CN110817636A - Elevator door system fault diagnosis method, device, medium and equipment - Google Patents
Elevator door system fault diagnosis method, device, medium and equipment Download PDFInfo
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- CN110817636A CN110817636A CN201911144060.XA CN201911144060A CN110817636A CN 110817636 A CN110817636 A CN 110817636A CN 201911144060 A CN201911144060 A CN 201911144060A CN 110817636 A CN110817636 A CN 110817636A
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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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Abstract
The invention relates to a fault diagnosis method, a fault diagnosis device, a fault diagnosis medium and equipment for an elevator door system. According to the scheme provided by the embodiment of the invention, the appointed characteristic parameters can be obtained through the operation data generated in the operation process of the elevator door system, and the type and the reason of the fault can be judged by utilizing the appointed characteristic parameters and the pre-trained fault diagnosis model. Based on the operation data, the fault type and the reason are judged through a pre-trained fault diagnosis model, so that the fault diagnosis is refined, and the accuracy of the fault diagnosis is ensured.
Description
Technical Field
The invention relates to the technical field of elevators, in particular to a method, a device, a medium and equipment for diagnosing faults of an elevator door system.
Background
With the increase of high-rise buildings, elevators are increasingly becoming indispensable vertical transportation means. Through years of development, the elevator keeping quantity in China is greatly increased and is increasingly saturated. Data display shows that the quantity of elevators in China reaches 554 thousands of elevators as the year of 2017. The probability of failure occurrence is remarkably improved along with the accumulation of the running time of the elevator, wherein the failure occurrence is frequent due to the frequent opening and closing actions of an elevator door motor and is the most main component of elevator accidents, and more than 80 percent of elevator failures and more than 70 percent of elevator failures are caused by the problem of a door system. Elevator accidents caused by elevator door system faults are numerous and serious in consequence, so that when the elevator door system is in a sub-health state or a fault state, fault diagnosis and classification are carried out on the elevator door, the elevator door can be maintained as required, measures are taken to avoid faults, and the problems which need to be faced in theoretical research and actual engineering are solved.
At present, the fault diagnosis method for the elevator door system is implemented in the following specific mode:
inputting elevator flow data, sliding window t
Output failure type
The method comprises the following steps:
step 1: receiving elevator stream data D according to the size of the sliding window, namely dividing micro data batches according to time t;
step 2: data preprocessing, namely filtering useless state parameters, extracting an elevator Identification (ID) as an index (key) value, and recording the elevator number of the elevator door fault and distinguishing flow data of different elevators;
step 3: clustering data according to the Key value, and gathering the data of the same elevator into the same group;
step 4: taking out the first piece of data in the group and matching the initial running state of the micro-batch data according to the state transition diagram, and recording as S1;
step 5: matching the subsequent data with each state in the state transition diagram one by one, if the current data is not successfully matched, indicating that the current elevator is being transferred from one state to the next state until the next determined state is successfully matched, and recording as S2;
step 6: judging whether the transition of the state S1 and the state S2 is a state transition process related to opening and closing the door or not, if so, executing Step7, otherwise, executing Step 8;
Step7：
if (if) S1 is the elevator stopped and S2 is the elevator arriving at the station
Then (then) S2 if the next state is not the elevator door open state, then the failure is that the elevator arrives at the station and does not open the door;
if S1 door for elevator
the then if exceeds the threshold time t and can not be transferred to the door closing in-place state
the then if light curtain signal is always 1
the then fault elevator is stuck by people and objects;
the else then fault is that the elevator cannot be normally closed;
if S1 is the running state of elevator
If the door lock of the then if hall door is opened, the door is opened in operation;
if S1 is at rest state and the door opening button is on state
the then fault is a fault that the door cannot be opened normally when a door opening button is pressed;
step 8: if the elevator has no fault, returning to Step5 to process the next group of data;
the method for diagnosing the fault of the elevator door system provided by the prior art detects the specific fault of the elevator door by analyzing the elevator flow data, and the method comprises the steps of firstly detecting the running state of the elevator at the current moment by setting the sliding window, and then detecting the fault type of the elevator door according to a detection algorithm.
The existing method at least has the following problems, which cause that the fault diagnosis is not accurate enough:
1) based on the fault detection algorithm of the rule, the fault diagnosis is carried out only through the running state of the elevator;
2) based on a regular fault detection algorithm, only a corresponding out-of-control fault in an out-of-control state of an elevator door system can be detected, and a slight fault which can cause the elevator door system to enter the out-of-control state can not be detected in the out-of-control state of the elevator door system;
3) the fault detection algorithm based on the rule can only diagnose simple fault types and can not judge fault reasons.
Disclosure of Invention
The embodiment of the invention provides a fault diagnosis method, a fault diagnosis device, a fault diagnosis medium and equipment for an elevator door system, which are used for solving the problem that the fault diagnosis of the elevator door system is not accurate enough.
The invention provides a fault diagnosis method for an elevator door system, which comprises the following steps:
acquiring operation data generated in the operation process of an elevator door system;
performing feature extraction and conversion on the operating data to obtain specified feature parameters;
and taking the specified characteristic parameters as the input of a fault diagnosis model trained in advance, and determining the type and the reason of the fault by using the fault diagnosis model.
The present invention also provides an elevator door system fault diagnosis device, the device including:
the data acquisition module is used for acquiring operation data generated in the operation process of the elevator door system;
the parameter acquisition module is used for extracting and converting the characteristics of the operating data acquired by the data acquisition module to acquire specified characteristic parameters;
and the diagnosis module is used for taking the specified characteristic parameters obtained by the parameter acquisition module as the input of a fault diagnosis model trained in advance and determining the type and the reason of the fault by using the fault diagnosis model.
The present invention also provides a non-volatile computer storage medium having stored thereon an executable program for execution by a processor to implement the method as described above.
The invention also provides a fault diagnosis device of the elevator door system, which comprises at least one processor; and a memory communicatively coupled to the at least one processor; wherein the memory stores instructions executable by the at least one processor to enable the at least one processor to perform the steps of the method as described above.
According to the scheme provided by the embodiment of the invention, the appointed characteristic parameters can be obtained through the operation data generated in the operation process of the elevator door system, and the type and the reason of the fault can be judged by utilizing the appointed characteristic parameters and the pre-trained fault diagnosis model. Based on the operation data, the fault type and the reason are judged through a pre-trained fault diagnosis model, so that the fault diagnosis is refined, and the accuracy of the fault diagnosis is ensured.
Additional features and advantages of the invention will be set forth in the description which follows, and in part will be obvious from the description, or may be learned by practice of the invention. The objectives and other advantages of the invention will be realized and attained by the structure particularly pointed out in the written description and claims hereof as well as the appended drawings.
Drawings
In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly described below, it is obvious that the drawings in the following description are only some embodiments of the present invention, and for those skilled in the art, other drawings can be obtained according to the drawings without creative efforts.
Fig. 1 is a schematic flow chart of a method for diagnosing a fault of an elevator door system according to an embodiment of the present invention;
FIG. 2 is a schematic diagram of a model training and using process according to a first embodiment of the present invention;
fig. 3 is a schematic structural diagram of an elevator door system fault diagnosis device according to a third embodiment of the present invention;
fig. 4 is a schematic structural diagram of an elevator door system fault diagnosis apparatus according to a fourth embodiment of the present invention.
Detailed Description
Aiming at the problem that the fault diagnosis of the elevator door system is not accurate enough by the conventional fault diagnosis method for the elevator door system, the embodiment of the invention provides a method for performing fault diagnosis through a fault diagnosis model based on operation data.
In order to make the objects, technical solutions and advantages of the present invention clearer, the present invention will be described in further detail with reference to the accompanying drawings, and it is apparent that the described embodiments are only a part of the embodiments of the present invention, not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
It should be noted that, the "plurality" or "a plurality" mentioned herein means two or more. "and/or" describes the association relationship of the associated objects, meaning that there may be three relationships, e.g., a and/or B, which may mean: a exists alone, A and B exist simultaneously, and B exists alone. The character "/" generally indicates that the former and latter associated objects are in an "or" relationship.
The terms "first," "second," and the like in the description and in the claims, and in the drawings described above, are used for distinguishing between similar elements and not necessarily for describing a particular sequential or chronological order. It is to be understood that the data so used is interchangeable under appropriate circumstances such that the embodiments of the invention described herein are capable of operation in sequences other than those illustrated or described herein.
Furthermore, the terms "comprises," "comprising," and "having," and any variations thereof, are intended to cover a non-exclusive inclusion, such that a process, method, system, article, or apparatus that comprises a list of steps or elements is not necessarily limited to those steps or elements expressly listed, but may include other steps or elements not expressly listed or inherent to such process, method, article, or apparatus.
Example one
An embodiment of the present invention provides a method for diagnosing a fault of an elevator door system, where the flow of the steps of the method may be as shown in fig. 1, and the method includes:
step 101, acquiring operation data.
In this step, the operation data generated during the operation of the elevator door system can be obtained.
The operational data may be any data generated during operation of the elevator door system and may include, for example and without limitation, electrical signal data, control signal data, elevator door opening and closing speed data, elevator door opening and closing position data, and elevator door operational state data. The elevator door system fault diagnosis is carried out through the acquired diversified operation data, and the accuracy of the fault diagnosis can be effectively ensured.
In one possible implementation, the electrical signal data may include, but is not limited to, current data, power data, and the control signal data may include, but is not limited to, gating signal data.
And 102, acquiring characteristic parameters.
In this step, feature extraction and conversion may be performed on the operation data acquired in step 101 to obtain specified feature parameters, which are used to input the specified feature parameters into a fault diagnosis model trained in advance to perform fault diagnosis.
And 103, carrying out fault diagnosis.
In this step, the specified characteristic parameters acquired in step 102 may be used as input of a fault diagnosis model trained in advance, and the type of the fault and the cause of the fault may be determined by using the fault diagnosis model.
In this embodiment, the types of faults determined by the fault diagnosis model may include a minor fault and an uncontrolled fault.
A minor fault may be understood as a fault that may cause the elevator door system to enter an out-of-control state if the elevator door system is not yet out-of-control, i.e., may be understood as a fault that may cause the elevator door system to enter an out-of-control state if the elevator door system is in a sub-health state.
An out-of-control fault can be understood as a fault in which the elevator door system enters an out-of-control state in an out-of-control state of the elevator door system.
That is, in the present embodiment, it is possible to perform the fault diagnosis not only for the uncontrolled state of the elevator door system but also for the sub-health state of the elevator door system. Through fault diagnosis under the sub-health state, the elevator door system can be effectively prevented from entering an out-of-control state, and the utilization rate of the elevator door system is improved.
When the determined type of fault is a minor fault, the determined cause of the fault may be any cause causing the minor fault, for example, but not limited to, the determined cause of the fault may include at least one of minor friction of a guide rail, wear of a door slider, wear of a hanging wheel, and increase of vibration of a door leaf.
When the determined type of the fault is an uncontrolled fault, the determined cause of the fault may be any cause causing the uncontrolled fault, for example, the determined cause of the fault may include, but is not limited to, at least one of an unhooking landing door, a foreign object stop door, a man-made stop door, and a wind pressure fault.
In other words, in this embodiment, the fault type is determined, and the cause of the fault can be further determined, so that the fault diagnosis is refined, and the fault diagnosis accuracy is improved. In addition, the reason of confirming the trouble still is favorable to alleviateing maintenance personal's work load, and help maintenance personal to arrange reasonable maintenance strategy reduces the degree of difficulty of troubleshooting for the speed of troubleshooting.
In one possible implementation, the fault diagnosis model may be constructed based on, but not limited to, a gradient-boosted tree algorithm (GBDT).
The following describes a process of training a fault diagnosis model constructed based on GBDT.
1. A training sample z is obtained.
1) Forming a feature matrix X using feature parameters of normal operation of an elevator door system1Forming a characteristic matrix (X) using characteristic parameters of the elevator door system in the out-of-control state and in the sub-health state2,X3,···,Xn) And obtained according to the experimentStatus tag composition of record mark y ═ y1,y2,····,yn)。
The sub-health state of the elevator door system can include but is not limited to the states of slight friction of a guide rail, abrasion of a door sliding block, abrasion of a hanging wheel, increase of vibration of a door leaf and the like, and the out-of-control state of the elevator door system can include but is not limited to the states of unhooking a landing door, a foreign body stop door, a man-made stop door, wind pressure failure and the like.
The obtaining of the characteristic parameters may include:
and acquiring operation data generated in the operation process of the elevator door system. The operational data may include, but is not limited to, current data, power data, gate signal data, elevator door opening and closing speed data, and the like.
And performing feature extraction and conversion on the operating data to obtain specified feature parameters. In one possible implementation, 15 characteristic parameters may be obtained, which are Q-axis current accumulated electric energy, D-axis current accumulated electric energy, sum of speed errors, speed error square or speed error count value, speed error positive maximum value, speed error negative maximum value, position where speed error positive maximum value occurs, position where speed error negative maximum value occurs, current state start position, current end start position, sum of Iq positive values, sum of Iq negative values, sum of Id positive values, sum of Id negative values, and mechanical energy.
The Q axis and the D axis are two coordinate axes used for describing frequency conversion of a three-phase asynchronous motor in an elevator door system;
the accumulated electric energy of the Q-axis current can be obtained according to the Q-axis current and the motor voltage in the acquisition period;
the accumulated electric energy of the D-axis current can be obtained according to the D-axis current and the motor voltage in the acquisition period;
the sum of speed errors, the square of the speed errors, a speed error counting value, a speed error positive maximum value and a speed error negative maximum value can be obtained according to a plurality of running speeds of the elevator door in the running process in the acquisition period;
the method can obtain the errors between the running speeds and the standard speed respectively according to the running speeds of the elevator door in the running process in the acquisition period, and extract the position of the cage corresponding to the maximum positive error of the running speed (namely the position where the positive maximum value of the speed error occurs) from a plurality of positions of the cage in the running process of the elevator door;
the method can obtain the errors between the running speeds and the standard speed respectively according to the running speeds of the elevator door in the running process in the acquisition period, and extract the position of the cage corresponding to the maximum negative error of the running speed (namely the position where the negative maximum value of the speed error occurs) from a plurality of positions of the cage in the running process of the elevator door;
the method can obtain the initial position of the elevator car (namely the initial position of the current state) when the elevator door system is in the current state from a plurality of positions of the elevator car in the running process of the elevator door;
the end position (namely the current end starting position) of the elevator car when the elevator door system changes the current state can be obtained from a plurality of positions of the elevator car in the running process of the elevator door;
the sum of the positive values of Iq is the sum of the positive values of the two-phase current corresponding to the collected Q axis;
the sum of the Iq negative values is the sum of the negative values of the two-phase current corresponding to the collected Q axis;
the sum of the positive values of the Id is the sum of the positive values of the two-phase current corresponding to the collected D axis;
the sum of the negative values of Id is the sum of the negative values of the two-phase current corresponding to the collected D axis;
the heat energy consumed by the motor can be obtained according to the motor voltage and the elevator loop resistance in the acquisition period, and the difference value between the electric energy and the heat energy consumed by the motor in the acquisition period is obtained and used as the mechanical energy (namely the mechanical energy) of the motor.
2) And (6) carrying out data standardization processing.
Mixing X1And (X)2,X3,···,Xn) The X of the composition is normalized to obtain a normalized feature matrix X ═ X1,x2,x3,···,xn). The normalization formula is shown below.
x＝(X-μ)/σ (1)
Where μ and σ are mean and covariance, respectively.
3) And forming a training sample.
X and y are combined to form a data training sample z, z { (x)1,y1)，(x2,y2)，(x3,y3)，…，(xn,yn)}。
2. Based on the training samples z, a fault diagnosis model of the elevator door system is constructed using GBDT.
Assume that the fault diagnosis model F (x; P) is represented as follows:
wherein β represents the weight of each base learner model and α represents the parameters of each base learner model.
Model parametersCan be expressed by the following formula:
equation (3) shows that for M sample points (xi, yi), the loss function under the model F (x; P) is calculated, and the optimal solution P can minimize the loss function. Written in the form of a gradient descent is represented as follows:
parameter β calculated by equation (4)n,αnFor ensuring the model Fn(x) Is the previous model Fn-1(x) The loss function falls in the fastest direction. For each data point xiAll can obtain a gn(xi) Finally, a complete gradient descent direction can be obtained:
to ensure Fn(x) Can be in gn(x) In the direction of (a), the above equation (5) can be optimized using the least squares method to obtain:
calculated to obtain αnAfter the value of (2), β can be calculatednThe formula is as follows:
the finally combined fault diagnosis model is as follows:
Fn(x)＝Fn-1(x)+βnh(x；αn) (8)
after the fault diagnosis model constructed based on the GBDT is obtained through training, the operation data generated in the operation process of the elevator door system can be obtained in real time (or non-real time), and fault diagnosis is carried out according to the obtained operation data.
In summary, the process of training to obtain the fault diagnosis model and using the fault diagnosis model may be as shown in fig. 2, and includes: acquiring running data and a state label generated when an elevator door system runs; data standardization processing; forming a training sample, and establishing a GBDT-based fault diagnosis model based on the training sample; and performing fault diagnosis on the acquired operation data.
The scheme provided by the first embodiment of the invention is explained by a specific embodiment.
Example two
The second embodiment of the invention provides a fault diagnosis method for an elevator door system, which comprises the following steps:
1. and training to obtain a fault diagnosis model constructed based on the GBDT.
The fault diagnosis model constructed based on the GBDT obtained by training is described in the first embodiment, and is not described herein again.
2. For the operation data (which can be the operation data obtained in real time) of the elevator door system to be evaluated, after the characteristic parameters corresponding to the operation data to be evaluated are subjected to standardization processing, a fault diagnosis model obtained by training is started, and fault diagnosis is performed for the operation data to be evaluated. For example, it is known from fault diagnosis that the type of fault of the elevator door system is a minor fault, and the cause of the fault is sheave wear.
3. And sending an alarm prompt to prompt the type of the fault and the reason of the fault for maintenance personnel so as to help the maintenance personnel to arrange the maintenance strategy more reasonably.
4. And (4) repeating the steps 2-3 according to the subsequent operation data to be evaluated.
In the solutions provided in the first and second embodiments of the present invention, the specific fault type and the fault occurrence component can be determined by the fault diagnosis model, so that the accuracy of fault diagnosis is improved, and maintenance personnel can be helped to arrange a reasonable maintenance strategy.
In addition, in the scheme provided by the embodiment of the invention, the data input by the model is more comprehensive, for example, the current data generated when the elevator door system operates can be considered, and the speed data, the position data and the state data generated in the operation process of the elevator door system can also be considered, so that the accuracy of fault diagnosis is effectively improved.
In addition, the fault diagnosis is carried out through the fault diagnosis model, so that the fault of the elevator door system in the sub-health state or the fault in the out-of-control state can be judged. Preferably, the fault diagnosis model can be constructed based on the GBDT to improve the accuracy of fault diagnosis.
Namely, according to the scheme provided by the embodiment of the invention, the fault diagnosis can be carried out on the operation state of the elevator door system, and the abnormal operation state of the elevator door system can be identified. The method and the system can diagnose the fault of the elevator door system in the out-of-control state and the sub-health state.
It should be noted that, when the fault of the elevator door system in the sub-health state is diagnosed, a slight fault can be checked before the elevator door system enters the out-of-control state, so that the situation that the elevator door system cannot be used due to the fact that the elevator door system enters the out-of-control state is avoided.
Corresponding to the method provided in the first embodiment, the following apparatuses are provided.
EXAMPLE III
A third embodiment of the present invention provides an elevator door system fault diagnosis apparatus, which may be configured as shown in fig. 3, and includes:
the data acquisition module 11 is used for acquiring operation data generated in the operation process of the elevator door system;
the parameter acquisition module 12 is configured to perform feature extraction and conversion on the operating data acquired by the data acquisition module to obtain specified feature parameters;
the diagnosis module 13 is configured to use the specified characteristic parameters obtained by the parameter obtaining module as inputs of a fault diagnosis model trained in advance, and determine the type and cause of the fault by using the fault diagnosis model.
The operation data acquired by the data acquisition module 11 includes electric signal data, control signal data, elevator door opening and closing speed data, elevator door opening and closing position data, and elevator door operation state data.
The fault diagnosis model trained in advance in the diagnosis module 13 is constructed based on GBDT.
The types of faults determined by the diagnostic module 13 include minor faults and runaway faults.
When the type of the fault determined by the diagnostic module 13 is a slight fault, the determined cause of the fault includes at least one of slight friction of a guide rail, abrasion of a door slider, abrasion of a hanging wheel and increase of vibration of a door leaf;
when the type of the fault determined by the diagnostic module 13 is an out-of-control fault, the determined cause of the fault includes at least one of an unhooking landing door, a foreign object stop door, a man-made stop door and a wind pressure fault.
Based on the same inventive concept, embodiments of the present invention provide the following apparatus and medium.
Example four
The fourth embodiment of the present invention provides an elevator door system fault diagnosis device, which can be configured as shown in fig. 4, and includes at least one processor 21; and a memory 22 communicatively coupled to the at least one processor; wherein the memory 22 stores instructions executable by the at least one processor 21, the instructions being executable by the at least one processor 21 to enable the at least one processor 21 to perform the steps of the method according to an embodiment of the present invention.
Optionally, the processor 21 may specifically include a Central Processing Unit (CPU), an Application Specific Integrated Circuit (ASIC), one or more integrated circuits for controlling program execution, a hardware circuit developed by using a Field Programmable Gate Array (FPGA), or a baseband processor.
Optionally, the processor 21 may include at least one processing core.
Alternatively, the memory 22 may include a Read Only Memory (ROM), a Random Access Memory (RAM), and a disk memory. The memory 22 is used for storing data required by the at least one processor 21 during operation. The number of the memory 22 may be one or more.
A fifth embodiment of the present invention provides a nonvolatile computer storage medium, where the computer storage medium stores an executable program, and when the executable program is executed by a processor, the method provided in the first embodiment of the present invention is implemented.
In particular implementations, computer storage media may include: various storage media capable of storing program codes, such as a Universal Serial Bus flash drive (USB), a mobile hard disk, a Read-Only Memory (ROM), a Random Access Memory (RAM), a magnetic disk, or an optical disk.
In the embodiments of the present invention, it should be understood that the disclosed apparatus and method may be implemented in other ways. For example, the above-described embodiments of the apparatus are merely illustrative, and for example, the described unit or division of units is only one division of logical functions, and there may be other divisions when actually implemented, for example, a plurality of units or components may be combined or may be integrated into another system, or some features may be omitted, or not executed. In addition, the shown or discussed mutual coupling or direct coupling or communication connection may be an indirect coupling or communication connection through some interfaces, devices or units, and may be in an electrical or other form.
The functional units in the embodiments of the present invention may be integrated into one processing unit, or each unit may be an independent physical module.
The integrated unit, if implemented in the form of a software functional unit and sold or used as a stand-alone product, may be stored in a computer readable storage medium. Based on such understanding, all or part of the technical solutions of the embodiments of the present invention may be embodied in the form of a software product, which is stored in a storage medium and includes instructions for causing a computer device, such as a personal computer, a server, or a network device, or a processor (processor) to execute all or part of the steps of the methods according to the embodiments of the present invention. And the aforementioned storage medium includes: various media that can store program codes, such as a universal serial bus flash drive (usb flash drive), a removable hard disk, a ROM, a RAM, a magnetic disk, or an optical disk.
As will be appreciated by one skilled in the art, embodiments of the present invention may be provided as a method, system, or computer program product. Accordingly, the present invention may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, the present invention may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, and the like) having computer-usable program code embodied therein.
The present invention is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the invention. It will be understood that each flow and/or block of the flow diagrams and/or block diagrams, and combinations of flows and/or blocks in the flow diagrams and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means which implement the function specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
While preferred embodiments of the present invention have been described, additional variations and modifications in those embodiments may occur to those skilled in the art once they learn of the basic inventive concepts. Therefore, it is intended that the appended claims be interpreted as including preferred embodiments and all such alterations and modifications as fall within the scope of the invention.
It will be apparent to those skilled in the art that various changes and modifications may be made in the present invention without departing from the spirit and scope of the invention. Thus, if such modifications and variations of the present invention fall within the scope of the claims of the present invention and their equivalents, the present invention is also intended to include such modifications and variations.
Claims (10)
1. A method of diagnosing a fault in an elevator door system, the method comprising:
acquiring operation data generated in the operation process of an elevator door system;
performing feature extraction and conversion on the operating data to obtain specified feature parameters;
and taking the specified characteristic parameters as the input of a fault diagnosis model trained in advance, and determining the type and the reason of the fault by using the fault diagnosis model.
2. The method of claim 1, wherein the operational data includes electrical signal data, control signal data, elevator door opening and closing speed data, elevator door opening and closing position data, and elevator door operational status data.
3. The method of claim 1, wherein the fault diagnosis model is constructed based on a gradient lifting tree algorithm (GBDT).
4. A method according to any one of claims 1 to 3, wherein the types of faults include minor faults and runaway faults.
5. An elevator door system fault diagnosis device, characterized in that the device comprises:
the data acquisition module is used for acquiring operation data generated in the operation process of the elevator door system;
the parameter acquisition module is used for extracting and converting the characteristics of the operating data acquired by the data acquisition module to acquire specified characteristic parameters;
and the diagnosis module is used for taking the specified characteristic parameters obtained by the parameter acquisition module as the input of a fault diagnosis model trained in advance and determining the type and the reason of the fault by using the fault diagnosis model.
6. The apparatus of claim 5, wherein the operation data acquired by the data acquisition module includes electric signal data, control signal data, elevator door opening and closing speed data, elevator door opening and closing position data, and elevator door operation state data.
7. The apparatus of claim 5, wherein the fault diagnosis model pre-trained in the diagnosis module is constructed based on a gradient lifting tree algorithm (GBDT).
8. The apparatus of any of claims 5 to 7, wherein the type of fault determined by the diagnostic module comprises a minor fault and an uncontrolled fault.
9. A non-transitory computer storage medium storing an executable program for execution by a processor to perform the method of any one of claims 1 to 4.
10. An elevator door system fault diagnosis apparatus, characterized in that the apparatus comprises:
at least one processor; and
a memory communicatively coupled to the at least one processor; wherein the content of the first and second substances,
the memory stores instructions executable by the at least one processor to enable the at least one processor to perform the steps of the method of any one of claims 1 to 4.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201911144060.XA CN110817636B (en) | 2019-11-20 | 2019-11-20 | Elevator door system fault diagnosis method, device, medium and equipment |
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Cited By (5)
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| CN111797944A (en) * | 2020-08-04 | 2020-10-20 | 上海仁童电子科技有限公司 | Vehicle door abnormity diagnosis method and device |
| CN111999580A (en) * | 2020-08-14 | 2020-11-27 | 佳都新太科技股份有限公司 | Detection method and device for subway platform gate, computer equipment and storage medium |
| CN112938684A (en) * | 2021-03-22 | 2021-06-11 | 大连奥远电子股份有限公司 | Elevator running track analysis system |
| CN113581961A (en) * | 2021-08-10 | 2021-11-02 | 江苏省特种设备安全监督检验研究院 | Automatic fault identification method for elevator hall door |
| CN114955771A (en) * | 2022-05-13 | 2022-08-30 | 江苏省特种设备安全监督检验研究院 | Elevator control system fault monitoring method based on finite-state machine |
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