CN116304636A - Elevator top-rushing accident dynamic prediction method and system based on fault tree - Google Patents

Elevator top-rushing accident dynamic prediction method and system based on fault tree Download PDF

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CN116304636A
CN116304636A CN202310297965.0A CN202310297965A CN116304636A CN 116304636 A CN116304636 A CN 116304636A CN 202310297965 A CN202310297965 A CN 202310297965A CN 116304636 A CN116304636 A CN 116304636A
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许娜
狄恪毅
李伟
张博
赵文成
王莉
刘非非
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China University of Mining and Technology CUMT
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Abstract

The invention discloses a dynamic prediction method and a system for elevator overhead collision accidents based on a fault tree, and belongs to the technical field of elevator fault prediction. Collecting data about elevator roof-punching accident faults, and constructing an elevator roof-punching accident fault tree by using the collected data; obtaining a plurality of common fault tree bottom events; the original vibration signals of the traction machine, which are acquired in real time by a sensor, are arranged on a bearing seat on the outer side of the traction machine to extract frequency domain features, frequency domain indexes reflecting fault features of the traction machine are subjected to failure probability conversion, fuzzy failure probability is calculated by other bottom events, extraction of time domain index information of the traction machine, failure probability conversion and quantitative analysis of fault trees are performed, and then the results are uploaded to an intelligent operation and maintenance cloud platform to perform dynamic risk assessment and display. And quantitatively outputting the occurrence probability index of the elevator overhead accident according to the analysis of the fault tree, and fully automatically evaluating the risk, thereby solving the technical problems of predictive diagnosis and dynamic risk evaluation of the elevator overhead accident.

Description

Elevator top-rushing accident dynamic prediction method and system based on fault tree
Technical Field
The invention relates to a dynamic prediction method and a system for elevator overhead collision accidents based on a fault tree, and belongs to the technical field of elevator fault prediction.
Background
The roof-rushing accident is an elevator accident with high incidence and high hazard, is one of five common accidents of the elevator, and has extremely high hazard. The occurrence of the elevator roof-rushing accident has great influence on the economy, reputation and development of enterprises; meanwhile, the life safety of personnel taking the elevator and waiting for the elevator is threatened, and great hit is brought to families; and even social stability.
In the prior art, the method for predicting the risk of the elevator roof-rushing accident is mainly based on static fault tree, FMEA analysis and analytic hierarchy process conversion to form health evaluation of the elevator whole elevator; the technical problem that prior art scheme exists is: the static elevator risk assessment method is most, and cannot reflect the current elevator risk condition in real time. Therefore, the method has very limited effect on dynamic risk prediction of elevator overhead accidents.
Disclosure of Invention
Aiming at the defects of the prior art, the elevator roof-rushing accident dynamic prediction method and system based on the fault tree are provided, the steps are simple, the current elevator risk condition can be fed back in real time, and the elevator roof-rushing accident dynamic risk prediction can be made.
In order to achieve the technical purpose, the invention provides a dynamic prediction method for an elevator overhead accident based on a fault tree, which comprises the following specific steps:
s1, collecting data about elevator roof-punching accident faults, and constructing a fault tree with the elevator roof-punching accidents as roof-punching accidents by using the collected data; obtaining a total of 23 common fault tree bottom events of the elevator roof-rushing accident fault tree;
s2, confirming that the failure importance of the elevator traction machine is the largest in a bottom event through structural importance sorting, so that original vibration information of the traction machine is selected and monitored;
s3, extracting frequency domain characteristics of original vibration signals of the traction machine, which are acquired in real time by arranging a sensor on a bearing seat on the outer side of the traction machine of the elevator, converting failure probability of frequency domain indexes reflecting fault characteristics of the traction machine, and calculating fuzzy failure probability of other 22 common fault tree bottom events by a fuzzy comprehensive evaluation theory;
s4, extracting time domain index information of the traction machine, converting failure probability and quantitatively analyzing fault trees by using edge section data acquisition equipment arranged at an elevator, and uploading the result to an intelligent operation and maintenance cloud platform for dynamic risk assessment and display.
Further, the elevator traction machine failure probability calculation method comprises the following steps:
the method comprises the steps of calculating an acquired original vibration signal of the traction machine to extract time domain characteristics, wherein the time domain characteristics comprise four time domain index values of the traction machine by using the following steps: mean value x of vibration signal mean Maximum x of vibration signal max Root mean square value x of vibration signal rms And a kurtosis value x of vibration signal kurt : the amplitude of vibration of the traction machine can be reflected by the average value and the root mean square value of the vibration signals; the maximum value and the kurtosis value can reflect the vibration impact of the traction machine;
Figure BDA0004143834780000021
x max ＝Max(X)，
Figure BDA0004143834780000022
Figure BDA0004143834780000023
wherein: x is x i The vibration value is represented by N, the number of points per sample, and σ the standard deviation.
Further, the performance evaluation method of the elevator traction machine specifically comprises the following steps:
dividing the known safety state of the tractor into four sections according to ISO-10816 vibration monitoring evaluation standards, performing failure probability conversion on a time domain index value reflecting the performance of the tractor, acquiring a failure probability threshold value area of the tractor newly put into use after combining expert experience and individual fuzzy opinion aggregation, wherein the failure probability threshold value area of the tractor which is not limited for a long time is [0.03-0.1 ], the failure probability threshold value area of the tractor which is not suitable for long-time continuous operation is [0.1-0.3], and when the failure probability threshold value area is larger than 0.3, the tractor is possibly damaged at any time;
the failure probability P of the traction machine is reflected by each index calculated according to the following formula n ：
Figure BDA0004143834780000024
P n,average ＝average(P mean ,P max ,P rms ,P kurt )，
Wherein x is n Is a time domain index value r of a traction machine vibration signal 0 -r 4 The vibration threshold value, P, is selected for the vibration monitoring evaluation criteria according to ISO-10816 n The failure probability of the traction machine is reflected by each time domain index, P n,average The total failure probability of the traction machine, P, reflected by the time domain index mean ,P max ,P rms ,P kurt And respectively calculating failure probability according to the mean value, the maximum value, the root mean square value and the kurtosis value of the vibration signal.
Further, the failure probability of the traction machine in a period of time is diagnosed through the interval where the failure probability value is located, and the overall failure probability of the traction machine can intuitively represent the overall health condition of the traction machine;
the safety state of the traction machine corresponding to the failure probability is shown in the following table:
Figure BDA0004143834780000025
Figure BDA0004143834780000031
further, quantitatively analyzing the failure probability of the traction machine by using a fault tree:
because the occurrence probability of the bottom events is lower, the occurrence probability of the high-order multi-bottom events is lower than the minimum value, the top events are independently and approximately calculated by adopting the minimum cut set to reduce the calculated amount,
the probability of the occurrence of a top-rushing accident top event of the elevator is calculated by adopting the independent approximation of the minimum cut set:
Figure BDA0004143834780000032
wherein P (T) is the probability value of the top event T, K i Is the i-th minimal cut set.
The elevator overhead collision accident dynamic prediction system based on the fault tree comprises a vibration sensor arranged on a traction machine, wherein the vibration sensor is connected with a fault tree analysis module through a data acquisition module and a data conversion module, and the fault tree analysis module is connected with an intelligent operation and maintenance cloud platform through a wireless transmission module;
the data acquisition module is used for controlling the vibration sensor to work and transmitting the vibration sensor data to the data conversion module;
the data conversion module is used for converting the vibration sensor data of the received analog signals into digital signals and sending the digital signals to the fault tree analysis module;
the fault tree analysis module is used for dividing the fault tree bottom event into a traction machine failure event and other bottom events according to the fault tree, wherein the failure probability conversion is carried out according to the vibration sensor data when the traction machine fails, the quantitative analysis is carried out according to the failure probability synchronization, and the impact top accident failure probability and probability importance key indexes are output and uploaded to the intelligent operation and maintenance cloud platform; aiming at other bottom events, collecting expert judgment language, aggregating expert opinions and calculating fuzzy failure probability through a fuzzy comprehensive evaluation theory; finally, through the characteristics of the top-punching fault tree, quantitative analysis is synchronously carried out, and the probability of occurrence of the top-punching event and some importance risk indexes are dynamically reflected;
the intelligent operation and maintenance cloud platform is used for carrying out dynamic risk assessment and display on the data sent by the fault tree analysis module.
An electronic device, comprising:
a processor;
a memory for storing the processor-executable instructions;
wherein the processor is configured to execute the instructions to implement a fault tree based elevator overhead incident dynamic prediction method.
A computer readable storage medium, which when executed by a processor of an electronic device, causes the electronic device to perform a fault tree based elevator overhead collision dynamic prediction method.
The beneficial effects are that: the invention constructs an elevator roof-punching accident fault tree, acquires original signals of key parts in real time to extract frequency domain characteristics, converts failure probability of frequency domain indexes reflecting the fault characteristics of the key parts, and constructs a bridge of a traction machine frequency spectrum data acquisition and fault tree analysis system; a fault tree analysis module is established by utilizing edge segment data acquisition equipment, quantitative analysis is synchronously carried out according to failure probability, real-time conversion is carried out to failure probability, meanwhile, important indexes such as elevator overhead accident occurrence probability and the like are quantitatively output according to the fault tree analysis, the risk is fully automatically estimated, and the technical problems of elevator overhead accident fault prediction diagnosis and dynamic risk estimation are solved.
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Fig. 1 is a block flow diagram of a dynamic prediction system for elevator overhead accidents based on fault trees of the present invention.
Fig. 2 is a block diagram of the dynamic prediction system of elevator overhead accidents based on fault tree of the present invention.
Detailed Description
The invention is further described below with reference to the accompanying drawings:
as shown in fig. 1, the method for dynamically predicting the elevator overhead accident based on the fault tree comprises the following steps:
1. constructing an elevator top-punching accident fault tree from top to bottom by taking an elevator top-punching accident as a top event, and digging 23 key bottom event causes altogether, wherein the importance of the failure bottom event of the traction machine is the largest, and the failure probability conversion is carried out after the original vibration information of the traction machine is required to be monitored;
2. collecting original signals of the traction machine in real time to extract frequency domain features, and converting failure probability of frequency domain indexes reflecting failure features of the traction machine; the rest 22 bottom events directly pass through a fuzzy comprehensive evaluation theory, and fuzzy failure probability is calculated by collecting expert judgment language and aggregating expert opinions;
3. and constructing a dynamic prediction system of the elevator overhead collision accident based on the fault tree, uploading the result to an intelligent operation and maintenance cloud platform according to extraction of the time domain index information of the traction machine, failure probability conversion and quantitative analysis of the fault tree, and quantitatively outputting the probability index of the elevator overhead collision accident by combining with the analysis of the fault tree to perform dynamic risk assessment and display.
Building an elevator overhead accident fault tree:
taking an elevator roof-punching event as a roof event, constructing an elevator roof-punching event fault tree from top to bottom, and digging 23 key bottom event causes in total, wherein the key bottom event causes are as follows:
the elevator is in a top-rushing accident which comprises two major types of traction system faults and safety protection system faults,
wherein the traction system faults include insufficient traction and brake faults; the reasons for the insufficient traction force include: the abrasion of the traction steel wire rope is seriously reduced, the abrasion of the traction sheave groove is seriously reduced, oil stains are formed on the traction sheave groove and the steel wire rope, the tension of the traction ropes on two sides of the traction sheave is too high, and the traction machine fails; the reasons for the brake failure include the brake circuit failure, the brake arm locking, the too small friction between the brake wheel and the brake shoe, the serious abrasion of the brake wheel and the brake shoe, the too large gap between the brake wheel and the brake shoe, the greasy dirt on the surface of the brake wheel and the too loose brake spring adjustment;
the safety protection system faults comprise a speed limiter fault and a safety tongs fault, the factors of the speed limiter fault comprise short circuit of a safety loop of the speed limiter, breakage of a steel wire rope of the speed limiter and insufficient lifting formation of the steel wire rope of the speed limiter, and the reasons of the insufficient lifting formation of the steel wire rope of the speed limiter comprise abrasion of a wheel groove of a tensioning wheel, loosening of nuts at a speed adjusting part, long-time telescopic damage of a spring of the speed limiter and errors on the surface of a part; factors of safety tongs faults include short circuit of a safety loop of the safety tongs, incorrect adjustment of installation, incapability of normal operation of the safety tongs on two sides, and reasons of incapability of normal operation of the safety tongs on two sides include overlarge clearance of a safety wedge block, grinding of a safety wedge opening, incapability of synchronous action of wedge blocks on two sides and greasy dirt on the surfaces of a sliding block/the wedge blocks;
the structural importance of the failure of the traction machine in the fault tree is 0.0615, and all bottom events of the bit sequence are first, namely the structural influence degree of the failure of the traction machine on the occurrence of the top event is the greatest.
Once the traction machine fails, the probability of the elevator to be in a jacking accident is greatly improved, so that the monitoring and diagnosis of the elevator is particularly important.
The elevator traction machine failure probability calculation and performance evaluation method comprises the following steps:
time domain feature extraction of the acquired raw vibration signal, including but not limited to vibration signal mean (x mean ) Maximum value (x) max ) Root mean square value (x rms ) And kurtosis value (x kurt )：
Figure BDA0004143834780000051
x max ＝Max(X)，
Figure BDA0004143834780000052
Figure BDA0004143834780000053
Wherein: x is x i Representing vibration values, N representing the number of points per sample, σ representing the standard deviation;
the safety state of the known traction machine is divided into four sections according to the ISO-10816 vibration monitoring evaluation standard,
combining expert experience and individual fuzzy opinion aggregation, the steps are as follows: inviting 3 industry experts to evaluate the bottom event of the elevator roof-rushing accident by adopting 5 language values, wherein the evaluation language comprises: the large, medium, small and small are respectively used for representing the influence degree of the accident cause factors on the occurrence of the elevator overhead accident. When using natural language for the uncertainty description, it is necessary to quantitatively express it by using fuzzy membership functions. Because the trapezoidal fuzzy function has the attribute of wider distance, the calculation process is simple and efficient, and the trapezoidal fuzzy membership function is used for mapping expert judgment language. The expression is:
Figure BDA0004143834780000054
f(x)＝1,b<x≤c
Figure BDA0004143834780000055
f (x) =0, others
In the formula, a and b represent upper and lower boundaries of the number of trapezoidal blur, respectively, and the region [ b, c ] represents the median of the number of trapezoidal blur.
Regarding the opinion of the same thing, different experts often have the situation that judgment conflicts or is consistent due to the similarity or difference of experience or experience, but the event occurring due to the elevator overhead accident has the characteristics of ambiguity and difficulty in quantification, so that the judgment of the experts cannot be proved to be correct or not. The algorithm proposed by Hus and Chen is a method for aggregating individual fuzzy opinions into a group of fuzzy consensus opinions, which can eliminate the contradiction of the expert judgment and reduce subjectivity. Therefore, the expert opinion is aggregated by adopting the algorithm, and the steps are as follows:
1) Determining similarity
Figure BDA0004143834780000061
Z in k 、Z y The evaluation languages of the kth expert and the y expert respectively; q is a fuzzy number; s (Z) k ，Z y ) The similarity of the languages is evaluated for two experts, and the value interval is [0,1]]。
2) Determining average degree of identity
Figure BDA0004143834780000062
Wherein n is the total number of experts; b (B) k For average degree of consistency among experts
3) Determining relative degree of identity
Figure BDA0004143834780000063
4) Determining aggregate weights
Because the experience and experience of the expertise of the judgment are different, the expertise and authority of the judgment on the elevator roof-rushing event are also different, and different weight values need to be allocated to each expertise. The experts are assigned differently through the three sides of title, academic and working age, and the weight values of different experts are given
Figure BDA0004143834780000064
5) The calculation formula of the aggregate weight is as follows
Figure BDA0004143834780000065
Wherein beta is a relaxation factor, reflecting whether the individual opinion or the group opinion is emphasized, and beta is E [0,1].
6) Summarizing expert opinion and determining fuzzy number of bottom events
In order to facilitate the synthesis operation of the fuzzy number, the alpha truncated set theory in the fuzzy set is applied to convert the fuzzy number into the interval number and then calculate the interval number, the trapezoidal fuzzy number F α Alpha truncated interval number of (2) is calculated as
F α ＝[a+(b-a)α，d-(d-c)α]
The ambiguity form and α -truncated set are specifically shown in the following table.
Fuzzy language Fuzzy number form Alpha truncated set
Small size (0.1，0.2，0.2，0.3) [0.1α+0.1，-0.1α+0.3]
Smaller size (0.2，0.3，0.4，0.5) [0.1α+0.2，-0.1α+0.5]
Medium and medium (0.4，0.5，0.5，0.6) [0.1α+0.4，-0.1α+0.6]
Larger size (0.5，0.6，0.7，0.8) [0.1α+0.5，-0.1α+0.8]
Big size (0.7，0.8，0.8，0.9) [0.1α+0.7，-0.1α+0.9]
The average fuzzy number of expert group evaluation under the alpha truncated set is applied as
Figure BDA0004143834780000071
7) Calculating a fuzzy likelihood value
After quantifying the judgment language made by the expert, the fuzzy number still has uncertainty. Therefore, the left-right fuzzy ordering method is adopted to convert the fuzzy ordering method into a fuzzy possible value F ps Firstly, a fuzzy maximization set and a fuzzy minimization set are required to be obtained, namely
f max (x)＝x，0≤x≤1
f max (x) =0, other
f min (x)＝1-x，0≤x≤1
f min (x) =0, other
The left and right blur probabilities are respectively
F PS，R (D)＝sup[f D (x) ∧f max (x)]
F PS，L (D)＝sup[f D (x) ∧f min (x)]
The fuzzy possible value is
Figure BDA0004143834780000072
8) And calculating the fuzzy failure probability.
The finally calculated probability of failure of the fuzzy is an accurate probability value calculated by the Onisawa formula, namely
Figure BDA0004143834780000073
F PS，T ≠0
F FR ＝0，F PS,T ＝0
Figure BDA0004143834780000081
Elevator overhead collision accident risk analysis based on fuzzy fault tree:
industry experts opinion identifies risk factors that may cause elevator surging accidents, and finally determines the risk factors. The bottom events of the elevator roof-rushing accident are independent of each other and only occur and do not occur. And then, according to the established fault tree, introducing a fuzzy set theory to analyze the elevator overhead fault as follows.
1) Qualitative analysis
Structural importance analysis related to fault tree structure only:
Figure BDA0004143834780000082
wherein K represents the total number of the minimum cutsets, K j Represents the j-th minimal cut set, N j Representing the base number of events for the j-th minimal cut set.
Let the structural importance of the bottom event X1, I phi (1) =0.123, and so on in the structural importance order:
Iφ(1)＝Iφ(2)＝Iφ(3)＝Iφ(4)＝0.123＞
Iφ(5)＝Iφ(6)＝Iφ(7)＝Iφ(8)＝Iφ(9)＝Iφ(20)＝0.0293＞
Iφ(10)＝Iφ(11)＝Iφ(12)＝Iφ(13)＝Iφ(14)＝Iφ(15)＝Iφ(16)＝Iφ(17)＝Iφ(18)＝Iφ(19)
from the above ordering, it can be seen that bottom events X1-X4 have the greatest effect on the occurrence of an elevator roof-rushing accident, followed by X5-X9, X20, =iΦ (21) =iΦ (22) = 0.000451. In order to reduce the risk of elevator roof-rushing, prevention and control measures are proposed aiming at the factors.
2) Fuzzy probability solution
First, the failure probability of each bottom event is calculated, and the case of serious abrasion of the traction steel wire rope is taken as an example for description. Three industry experts related to the project construction are used for acquiring evaluation languages, the evaluation languages of the three experts on the event are respectively smaller, smaller and medium, and the average consistency degree B1=0.7825, B2=0.675, B3=0.6875, the relative consistency degree R1= 0.3663, R2= 0.3140 and R3= 0.3198 of the three expert opinions are obtained; the ability weights of three experts, phi1=0.348, phi2=0.370, phi3=0.283, aggregate the evaluation opinions, and take beta in the calculation formula to be 0.5, and represent that the personal opinion and the group opinion are equally important, w1= 0.3572, w2=0.342, and w3=0.301. Then, a left-right likelihood value F is obtained according to the formula PS，R(W) ＝0.4179，F PS，L(W) ＝0.724，F PS，T(W) =0.347. Finally, deblurring to obtain the fuzzy number F FR =0.0014, i.e. the probability of the traction wire rope wearing seriously and the diameter decreasing is 0.0014.
The failure probability of all fuzzy events is calculated by the flow
Figure BDA0004143834780000091
Taking the maximum value to obtain the maximum value of the failure probability of the new operation of the traction machine as 0.03, setting the upper limit of the threshold value of the new operation of the traction machine as less than 0.03, setting the failure threshold value area of the operation of the traction machine which can be operated without limitation for a long time as 0.03-0.1 according to the failure probability calculated by expert judgment, setting the failure probability threshold value area of the traction machine which is not suitable for continuous operation for a long time as 0.1-0.3, and indicating that the damage of the traction machine is possible at any time when the failure probability threshold value is greater than 0.3;
the failure probability P of the traction machine is reflected by each index calculated according to the following formula n ：
Figure BDA0004143834780000092
P n,average ＝average(P mean ,P max ,P rms ,P kurt )，
Wherein x is n Is a time domain index value r of a traction machine vibration signal 0 -r 4 The vibration threshold value, P, is selected for the vibration monitoring evaluation criteria according to ISO-10816 n The failure probability of the traction machine is reflected by each time domain index, P n,average The total failure probability of the traction machine, P, reflected by the time domain index mean ,P max ,P rms ,P kurt And respectively calculating failure probability according to the mean value, the maximum value, the root mean square value and the kurtosis value of the vibration signal.
Each time domain index can reflect the safety state of the traction machine: the mean value and the root mean square value of the vibration signal can reflect the vibration amplitude of the traction machine; the maximum value and the kurtosis value can reflect the vibration impact of the traction machine. The failure probability of the traction machine in a period of time can be diagnosed through each time domain index value, and the overall failure probability of the traction machine can intuitively represent the overall health condition of the traction machine. The safety state of the traction machine corresponding to the failure probability is shown in the following table.
Figure BDA0004143834780000093
Figure BDA0004143834780000101
Dynamic prediction system for elevator roof-rushing accident
The elevator overhead accident dynamic prediction diagnosis system architecture is shown in fig. 2. The vibration sensor is connected to the edge end data acquisition equipment, vibration sensor signals are acquired by the edge end data acquisition equipment, failure probability calculation is carried out through the built-in module, and the prediction result is uploaded to the cloud platform after analysis and processing are carried out through the fault tree quantitative analysis algorithm.
Quantitative analysis of fault tree:
the elevator overhead accident fault tree has no repeated bottom events, which means that the minimum cutsets do not contain the same bottom events with each other and have independence.
When the occurrence probability of the top-rushing accident top event of the elevator is accurately calculated, the probability formula (namely the repulsion theorem) of the logical union in the Boolean algebra is expanded. However, as the minimum cut sets generated by the elevator roof-rushing accident fault tree are more, if the calculation is accurately performed by adopting the repulsion theorem, the combination explosion phenomenon is easy to generate in the calculation item number, and the calculation amount is huge;
because the occurrence probability of the event is lower, the occurrence probability of the high-order multiple events is lower than the minimum value, the top event can be calculated by adopting the independent approximation of the minimum cut set to obtain better results,
the probability of the occurrence of a top-rushing accident top event of the elevator is calculated by adopting the independent approximation of the minimum cut set:
Figure BDA0004143834780000102
wherein P (T) is the probability value of the top event T, K i For the ith minimal cut set, a total of k minimal cut sets.
As shown in fig. 2, the elevator overhead collision accident dynamic prediction system based on the fault tree comprises a vibration sensor arranged on a traction machine, wherein the vibration sensor is connected with a fault tree analysis module through a data acquisition module and a data conversion module, and the fault tree analysis module is connected with an intelligent operation and maintenance cloud platform through a wireless transmission module;
the data acquisition module is used for controlling the vibration sensor to work and transmitting the vibration sensor data to the data conversion module;
the data conversion module is used for converting the vibration sensor data of the received analog signals into digital signals and sending the digital signals to the fault tree analysis module;
the fault tree analysis module is used for dividing the fault tree bottom event into a traction machine failure event and other bottom events according to the fault tree, wherein the failure probability conversion is carried out according to the vibration sensor data when the traction machine fails, the quantitative analysis is carried out according to the failure probability synchronization, and the impact top accident failure probability and probability importance key indexes are output and uploaded to the intelligent operation and maintenance cloud platform; aiming at other bottom events, collecting expert judgment language, aggregating expert opinions and calculating fuzzy failure probability through a fuzzy comprehensive evaluation theory; finally, through the characteristics of the top-punching fault tree, quantitative analysis is synchronously carried out, and the probability of occurrence of the top-punching event and some importance risk indexes are dynamically reflected;
the intelligent operation and maintenance cloud platform is used for carrying out dynamic risk assessment and display on the data sent by the fault tree analysis module.

Claims (8)

1. The dynamic prediction method for the elevator roof-rushing accident based on the fault tree is characterized by comprising the following specific steps:
s1, collecting data about elevator roof-punching accident faults, and constructing a fault tree with the elevator roof-punching accidents as roof-punching accidents by using the collected data; obtaining a total of 23 common fault tree bottom events of the elevator roof-rushing accident fault tree;
s2, confirming that the failure importance of the elevator traction machine is the largest in a bottom event through structural importance sorting, so that original vibration information of the traction machine is selected and monitored;
s3, extracting frequency domain characteristics of original vibration signals of the traction machine, which are acquired in real time by arranging a sensor on a bearing seat on the outer side of the traction machine of the elevator, converting failure probability of frequency domain indexes reflecting fault characteristics of the traction machine, and calculating fuzzy failure probability of other 22 common fault tree bottom events by a fuzzy comprehensive evaluation theory;
s4, extracting time domain index information of the traction machine, converting failure probability and quantitatively analyzing fault trees by using edge section data acquisition equipment arranged at an elevator, and uploading the result to an intelligent operation and maintenance cloud platform for dynamic risk assessment and display.
2. The method for dynamically predicting elevator overhead accidents based on fault trees according to claim 1, wherein the method comprises the following steps: the elevator traction machine failure probability calculation method comprises the following steps:
the method comprises the steps of calculating an acquired original vibration signal of the traction machine to extract time domain characteristics, wherein the time domain characteristics comprise four time domain index values of the traction machine by using the following steps: mean value x of vibration signal mean Maximum x of vibration signal max Root mean square value x of vibration signal rms And a kurtosis value x of vibration signal kurt : the amplitude of vibration of the traction machine can be reflected by the average value and the root mean square value of the vibration signals; the maximum value and the kurtosis value can reflect the vibration impact of the traction machine;
Figure FDA0004143834770000011
x max ＝Max(X)，
Figure FDA0004143834770000012
Figure FDA0004143834770000013
wherein: x is x i The vibration value is represented by N, the number of points per sample, and σ the standard deviation.
3. The dynamic prediction method of elevator overhead collision accidents based on fault trees according to claim 2, wherein the performance evaluation method of the elevator traction machine is specifically as follows:
dividing the known safety state of the tractor into four sections according to ISO-10816 vibration monitoring evaluation standards, performing failure probability conversion on a time domain index value reflecting the performance of the tractor, acquiring a failure probability threshold value area of the tractor newly put into use after combining expert experience and individual fuzzy opinion aggregation, wherein the failure probability threshold value area of the tractor which is not limited for a long time is [0.03-0.1 ], the failure probability threshold value area of the tractor which is not suitable for long-time continuous operation is [0.1-0.3], and when the failure probability threshold value area is larger than 0.3, the tractor is possibly damaged at any time;
the failure probability P of the traction machine is reflected by each index calculated according to the following formula n ：
Figure FDA0004143834770000021
P n,average ＝average(P mean ,P max ,P rms ,P kurt )，
Wherein x is n Is a time domain index value r of a traction machine vibration signal 0 -r 4 The vibration threshold value, P, is selected for the vibration monitoring evaluation criteria according to ISO-10816 n The failure probability of the traction machine is reflected by each time domain index, P n,average The total failure probability of the traction machine, P, reflected by the time domain index mean ,P max ,P rms ,P kurt And respectively calculating failure probability according to the mean value, the maximum value, the root mean square value and the kurtosis value of the vibration signal.
4. A method for dynamically predicting an elevator overhead collision event based on a fault tree according to claim 3, wherein:
diagnosing the failure probability of the traction machine according to the interval where the failure probability value is located, wherein the overall failure probability of the traction machine can intuitively represent the overall health condition of the traction machine;
the safety state of the traction machine corresponding to the failure probability is shown in the following table:
Figure FDA0004143834770000022
5. the method for dynamically predicting elevator overhead accidents based on fault trees according to claim 1, wherein the method comprises the following steps: quantitatively analyzing failure probability of the traction machine by using a fault tree:
because the occurrence probability of the bottom events is lower, the occurrence probability of the high-order multi-bottom events is lower than the minimum value, the top events are independently and approximately calculated by adopting the minimum cut set to reduce the calculated amount,
the probability of the occurrence of a top-rushing accident top event of the elevator is calculated by adopting the independent approximation of the minimum cut set:
Figure FDA0004143834770000023
wherein P (T) is the probability value of the top event T, K i Is the i-th minimal cut set.
6. A prediction system for a dynamic prediction method of an elevator overhead collision event based on a fault tree according to any one of claims 1 to 5, characterized in that: the system comprises a vibration sensor arranged on a traction machine, wherein the vibration sensor is connected with a fault tree analysis module through a data acquisition module and a data conversion module, and the fault tree analysis module is connected with an intelligent operation and maintenance cloud platform through a wireless transmission module;
the data acquisition module is used for controlling the vibration sensor to work and transmitting the vibration sensor data to the data conversion module;
the data conversion module is used for converting the vibration sensor data of the received analog signals into digital signals and sending the digital signals to the fault tree analysis module;
the fault tree analysis module is used for dividing the fault tree bottom event into a traction machine failure event and other bottom events according to the fault tree, wherein the failure probability conversion is carried out according to the vibration sensor data when the traction machine fails, the quantitative analysis is carried out according to the failure probability synchronization, and the impact top accident failure probability and probability importance key indexes are output and uploaded to the intelligent operation and maintenance cloud platform; aiming at other bottom events, collecting expert judgment language, aggregating expert opinions and calculating fuzzy failure probability through a fuzzy comprehensive evaluation theory; finally, through the characteristics of the top-punching fault tree, quantitative analysis is synchronously carried out, and the probability of occurrence of the top-punching event and some importance risk indexes are dynamically reflected;
the intelligent operation and maintenance cloud platform is used for carrying out dynamic risk assessment and display on the data sent by the fault tree analysis module.
7. An electronic device, comprising:
a processor;
a memory for storing the processor-executable instructions;
wherein the processor is configured to execute the instructions to implement the fault tree based elevator overhead incident dynamic prediction method of any one of claims 1 to 5.
8. A computer readable storage medium, characterized in that instructions in the computer readable storage medium, when executed by a processor of an electronic device, enable the electronic device to perform the fault tree based elevator top impact event dynamic prediction method according to any of claims 1 to 5.
CN202310297965.0A 2023-03-24 2023-03-24 Elevator top-rushing accident dynamic prediction method and system based on fault tree Pending CN116304636A (en)

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Cited By (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN116579669A (en) * 2023-07-12 2023-08-11 中国电子产品可靠性与环境试验研究所（（工业和信息化部电子第五研究所）（中国赛宝实验室）） Reliability evaluation method, reliability evaluation device, computer equipment and storage medium thereof

Cited By (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN116579669A (en) * 2023-07-12 2023-08-11 中国电子产品可靠性与环境试验研究所（（工业和信息化部电子第五研究所）（中国赛宝实验室）） Reliability evaluation method, reliability evaluation device, computer equipment and storage medium thereof
CN116579669B (en) * 2023-07-12 2024-03-26 中国电子产品可靠性与环境试验研究所（（工业和信息化部电子第五研究所）（中国赛宝实验室）） Reliability evaluation method, reliability evaluation device, computer equipment and storage medium thereof

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