 | Bearing life prediction method based on performance degradation dictionary structure and similarity |
| CN112396215A (en) * | 2020-10-22 | 2021-02-23 | 国网浙江省电力有限公司嘉兴供电公司 | Intelligent prediction method for self-adaptive interval of residual life of equipment |
| CN112966441A (en) * | 2021-03-08 | 2021-06-15 | 中国人民解放军海军航空大学 | Equipment residual life evaluation method based on continuous Weiner process damage |
Non-Patent Citations (1)
| Title |
|---|
| 基于统计回归和非线性Wiener过程的交流接触器剩余寿命预测;李奎 等;《电工技术学报》;20191031;第34卷(第19期);4058-4070 * |
Also Published As
| Publication number | Publication date |
|---|---|
| CN114970215A (en) | 2022-08-30 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN104966141B (en) | Method and system for updating a model used to generate an industrial asset health profile | |
| US7895016B2 (en) | System and method for wind turbine health management | |
| US7228234B2 (en) | Weather data quality control and ranking method | |
| CN104517241B (en) | A kind of methods of risk assessment based on transmission line of electricity full working scope information | |
| CN110375787B (en) | Online evaluation method for running state of metering device | |
| KR101010717B1 (en) | Condition-based plant operation and maintenance management system | |
| Huynh et al. | Maintenance decision-making for systems operating under indirect condition monitoring: value of online information and impact of measurement uncertainty | |
| CN103487781B (en) | A kind of reliability estimation method of the electronic mutual inductor based on accelerated deterioration | |
| CN105761001B (en) | Distribution network equipment state evaluation method fusing multi-source information | |
| CN112904266B (en) | Method and device for predicting service life of electric energy meter | |
| JP7053152B2 (en) | Systems and methods for optimizing recommended inspection intervals | |
| JP2017151980A5 (en) | ||
| CN114970215B (en) | Internet of things equipment robustness analysis method based on normal distribution function | |
| CN107239876B (en) | Management method and system for aging life cycle of I & C equipment of nuclear power plant | |
| CN111339661A (en) | Automatic planning method for high-voltage cable inspection cycle | |
| Lunacek et al. | Understanding biases in pre-construction estimates | |
| CN112213681B (en) | Electric energy meter service life prediction method and device | |
| Babykina et al. | Modelling pipe failures in water distribution systems: accounting for harmful repairs and a time-dependent covariate | |
| Koutsellis et al. | Warranty forecasting of repairable systems for different production patterns | |
| CN115809805A (en) | Power grid multi-source data processing method based on edge calculation | |
| CN111210097A (en) | State maintenance method for power transmission and transformation equipment | |
| CN112507290B (en) | Power distribution equipment fault probability pre-judging method, device and storage medium | |
| CN113911870B (en) | Elevator on-line inspection and detection method based on Internet of things | |
| Rastayesh et al. | Bayesian Network Methods for Risk-Based Decision Making for Wind Turbines | |
| Neelamkavil | Condition-based maintenance in facilities management |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant |