CN114655807A - Elevator vibration fault diagnosis equipment - Google Patents
Elevator vibration fault diagnosis equipment Download PDFInfo
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- CN114655807A CN114655807A CN202210269359.3A CN202210269359A CN114655807A CN 114655807 A CN114655807 A CN 114655807A CN 202210269359 A CN202210269359 A CN 202210269359A CN 114655807 A CN114655807 A CN 114655807A
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- B—PERFORMING OPERATIONS; TRANSPORTING
- B66—HOISTING; LIFTING; HAULING
- B66B—ELEVATORS; ESCALATORS OR MOVING WALKWAYS
- B66B5/00—Applications of checking, fault-correcting, or safety devices in elevators
- B66B5/0006—Monitoring devices or performance analysers
- B66B5/0018—Devices monitoring the operating condition of the elevator system
- B66B5/0025—Devices monitoring the operating condition of the elevator system for maintenance or repair
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- B—PERFORMING OPERATIONS; TRANSPORTING
- B66—HOISTING; LIFTING; HAULING
- B66B—ELEVATORS; ESCALATORS OR MOVING WALKWAYS
- B66B5/00—Applications of checking, fault-correcting, or safety devices in elevators
- B66B5/0006—Monitoring devices or performance analysers
- B66B5/0037—Performance analysers
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Abstract
This application is a divisional application with application number 202110124400.3. An elevator vibration fault diagnosis device comprises an acceleration sensor, a vibration sensor and a vibration sensor, wherein the acceleration sensor is used for acquiring the horizontal acceleration and the vertical acceleration of an elevator car; the elevator signal acquisition box is used for running a diagnosis algorithm of the elevator vibration fault; the wireless gateway is used for receiving the data from the acquisition box, uploading the data to the cloud and finally displaying the data on the client terminal; the elevator vibration fault diagnosis equipment realizes elevator vibration fault diagnosis through the following steps: s1, acquiring data; s2, data processing; s3, calculating each vibration characteristic value; s4, calculating each predicted value; s5, normalizing the vibration characteristics; s6, calculating the probability of the fault reason; and S7, determining the fault reason. The acceleration of the elevator car is obtained, the acceleration is deeply excavated, analyzed and processed through the algorithm, the cause of the elevator fault is determined through the acceleration, and the elevator fault hidden danger prediction and positioning function is achieved.
Description
The invention relates to a diagnosis algorithm for elevator vibration faults, which is a divisional application with the original application number of 202110124400.3 and the application date of 2021, 01-29.01 and is named as 'a diagnosis algorithm for elevator vibration faults'.
[ technical field ] A method for producing a semiconductor device
The invention relates to the technical field of elevators, in particular to elevator vibration fault diagnosis equipment.
[ background of the invention ]
At present, elevator internet of things terminal equipment mainly completes tasks of collecting, simply processing and uploading elevator running signals (vibration, door switches, motor current and the like) to the cloud. The acquired signals are hardly subjected to deep analysis processing. Terminal equipment of the internet of things monitoring system in the elevator industry only stops collecting and uploading various data of elevator operation, only carries out data transparent transmission and simple logic judgment on collected elevator operation signals, lacks deep mining analysis and processing of data, and is unstable in network caused by too high requirements on the network and a server for uploaded original data.
[ summary of the invention ]
The invention solves the technical problems that various data of the elevator operation only stay at the acquisition and uploading level and the deep mining analysis and processing of the data are lacked at present, and provides the elevator vibration fault diagnosis equipment.
The invention is realized by the following technical scheme:
an elevator vibration fault diagnosis apparatus characterized in that: comprises that
The acceleration sensor is used for acquiring the horizontal acceleration and the vertical acceleration of the elevator car;
the elevator signal acquisition box is used for running a diagnosis algorithm of the elevator vibration fault;
the wireless gateway is used for receiving the data from the acquisition box, uploading the data to the cloud and finally displaying the data on the client terminal;
the elevator vibration fault diagnosis equipment realizes elevator vibration fault diagnosis through the following steps:
s1, acquiring data, wherein the acceleration sensor acquires the current acceleration of the elevator car, and the acceleration comprises the acceleration in the horizontal direction and the acceleration in the vertical direction;
s2, data processing, namely, differentiating the acceleration signal to the time to obtain the jerk, wherein the acceleration signal to the time comprises differentiating the acceleration in the horizontal direction to the time to obtain the horizontal jerk; the acceleration in the vertical direction is derived from the time to obtain vertical jerk;
s3, calculating vibration characteristic values, wherein the vibration characteristic values comprise a vertical running standard deviation of acceleration change in the vertical direction during the running of the elevator, a horizontal running standard deviation of acceleration change in the horizontal direction during the running of the elevator and a vertical acceleration standard deviation of acceleration change in the vertical direction during the acceleration of the elevator;
s4, calculating each predicted value, recurrently storing each vibration characteristic value in S3 into an array according to the time sequence, and obtaining the predicted value of each vibration characteristic value after predicting for a plurality of cycles by an exponential smoothing prediction method;
s5, vibration characteristic normalization, setting the upper and lower limits of the vibration characteristic value, and normalizing the predicted value by using a membership function;
s6, calculating the probability of the fault reason, setting a Bayesian probability network, designing a Bayesian probability network according to the relation between each vibration characteristic and each fault reason, calculating the father node of the Bayesian probability network, and obtaining the posterior probability of each father node;
s7, determining the fault reason, confirming the maximum father node posterior probability in the posterior probabilities of the father nodes, and outputting the father node corresponding to the father node as the fault reason; or
And confirming the posterior probability of the father node which is greater than the preset fault probability value in the posterior probability of each father node, and outputting the corresponding father node as a fault reason.
In S2, the jerk j is Δ a/Δ t, where Δ a is the acceleration change amount and Δ t is the minimum unit time.
An elevator vibration fault diagnosis apparatus as described above, the jerk j comprising a vertical jerk jzAnd horizontal jerk jXYWherein, in the step (A),
vertical jerk
Wherein a isZIs the vertical acceleration of the sampling period, a'ZVertical acceleration for the last sampling period, T being the sampling period, aZ-a’ZIs the vertical acceleration variation, Δ T ═ T;
horizontal jerk
Wherein a isXIs the X-axis acceleration of the sampling period of a'XAcceleration of the X-axis for the last sampling period, aYIs the Y-axis acceleration of the sampling period of a'YIs the Y-axis acceleration of the last sampling period, T is the sampling period,
for the horizontal acceleration change amount, Δ T is T.
In the elevator vibration failure diagnosis apparatus described above, in S3, the jerk j obtained in S2 is substituted into the standard deviation formula
Calculating the standard deviation of vertical operation, the standard deviation of horizontal operation and the standard deviation of vertical acceleration, wherein sigma is the standard deviation, N is the number of data, jiMu is the average of a plurality of jerks j.
The elevator vibration failure diagnosis apparatus as described above, wherein the step of chronologically recursive storing the vibration characteristic values of S3 into an array at S4 comprises,
s401, setting array serial numbers including 0 to 9;
s402, sequentially storing the array elements according to the array sequence number sequence and corresponding to the array sequence numbers one by one, wherein the array elements comprise S0-S9, and S is the current vibration standard deviation;
s403: forming an array: sequence number: 0,1,2,3,4,5,6,7,8, 9;
elements: s0, s1, s2, s3, s4, s5, s6, s7, s8, s 9;
in S4, the step of exponential smoothing prediction includes:
s404, moving the array elements in the S403 to the direction with the lower sequence number once in sequence in each sampling period;
s405, respectively bringing each vibration characteristic value into respective independent arrays according to a time sequence for operation;
and S406, respectively obtaining the predicted values of the vibration characteristic values after a plurality of sampling periods.
The diagnosis algorithm for the elevator vibration fault comprises the following steps in S5:
s501, setting an upper limit max of a vibration characteristic value and a lower limit min of the vibration characteristic value;
s502, determining a membership function, obtaining a return normalization value,
when x is less than or equal to min, returning the normalization value y to be 0;
when x is larger than or equal to max, returning the normalization value y to be 1;
when x is>min and x<At max, return the normalized value to
The elevator vibration failure diagnosis apparatus as described above, in S6, comprising the steps of:
s601, taking each vibration characteristic value as a child node, taking each fault reason as a father node, wherein the prior probability of each node and the conditional probability between the father node and the child node are obtained according to the elevator maintenance historical record;
s602, taking the normalized value of each vibration characteristic as the latest probability of the child node;
s603, calculating the posterior probability of the parent node of the bayesian network by the following formula,
wherein, P (a | B) is the posterior probability of the parent node, P (B | a) is the conditional probability, P (a) is the prior probability of the parent node, P (B) is the prior probability of the child node, and P (B') is the latest probability of the child node.
According to the elevator vibration fault diagnosis device, the father node comprises control system faults, traction machine system faults, steel wire rope faults, guide rail system faults, artificial shaking and well air flow.
In the elevator vibration failure diagnosis apparatus as described above, a failure probability value of 0.5 is preset in S7.
According to the elevator vibration fault diagnosis equipment, the horizontal direction acceleration comprises the static horizontal acceleration of the elevator when the elevator opens and closes the door, and the vibration characteristic value further comprises the static horizontal vibration standard deviation of the elevator when the elevator opens and closes the door.
Compared with the prior art, the invention has the following advantages:
1. the invention relates to elevator vibration fault diagnosis equipment which mainly runs on an elevator signal acquisition box, and the software implementation mainly comprises the parts of vibration signal preprocessing, data feature extraction, feature value trend prediction, feature value normalization, fault part probability calculation and the like.
2. The invention provides elevator vibration fault diagnosis equipment, which comprises an acceleration sensor, a vibration sensor and a vibration sensor, wherein the acceleration sensor is used for acquiring the acceleration of an elevator car in the horizontal direction and the acceleration of the elevator car in the vertical direction; the elevator signal acquisition box is used for running a diagnosis algorithm of the elevator vibration fault; the wireless gateway is used for receiving the data from the acquisition box, uploading the data to the cloud and finally displaying the data on the client terminal; the elevator vibration fault diagnosis equipment realizes elevator vibration fault diagnosis through the following steps:
s1, acquiring data; s2, data processing; s3, calculating each vibration characteristic value; s4, calculating each predicted value; s5, normalizing the vibration characteristics; s6, calculating the probability of the fault reason; and S7, determining the fault reason. The acceleration of the elevator car is obtained, the acceleration is deeply excavated, analyzed and processed through the algorithm, the cause of the elevator fault is determined through the acceleration, and the elevator fault hidden danger prediction and positioning function is achieved. The invention belongs to an algorithm which is applied to an elevator fault hidden danger early warning system and has a function of predicting a fault hidden danger position of an elevator, so that an elevator property or a maintenance unit can predict and position the fault hidden danger position of the elevator in advance.
3. The invention can provide valuable reference basis for elevator maintenance work. The terminal equipment of the internet of things of the elevator also has certain data analysis and processing capacity, the processing load of the server is shared, and the dependence on the network stability is reduced.
[ description of the drawings ]
In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the drawings needed to be used in the description of the embodiments will be briefly introduced below, and it is obvious that the drawings in the following description are only some embodiments of the present invention, and it is obvious for those skilled in the art to obtain other drawings based on these drawings without creative efforts.
FIG. 1 is a flow chart of the algorithm of the present invention;
FIG. 2 is a schematic view of acceleration and jerk of the present invention;
FIG. 3 is a schematic illustration of eigenvalue normalization of the present invention;
FIG. 4 is a Bayesian network diagram of the present invention;
FIG. 5 is a fault fraction table of an embodiment of the present invention;
fig. 6 is a parent node a priori probability table according to an embodiment of the present invention.
[ detailed description ] embodiments
In order to make the technical problems and the technical solutions of the present invention more clearly understood, the present invention is further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative of the invention and are not intended to limit the invention.
When embodiments of the present invention refer to the ordinal numbers "first", "second", etc., it should be understood that the terms are used for distinguishing only when they do express the ordinal order in context.
In the description of the present invention, it should be noted that, unless otherwise explicitly specified or limited, the terms "mounted," "connected," and "connected" are to be construed broadly, e.g., as meaning either a fixed connection, a removable connection, or an integral connection; can be mechanically or electrically connected; they may be connected directly or indirectly through intervening media, or they may be interconnected between two elements. The specific meanings of the above terms in the present invention can be understood in specific cases to those skilled in the art.
An elevator vibration fault diagnostic algorithm comprising the steps of:
s1, acquiring data, wherein the acceleration sensor acquires the current acceleration of the elevator car, and the acceleration comprises the acceleration in the horizontal direction and the acceleration in the vertical direction;
s2, data processing, namely, differentiating the acceleration signal to the time to obtain the jerk, wherein the acceleration signal to the time comprises differentiating the acceleration in the horizontal direction to the time to obtain the horizontal jerk; the acceleration in the vertical direction is derived from the time to obtain vertical jerk;
further, as a preferred embodiment of the present invention, but not limited thereto, in S2, the jerk j is Δ a/Δ t, where Δ a is the acceleration change amount and Δ t is the minimum unit time.
Further, as a preferred embodiment of the present solution, and not by way of limitation, jerk j comprises vertical jerk jzAnd horizontal jerk jXYWherein, in the step (A),
vertical jerk
Wherein a isZIs the vertical acceleration of the sampling period, a'ZVertical acceleration for the last sampling period, T being the sampling period, aZ-a’ZIs the vertical acceleration variation, Δ T ═ T;
horizontal jerk
Wherein a isXIs the X-axis acceleration of the sampling period of a'XAcceleration of the X-axis for the last sampling period, aYIs the Y-axis acceleration of the sampling period of a'YIs the Y-axis acceleration of the last sampling period, T is the sampling period,
is horizontalThe acceleration change amount Δ T is T.
The variation of the acceleration in the vertical direction due to the operation of the elevator and the difference of the sensors caused by production, installation and the like all affect the extraction of the vibration acceleration signal. The acceleration signal is subjected to time derivation, namely, jerk is obtained, and the influences caused by elevator acceleration and deceleration and zero offset of the sensor can be eliminated. Because the frequency of the vibration signal is far higher than the frequency of the acceleration and deceleration of the elevator, the influence of the acceleration and deceleration can be eliminated by reasonably selecting the derivative time, and the characteristic information of the vibration signal is reserved. As shown in fig. 2, the blue curve is acceleration and the yellow curve is jerk.
S3, calculating vibration characteristic values, wherein the vibration characteristic values comprise a vertical running standard deviation of acceleration change in the vertical direction during the running of the elevator, a horizontal running standard deviation of acceleration change in the horizontal direction during the running of the elevator and a vertical acceleration standard deviation of acceleration change in the vertical direction during the acceleration of the elevator;
further, as a preferred embodiment of the present invention, not limited thereto, in S3, the jerk j obtained in S2 is substituted into the standard deviation equation
Calculating the standard deviation of vertical operation, the standard deviation of horizontal operation and the standard deviation of vertical acceleration, wherein sigma is the standard deviation, N is the number of data, jiMu is the average of a plurality of jerks j.
S4, calculating each predicted value, recurrently storing each vibration characteristic value in S3 into an array according to the time sequence, and obtaining the predicted value of each vibration characteristic value after predicting for a plurality of cycles by an exponential smoothing prediction method;
further, as a preferred embodiment of the present invention, but not limited thereto, the step of recursively storing the vibration feature values in S3 into an array in S4 in time series includes,
s401, setting array serial numbers including 0 to 9; the smaller the sequence number is, the earlier the data is stored;
s402, sequentially storing the array elements according to the sequence of the array sequence numbers and corresponding to the array sequence numbers one by one, wherein the array elements comprise S0-S9;
s403: forming an array: sequence number: 0,1,2,3,4,5,6,7,8, 9;
elements: s0, s1, s2, s3, s4, s5, s6, s7, s8, s 9;
in S4, the step of exponential smoothing prediction includes:
s404, moving the array elements in the S403 to the direction with the lower sequence number once in sequence in each sampling period;
after the array has been moved once, the array is moved, as shown below,
array: sequence number: 0,1,2,3,4,5,6,7,8, 9;
elements: s1, s2, s3, s4, s5, s6, s7, s8, s9, s;
wherein s is the standard deviation of the vibration.
S405, respectively bringing each vibration characteristic value into respective independent arrays according to a time sequence for operation;
and S406, respectively obtaining the predicted values of the vibration characteristic values after a plurality of sampling periods.
The prediction method of the array data adopts the exponential smoothing prediction method as described above, and the three vibration signals are respectively brought into respective independent arrays according to the time sequence to carry out operation, so that three vibration prediction values are finally obtained.
S5, vibration characteristic normalization, setting the upper and lower limits of the vibration characteristic value, and normalizing the predicted value by using a membership function; as shown in figure 3 of the drawings,
further, as a preferred embodiment of the present invention, but not limited thereto, S5 includes the steps of:
s501, setting an upper limit max of a vibration characteristic value and a lower limit min of the vibration characteristic value;
s502, determining a membership function, obtaining a return normalization value,
when x is less than or equal to min, returning the normalization value y to be 0;
when x is larger than or equal to max, returning the normalization value y to be 1;
when x is>min and x<At max, return the normalized value to
In the present embodiment, the current vibrationCharacteristic value x is 0.3, lower limit min: 0.1, upper limit max: 0.5; substituting x into the membership function calculation, returning a normalized value because x is 0.3
S6, calculating the probability of the fault reason, setting a Bayesian probability network, designing a Bayesian probability network according to the relation between each vibration characteristic and each fault reason, calculating the father node of the Bayesian probability network, and obtaining the posterior probability of each father node; as shown in figure 4 of the drawings,
further, as a preferred embodiment of the present invention, but not limited thereto, S6 includes the steps of:
s601, taking each vibration characteristic value as a child node, taking each fault reason as a father node, wherein the prior probability of each node and the conditional probability between the father node and the child node are obtained according to the elevator maintenance historical record;
s602, taking the normalized value of each vibration characteristic as the latest probability of the child node;
s603, calculating the posterior probability of the parent node of the bayesian network by the following formula,
wherein, P (A | B) is the posterior probability of the father node, P (B | A) is the conditional probability, P (A) is the prior probability of the father node, P (B) is the prior probability of the child node, and P (B') is the latest probability of the child node.
And designing a Bayesian probability network according to the relation between each vibration characteristic and each fault reason. And obtaining the prior probability of each node and the conditional probability between the father node and the child node according to the elevator maintenance historical record by taking each vibration characteristic value as a child node and taking each fault reason as a father node. And taking the normalized value of each vibration characteristic as the latest probability of the child node, so as to calculate the posterior probability of the parent node of the Bayesian network.
The probability of each child node of the probability network represents the size of each vibration feature and is represented by the normalized value of each vibration feature.
The probability of each father node of the probabilistic network represents the possible degree of each fault reason, and the prior probability of each father node of the probabilistic network is obtained from the maintenance record of a large sample.
As shown in fig. 5 to 6, the elevator maintenance statistics show: in all elevator faults, the ratio caused by the traction system is 40%, the ratio caused by the electric control system is 20%, the ratio caused by the guide rail system is 30%, and the ratio caused by other reasons is 10%.
From the above, the prior probabilities of the parent nodes are 0.4, 0.2, 0.3, and 0.1, respectively. The conditional probability between the parent node and the child node is given by expert experience. For example, a conditional probability table for a child node (vertical vibration at startup) is initialized to:
| electric control system | Traction system | Guide rail system | Vibrating vertically at start-up | |
| OK | OK | OK | 0 | |
| OK | OK | BAD | 0.1 | |
| OK | BAD | OK | 0.5 | |
| OK | BAD | BAD | 0.6 | |
| BAD | OK | OK | 0.4 | |
| BAD | OK | BAD | 0.5 | |
| BAD | BAD | OK | 0.9 | |
| BAD | | BAD | 1 |
When the electric control system, the traction system and the guide rail system are all OK, the probability of vertical vibration is 0 when the machine is started.
When the electric control system OK, the traction system BAD and the guide rail system BAD are started, the probability of vertical vibration is 0.6.
Further, as a preferred embodiment of the present solution, but not limited thereto, the parent node includes a control system failure, a traction machine system failure, a wire rope failure, a guide rail system failure, a human sway, and a hoistway air flow.
S7, determining the fault reason, confirming the maximum father node posterior probability in the posterior probabilities of the father nodes, and outputting the father node corresponding to the father node as the fault reason; or
And confirming the parent node posterior probability which is greater than the preset fault probability value in the posterior probabilities of the parent nodes, and outputting the corresponding parent node as a fault reason.
Further, as a preferred embodiment of the present solution, but not limited thereto, a failure probability value of 0.5 is preset in S7.
And selecting a corresponding standard for determining the fault reason according to the requirement.
The embodiment of the invention provides a diagnosis algorithm of elevator vibration faults, and S1, data is obtained; s2, data processing; s3, calculating each vibration characteristic value; s4, calculating each predicted value; s5, normalizing the vibration characteristics; s6, calculating the probability of the fault reason; and S7, determining the fault reason. The acceleration of the elevator car is obtained, the acceleration is subjected to deep excavation analysis and processing through the algorithm of the invention, and the cause of the elevator fault is determined through the acceleration. The invention belongs to an algorithm which is applied to an elevator fault hidden danger early warning system and has the function of predicting the elevator fault hidden danger position, so that an elevator property or a maintenance unit can predict and position the fault hidden danger position of an elevator in advance.
The embodiment of the invention can provide a valuable reference basis for elevator maintenance work. The terminal equipment of the internet of things of the elevator also has certain data analysis and processing capacity, the processing load of the server is shared, and the dependence on the network stability is reduced.
Compared with the prior art, the invention has the following advantages: the method comprises the steps of adopting an extraction method of elevator vibration signal characteristics, a trend prediction method of vibration characteristic values, a method for calculating the probability of fault reasons by using a Bayesian probability network and the like. The method for extracting the elevator vibration signal features by using the pure time domain mode is simple and effective, and is suitable for terminal equipment with low computing capability. The mechanism of predicting and classifying the hidden trouble is really realized, the part codes of the hidden trouble of the elevator are uploaded to the cloud, the name of the elevator part with the hidden trouble is output by matching with the client terminal APP, and the mechanism has obvious practical significance for elevator maintenance work.
Further, as a preferred embodiment of the present solution without limitation, the horizontal direction acceleration includes a static horizontal acceleration when the elevator opens and closes the door, and the vibration characteristic value further includes a static horizontal vibration standard deviation when the elevator opens and closes the door.
The embodiment of the invention also discloses elevator vibration fault diagnosis equipment, which comprises
The acceleration sensor is used for acquiring the horizontal acceleration and the vertical acceleration of the elevator car;
the elevator signal acquisition box is used for running a diagnosis algorithm of the elevator vibration fault;
and the wireless gateway is used for receiving the data from the acquisition box, uploading the data to the cloud and finally displaying the data on the client terminal.
The elevator vibration fault diagnosis algorithm mainly runs on an elevator signal acquisition box, and the software implementation mainly comprises the parts of vibration signal preprocessing, data feature extraction, feature value trend prediction, feature value normalization, fault part probability calculation and the like.
The working principle of the embodiment is as follows:
the embodiment of the invention provides a diagnosis algorithm of elevator vibration faults, and S1, data is obtained; s2, data processing; s3, calculating each vibration characteristic value; s4, calculating each predicted value; s5, normalizing the vibration characteristics; s6, calculating the probability of the fault reason; and S7, determining the fault reason. The acceleration of the elevator car is obtained, the acceleration is subjected to deep excavation analysis and processing through the algorithm of the invention, and the cause of the elevator fault is determined through the acceleration. The invention belongs to an algorithm which is applied to an elevator fault hidden danger early warning system and has the function of predicting the elevator fault hidden danger position, so that an elevator property or a maintenance unit can predict and position the fault hidden danger position of an elevator in advance.
The embodiment of the invention can provide a valuable reference basis for elevator maintenance work. The terminal equipment of the internet of things of the elevator also has certain data analysis and processing capacity, the processing load of the server is shared, and the dependence on the network stability is reduced.
The foregoing is illustrative of embodiments provided in connection with the detailed description and is not intended to limit the disclosure to the particular forms set forth herein. Similar to the structure of the method, or several technical deductions or substitutions made on the premise of the conception of the present application, should be regarded as the protection scope of the present application.
Claims (10)
1. An elevator vibration fault diagnosis apparatus characterized in that: comprises that
The acceleration sensor is used for acquiring the acceleration in the horizontal direction and the acceleration in the vertical direction of the elevator car;
the elevator signal acquisition box is used for running a diagnosis algorithm of the elevator vibration fault;
the wireless gateway is used for receiving the data from the acquisition box, uploading the data to the cloud and finally displaying the data on the client terminal;
the elevator vibration fault diagnosis equipment realizes elevator vibration fault diagnosis through the following steps:
s1, acquiring data, wherein the acceleration sensor acquires the current acceleration of the elevator car, and the acceleration comprises the acceleration in the horizontal direction and the acceleration in the vertical direction;
s2, data processing, namely, differentiating the acceleration signal to the time to obtain the jerk, wherein the acceleration signal to the time comprises differentiating the acceleration in the horizontal direction to the time to obtain the horizontal jerk; the acceleration in the vertical direction is derived from the time to obtain vertical jerk;
s3, calculating vibration characteristic values, wherein the vibration characteristic values comprise a vertical running standard deviation of acceleration change in the vertical direction during the running of the elevator, a horizontal running standard deviation of acceleration change in the horizontal direction during the running of the elevator and a vertical acceleration standard deviation of acceleration change in the vertical direction during the acceleration of the elevator;
s4, calculating each predicted value, recurrently storing each vibration characteristic value in S3 into an array according to the time sequence, and obtaining the predicted value of each vibration characteristic value after predicting for a plurality of cycles by an exponential smoothing prediction method;
s5, vibration characteristic normalization, wherein upper and lower limits of the vibration characteristic value are set, and the predicted value is normalized by utilizing the membership function;
s6, calculating the probability of the fault reason, setting a Bayesian probability network, designing a Bayesian probability network according to the relationship between each vibration characteristic and each fault reason, calculating the father nodes of the Bayesian probability network, and obtaining the posterior probability of each father node;
s7, determining the fault reason, confirming the maximum father node posterior probability in the posterior probabilities of the father nodes, and outputting the father node corresponding to the father node as the fault reason; or
And confirming the posterior probability of the father node which is greater than the preset fault probability value in the posterior probability of each father node, and outputting the corresponding father node as a fault reason.
2. The elevator vibration failure diagnosis apparatus according to claim 1, wherein: in S2, jerk j is Δ a/Δ t, where Δ a is the amount of change in acceleration and Δ t is the minimum unit time.
3. The elevator vibration failure diagnosis apparatus according to claim 2, wherein: jerk j includes vertical jerk jzAnd horizontal jerk jXYWherein, in the step (A),
vertical jerk
Wherein a isZIs the vertical acceleration of the sampling period, a'ZVertical acceleration for the last sampling period, T being the sampling period, aZ-a’ZIs the vertical acceleration variation, Δ T ═ T;
horizontal jerk
Wherein a isXIs the X-axis acceleration of the sampling period of a'XAcceleration of the X-axis for the last sampling period, aYIs the Y-axis acceleration of the sampling period of a'YIs the Y-axis acceleration of the last sampling period, T is the sampling period,
for the horizontal acceleration change amount, Δ T is T.
4. The elevator vibration failure diagnosis apparatus according to claim 1, characterized in that: in S3, the jerk j obtained in S2 is substituted into the standard deviation equation
Calculating the standard deviation of vertical operation, the standard deviation of horizontal operation and the standard deviation of vertical acceleration, wherein sigma is the standard deviation, N is the number of data, jiMu is the average of a plurality of jerks j.
5. The vibration fault diagnosis apparatus for an elevator according to claim 1, wherein the step of chronologically recursive storing of the vibration characteristic values of S3 into an array at S4 comprises,
s401, setting array serial numbers including 0 to 9;
s402, sequentially storing the array elements according to the array sequence number sequence and corresponding to the array sequence numbers one by one, wherein the array elements comprise S0-S9, and S is the current vibration standard deviation;
s403: forming an array: sequence number: 0,1,2,3,4,5,6,7,8, 9;
elements: s0, s1, s2, s3, s4, s5, s6, s7, s8, s 9;
in S4, the step of exponential smoothing prediction includes:
s404, sequentially moving the array elements in the S403 to the direction with the lower sequence number once in each sampling period;
s405, respectively bringing each vibration characteristic value into respective independent arrays according to a time sequence for operation;
and S406, respectively obtaining the predicted values of the vibration characteristic values after a plurality of sampling periods.
6. The elevator vibration fault diagnosis apparatus according to claim 1, characterized by comprising, in S5, the steps of:
s501, setting an upper limit max of a vibration characteristic value and a lower limit min of the vibration characteristic value;
s502, determining a membership function, obtaining a return normalization value,
when x is less than or equal to min, returning the normalization value y to be 0;
when x is larger than or equal to max, returning the normalization value y to be 1;
when x is>min and x<At max, return the normalized value to
7. The elevator vibration fault diagnosis apparatus according to claim 1, characterized by comprising, in S6, the steps of:
s601, taking each vibration characteristic value as a child node, taking each fault reason as a father node, wherein the prior probability of each node and the conditional probability between the father node and the child node are obtained according to the elevator maintenance historical record;
s602, taking the normalized value of each vibration characteristic as the latest probability of the child node;
s603, calculating the posterior probability of the parent node of the bayesian network by the following formula,
wherein, P (A | B) is the posterior probability of the father node, P (B | A) is the conditional probability, P (A) is the prior probability of the father node, P (B) is the prior probability of the child node, and P (B') is the latest probability of the child node.
8. The elevator vibration fault diagnosis device according to claim 7, wherein the father node includes control system failure, traction machine system failure, wire rope failure, guide rail system failure, human sway, and hoistway airflow.
9. The elevator vibration failure diagnosis apparatus according to claim 1, wherein: in S7, a failure probability value of 0.5 is preset.
10. The elevator vibration failure diagnosis apparatus according to claim 1, wherein: the horizontal direction acceleration comprises static horizontal acceleration when the elevator opens and closes the door, and the vibration characteristic value further comprises static horizontal vibration standard deviation when the elevator opens and closes the door.
Priority Applications (1)
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