CN110817628A - Intelligent fault diagnosis method, device and system for elevator - Google Patents
Intelligent fault diagnosis method, device and system for elevator Download PDFInfo
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- CN110817628A CN110817628A CN201810898882.6A CN201810898882A CN110817628A CN 110817628 A CN110817628 A CN 110817628A CN 201810898882 A CN201810898882 A CN 201810898882A CN 110817628 A CN110817628 A CN 110817628A
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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
- B66B5/0031—Devices monitoring the operating condition of the elevator system for safety reasons
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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
- B66B5/02—Applications of checking, fault-correcting, or safety devices in elevators responsive to abnormal operating conditions
Abstract
The invention provides an intelligent fault diagnosis method, device and system for an elevator, wherein the method comprises the following steps: acquiring running state data of the elevator to be diagnosed in the running process; determining the operation fault type of the elevator according to a preset fault diagnosis model and operation state data; the method comprises the following steps of: acquiring multiple groups of operation sample data of the elevator and corresponding operation state information, wherein the operation sample data is converted into data in a unified format through an FFT analyzer; and training a preset neural network learning model according to the multiple groups of operation sample data and the corresponding operation state information to generate a preset fault diagnosis model. By implementing the method, the running state of the elevator equipment is more comprehensively and accurately grasped, the prediction accuracy is improved, the running stability and safety of an elevator system are greatly improved, the technical threshold of maintenance of the elevator control system is reduced, and the elevator is more accurately, simply and quickly maintained and repaired.
Description
Technical Field
The invention relates to the technical field of elevator measurement and control, in particular to an intelligent fault diagnosis method, device and system for an elevator.
Background
As a public transport means, the elevator has the most essential function of stably and safely delivering people to a target floor, but for a long time, the casualties caused by elevator accidents frequently occur in China, the accident rate and the severity degree are far higher than those of developed countries and regions, and the elevator is taken as a special device closely related to the life safety of the public, and the safe operation of the elevator is more and more concerned by the society. By 2014, the quantity of elevators in China is 360 thousands, the elevators are increased at the speed of about 20% every year, the manual quality inspection of the existing inspectors cannot meet the requirements of elevator fault treatment and regular maintenance, the phenomena of failure of safety parts, elevator omission, carelessness in maintenance and the like occur occasionally, in addition, the existing supervision system is still unsound, the management and maintenance level is also deficient, how to ensure the use safety of the elevators and reducing the loss to the maximum extent, a safe use experience is provided for elevator users, and the elevator safety inspection system becomes the first major affairs which need to be considered by government supervision departments, elevator manufacturers, elevator suppliers, elevator maintenance service providers, real estate developers, community property industries and the like.
Disclosure of Invention
In view of the above, the invention provides an intelligent fault diagnosis method, device and system for an elevator, so as to solve the problems that in the prior art, operation safety monitoring is not timely, and safety is difficult to guarantee.
Therefore, the invention provides the following technical scheme:
the embodiment of the invention provides an intelligent fault diagnosis method for an elevator, which comprises the following steps: acquiring running state data of the elevator to be diagnosed in the running process; determining the operation fault type of the elevator according to a preset fault diagnosis model and the operation state data; wherein the preset fault diagnosis model is constructed by the following steps: acquiring multiple groups of operation sample data of the elevator and corresponding operation state information, wherein the operation sample data is converted into data in a unified format through an FFT analyzer; and training a preset neural network learning model according to the multiple groups of operation sample data and the corresponding operation state information to generate the preset fault diagnosis model.
Optionally, in an embodiment, the determining the operation fault type of the elevator according to a preset fault diagnosis model and the operation state data includes: calculating the position information of the lifter according to the running state data; determining the leveling condition of the elevator according to the position information; and if the height difference between the elevator and the target floor is greater than a preset height value, judging that the elevator breaks down in operation.
Optionally, in an embodiment, the determining the operation fault type of the elevator according to a preset fault diagnosis model and the operation state data includes: determining acceleration information of the elevator according to the running state data; calculating speed information and position information of the elevator according to the acceleration information; judging whether the elevator reaches an upper limit position or not according to the position information; if the elevator reaches the upper limit position, judging whether the elevator rushes to the top or not according to the speed information; and if the elevator is in a top rushing state, driving a braking mechanism of the elevator to perform emergency braking.
Optionally, in an embodiment, the determining the operation fault type of the elevator according to a preset fault diagnosis model and the operation state data includes: determining acceleration information of the elevator according to the running state data; calculating speed information and position information of the elevator according to the acceleration information; judging whether the elevator reaches a lower limit position or not according to the position information; if the elevator reaches the lower limit position, judging whether the elevator is at the bottom of the pier or not according to the speed information; and if the elevator is in a pier bottom state, driving a braking mechanism of the elevator to perform emergency braking.
Optionally, in an embodiment, the determining the operation fault type of the elevator according to a preset fault diagnosis model and the operation state data includes: calculating speed information of the elevator according to the running state data; judging whether the speed information exceeds a preset speed limit value or not; and if the speed information exceeds the preset speed limit value, judging that the elevator is in an overspeed state, and alarming.
Optionally, in an embodiment, the determining the operation fault type of the elevator according to a preset fault diagnosis model and the operation state data includes: calculating at least one of speed information, acceleration information and displacement information of the elevator according to the running state data; calculating the vibration intensity of the elevator according to at least one of the speed information, the acceleration information and the displacement information; judging whether the vibration intensity is greater than or equal to a preset vibration threshold value; and if the vibration intensity is greater than or equal to the preset vibration threshold value, judging that the elevator is in an abnormal shaking state.
Further, in an embodiment, the determining the operation fault type of the elevator according to a preset fault diagnosis model and the operation state data further includes: calculating a vibration frequency and a vibration amplitude of the elevator according to at least one of the speed information, the acceleration information and the displacement information; and judging whether the tractor of the elevator is in an abnormal shaking state or not according to the vibration frequency and the vibration amplitude.
The embodiment of the invention also provides an intelligent fault diagnosis device for the elevator, which comprises: the running state data acquisition module is used for acquiring running state data of the elevator to be diagnosed in the running process; the operation fault type determination module is used for determining the operation fault type of the elevator according to a preset fault diagnosis model and the operation state data; the operation fault type determination module is further used for constructing the preset fault diagnosis model through the following steps: acquiring multiple groups of operation sample data of the elevator and corresponding operation state information, wherein the operation sample data is converted into data in a unified format through an FFT analyzer; and training a preset neural network learning model according to the multiple groups of operation sample data and the corresponding operation state information to generate the preset fault diagnosis model.
The embodiment of the invention also provides an intelligent fault diagnosis system for the elevator, which comprises: an elevator body; the signal collector is arranged on the elevator body and used for collecting the running state data of the elevator body and sending the running state data; and the remote monitoring equipment is in communication connection with the signal collector, receives the running state data sent by the signal collector, and determines the running fault type of the elevator body according to a preset fault diagnosis model and the running state data.
Optionally, in an embodiment, the signal collector includes: the data acquisition module is used for acquiring the running state data of the elevator body; the communication module is used for sending the running state data acquired by the data acquisition module; and the power supply module is used for providing power for the data acquisition module and the communication module.
Optionally, in an embodiment, the data acquisition module includes: at least one of a speed sensor, an acceleration sensor and a displacement sensor.
The technical scheme of the invention has the following advantages:
the intelligent fault diagnosis method, the intelligent fault diagnosis device and the intelligent fault diagnosis system for the elevator, provided by the embodiment of the invention, have the advantages that the running state of the elevator equipment can be more comprehensively and accurately grasped, the prediction accuracy is improved, the running stability and the running safety of an elevator system are greatly improved, the technical threshold for maintaining the elevator control system is reduced, and the elevator is more accurate, simpler and faster to maintain and repair.
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, and it is obvious that the drawings in the following description are some embodiments of the present invention, and other drawings can be obtained by those skilled in the art without creative efforts.
Fig. 1 is a flowchart of an elevator intelligent fault diagnosis method according to an embodiment of the present invention;
fig. 2A-2E are flow charts of a process for determining an operational fault type of an elevator based on a preset fault diagnosis model and operational status data according to an embodiment of the present invention;
fig. 3 is a schematic structural diagram of an intelligent fault diagnosis device for an elevator according to an embodiment of the present invention;
FIG. 4 is a schematic structural diagram of an elevator intelligence fault diagnostic system according to an embodiment of the present invention;
fig. 5 is a schematic structural diagram of a signal collector according to an embodiment of the present invention;
fig. 6 is a schematic view of an application scenario of the elevator intelligent fault diagnosis system according to the embodiment of the invention.
Detailed Description
The technical solutions of the present invention will be described clearly and completely with reference to the accompanying drawings, and it should be understood that the described embodiments are some, but not all embodiments of the present invention. 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.
In the description of the present invention, it should be noted that the terms "first", "second", and "third" are used for descriptive purposes only and are not to be construed as indicating or implying relative importance.
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; the two elements may be directly connected or indirectly connected through an intermediate medium, or may be communicated with each other inside the two elements, or may be wirelessly connected or wired connected. The specific meanings of the above terms in the present invention can be understood in specific cases to those skilled in the art.
In addition, the technical features involved in the different embodiments of the present invention described below may be combined with each other as long as they do not conflict with each other.
Example 1
The embodiment of the invention provides an intelligent fault diagnosis method for an elevator, which mainly comprises the following steps of:
step S1: and acquiring the running state data of the elevator to be diagnosed in the running process.
In the embodiment of the invention, the running state data of the elevator (such as an elevator and other vehicles) can be acquired in real time through the data acquisition unit. The data collector may include: at least one of a speed sensor, an acceleration sensor and a displacement sensor, and correspondingly, the acquired running state data comprises one or more of speed information, acceleration information and displacement information in the running process of the elevator.
However, in practical applications, the operation state data of the elevator is not limited to the above-mentioned data, and the corresponding data collector may be adjusted as needed to obtain the corresponding operation state data, for example, data such as a current signal and a voltage signal during the operation of the elevator, which is not limited to this.
Step S2: and determining the operation fault type of the elevator according to a preset fault diagnosis model and the operation state data.
In the embodiment of the invention, a fault diagnosis intelligent expert system knowledge base can be established according to historical data, and fault prediction analysis is carried out by combining a corresponding prediction model through a large database rule. Specifically, a preset fault diagnosis model is constructed through the following steps:
and acquiring multiple groups of operation sample data of the elevator and corresponding operation state information, wherein the operation sample data is converted into data with a uniform format through an FFT analyzer and is used as a training set for subsequently training the neural network prediction model.
And training a preset neural network learning model according to the multiple groups of operation sample data and the corresponding operation state information to generate a preset fault diagnosis model. And constructing a fault diagnosis model of the elevator based on the fault diagnosis intelligent expert system knowledge base through training of a large amount of sample data and corresponding state detection information.
Through the steps S1-S2, the intelligent fault diagnosis method for the elevator, provided by the embodiment of the invention, has the advantages that the running state of the elevator equipment is more comprehensively and accurately grasped, the prediction accuracy is improved, the running stability and safety of an elevator system are greatly improved, the technical threshold of maintenance of the elevator control system is reduced, and the elevator is maintained and repaired more accurately, simply and quickly.
Alternatively, in some embodiments of the present invention, as shown in fig. 2A, the step S2 of determining the operation fault type of the elevator according to the preset fault diagnosis model and the operation state data includes:
step S21 a: calculating the position information of the elevator according to the running state data;
step S22 a: determining the leveling condition of the elevator according to the position information;
step S23 a: if the height difference between the elevator and the target floor is greater than the preset height value, judging that the elevator breaks down in operation; and if the height difference between the elevator and the target floor is not greater than the preset height value, returning to the step S21a, and continuously acquiring new running state data for judgment.
In the embodiment of the invention, whether the uneven layer occurs in the running process of the elevator can be judged through the running state data. Firstly, calculating the position information of the elevator according to the running state data; then, the leveling condition of the elevator is determined based on the calculated position information. For example, the position of the upper and lower boundaries of each floor where the elevator needs to stop is known through an infrared sensor, and whether the height difference between the elevator and the target floor is greater than a preset height value or not is judged according to the position. If the height is higher than the preset height difference, the positioning function stop of the elevator is possibly failed, and the possibility of causing danger to the personnel of passengers exists. At the moment, the operation of the elevator is judged to be in fault, emergency shutdown treatment can be adopted, an alarm is given in time, and operation and maintenance personnel are informed to carry out maintenance.
Alternatively, in some embodiments of the present invention, as shown in fig. 2B, the step S2 of determining the operation fault type of the elevator according to the preset fault diagnosis model and the operation state data includes:
step S21 b: determining acceleration information of the elevator according to the running state data;
step S22 b: calculating speed information and position information of the elevator according to the acceleration information;
step S23 b: judging whether the elevator reaches an upper limit position or not according to the position information;
step S24 b: if the elevator reaches the upper limit position, judging whether the elevator rushes to the top or not according to the speed information; if the elevator does not reach the upper limit position, returning to the step S21b, and acquiring new operation state data for judgment;
step S25 b: if the elevator is in a top rushing state, a braking mechanism of the elevator is driven to perform emergency braking; if the elevator is not in the top-rushing state, the process returns to step S21b, and new operation state data is acquired for judgment.
In the embodiment of the invention, whether the problem of top rushing possibly occurs in the running process of the elevator can be judged through the running state data. The acceleration of the elevator is first determined from the running loading data. Specifically, the real-time acceleration of the elevator may be obtained by an acceleration sensor, and the real-time speed and the real-time displacement of the elevator may be obtained by calculation. Judging whether the position of the elevator reaches the upper limit position or not according to the real-time displacement information; if the elevator reaches the upper limit position, further judging whether the elevator rushes to the top according to the real-time speed; if the speed of the elevator is judged to exceed the speed threshold value, the danger of the elevator rushing to the top is probably existed, and the braking mechanism of the elevator needs to be driven to brake emergently.
Alternatively, in some embodiments of the present invention, as shown in fig. 2C, the step S2 of determining the operation fault type of the elevator according to the preset fault diagnosis model and the operation state data includes:
step S21 c: determining acceleration information of the elevator according to the running state data;
step S22 c: calculating speed information and position information of the elevator according to the acceleration information;
step S23 c: judging whether the elevator reaches a lower limit position or not according to the position information;
step S24 c: if the elevator reaches the lower limit position, judging whether the elevator lifts the bottom or not according to the speed information; if the elevator does not reach the lower limit position, returning to the step S21c, and acquiring new running state data for judgment;
step S25 c: if the elevator is in a pier bottom state, a braking mechanism for driving the elevator to perform emergency braking; and if the elevator is not in the pier bottom state, returning to the step S21c, and acquiring new running state data for judgment.
In the embodiment of the invention, whether the problem of pier bottom possibly occurs in the running process of the elevator can be judged through the running state data. The acceleration of the elevator is first determined from the running loading data. Specifically, the real-time acceleration of the elevator may be obtained by an acceleration sensor, and the real-time speed and the real-time displacement of the elevator may be obtained by calculation. Judging whether the position of the elevator reaches a lower limit position or not according to the real-time displacement information; if the elevator reaches the lower limit position, further judging whether the elevator is at the bottom of the pier according to the real-time speed; if the speed of the elevator is judged to exceed the speed threshold value, the danger of the bottom of the elevator is probably existed, and the braking mechanism of the elevator needs to be driven to brake emergently.
Alternatively, in some embodiments of the present invention, as shown in fig. 2D, the step S2 of determining the operation fault type of the elevator according to the preset fault diagnosis model and the operation state data includes:
step S21 d: calculating speed information of the elevator according to the running state data;
step S22 d: judging whether the speed information exceeds a preset speed limit value or not;
step S23 d: if the speed information exceeds the preset speed limit value, judging that the elevator is in an overspeed state, and alarming; and if the speed information does not exceed the preset speed limit, returning to the step 21d, and acquiring new running state data for judgment.
In the embodiment of the invention, whether the problem of overspeed possibly occurs in the running process of the elevator can be judged through the running state data. Firstly, calculating the instant speed of the elevator according to the running state data; judging whether the elevator exceeds a preset speed threshold value according to the instant speed; if the instant speed of the elevator exceeds the preset speed threshold value, the situation that the elevator is overspeed and certain potential safety hazard exists is indicated, and at the moment, the alarm device can give an alarm and immediately report to related operation and maintenance personnel.
Alternatively, in some embodiments of the present invention, as shown in fig. 2E, the step S2 of determining the operation fault type of the elevator according to the preset fault diagnosis model and the operation state data includes:
step S21 e: calculating at least one of speed information, acceleration information and displacement information of the elevator according to the running state data;
step S22 e: calculating the vibration intensity of the elevator according to at least one of the speed information, the acceleration information and the displacement information;
step S23 e: judging whether the vibration intensity is greater than or equal to a preset vibration threshold value;
step S24 e: if the vibration intensity is larger than or equal to a preset vibration threshold value, judging that the elevator is in an abnormal shaking state; and if the vibration intensity is smaller than the preset vibration threshold, returning to the step S21e to obtain new operation state data for judgment.
In the embodiment of the invention, whether the problem of abnormal jitter occurs in the running process of the elevator can be judged through the running state data. Firstly, calculating at least one of speed information, acceleration information and displacement information of the elevator according to the running state data; according to at least one of the speed information, the acceleration information and the displacement information of the lifter, the vibration intensity of the lifter can be further calculated; judging whether the vibration intensity is larger than or equal to a vibration threshold value or not according to the vibration intensity; if the vibration intensity is larger than or equal to the vibration threshold, the abnormal shaking exists in the running process of the elevator, and related operation and maintenance personnel can be prompted to carry out maintenance.
Further, according to at least one of the speed information, the acceleration information and the displacement information of the lifter, the vibration frequency and the vibration amplitude of the lifter can be calculated; and further, it is judged whether or not the hoist of the elevator is in an abnormal vibration state based on the vibration frequency and the vibration amplitude.
The intelligent fault diagnosis method for the elevator, provided by the embodiment of the invention, has the advantages that the running state of the elevator equipment is more comprehensively and accurately grasped, the prediction accuracy is improved, the running stability and safety of an elevator system are greatly improved, the technical threshold of maintenance of the elevator control system is reduced, and the elevator is more accurate, simpler and faster to maintain and repair.
Example 2
The embodiment of the invention also provides an intelligent fault diagnosis device for the elevator, which is used for realizing the embodiment and the preferred embodiment, and the description is omitted for the sake of description. As used below, the term "module" may be a combination of software and/or hardware that implements a predetermined function. Although the means described in the embodiments below are preferably implemented in software, an implementation in hardware, or a combination of software and hardware is also possible and contemplated.
An embodiment of the present invention provides an intelligent fault diagnosis device for an elevator, as shown in fig. 3, the intelligent fault diagnosis device for an elevator includes: the device comprises an operation state data acquisition module 1 and an operation fault type determination module 2.
The running state data acquisition module 1 is used for acquiring running state data of the elevator to be diagnosed in the running process; for details, refer to the related description of step S1 in the above embodiment.
The operation fault type determination module 2 is used for determining the operation fault type of the elevator according to a preset fault diagnosis model and operation state data; for details, refer to the related description of step S2 in the above embodiment.
Further functional descriptions of the modules are the same as those of the corresponding embodiments, and are not repeated herein.
Example 3
An embodiment of the present invention further provides an elevator intelligent fault diagnosis system, as shown in fig. 4, the elevator intelligent fault diagnosis system mainly includes: an elevator body 41; a signal collector 42 disposed on the elevator body 41 for collecting operation state data of the elevator body 41 and transmitting the operation state data; and the remote monitoring equipment 43 is in communication connection with the signal collector 42, receives the running state data sent by the signal collector 42, and determines the running fault type of the elevator body according to a preset fault diagnosis model and the running state data.
It should be noted that in the connection relationship shown in fig. 4, the signal collector 42 establishes a communication connection with the remote monitoring device 43 in a wired manner, but in practical applications, the signal collector 42 may also establish a communication connection with the remote monitoring device 43 in a wireless manner, and the invention is not limited thereto.
The remote monitoring device 43 can be combined with a cloud platform to realize resource sharing, investment saving and remote diagnosis, and the monitored parameters are not limited to vibration, shaft displacement and rotating speed any more, and are further expanded to the technical processes of various sensors and the like.
Specifically, as shown in fig. 5, the signal collector 42 includes: the data acquisition module 421 is used for acquiring the running state data of the elevator body; a communication module 422, configured to send the operation state data acquired by the data acquisition module 421; and a power module 423 for providing power to the data acquisition module 421 and the communication module 422. The data collection module 421 may be: at least one of a speed sensor, an acceleration sensor and a displacement sensor.
In practical applications, the data collection performed by the signal collector 42 can adopt two ways, one-to-one and one-to-many, that is: when the distances between a plurality of elevators are close, one signal collector 42 is used for collecting the data of the plurality of elevators: when the elevators are relatively decentralized, one signal collector 42 is used for each individual elevator.
In practical applications, the speed sensor, the acceleration sensor, the displacement sensor, and the like may be disposed outside the hoisting machine brake of the elevator, but the present invention is not limited thereto.
Optionally, in some embodiments of the present invention, the elevator intelligent fault diagnosis system may be applied to a plurality of related users including a monitoring and rescue center, an engineer station, and a rescue and repair network, so as to build a whole set of system-based elevator management, maintenance, and rescue system, and ensure that the system plays a crucial role in the operation safety of the elevator.
Fig. 6 is a topological structure diagram of an application example of the elevator intelligent fault diagnosis system according to the embodiment of the present invention. As shown in fig. 6, the car data acquisition module 61 applies the data acquisition device 42, and the machine room industrial control module 62 corresponds to the remote monitoring device 43, as shown in fig. 6, in this embodiment, the car data acquisition module 61 and the machine room industrial control module 62 are in wireless communication through a wireless Access Point (AP). The car data acquisition module 61 sends the acquired running state parameters of the elevator to the machine room industrial control module 62 through the wireless access point, the machine room industrial control module 62 analyzes the running state parameters, judges the running state of the elevator, and determines the fault type of the elevator if the elevator is judged to have a fault.
The information analyzed and generated by the machine room industrial control module 62 can also be uploaded to a fault diagnosis cloud server 63 through a network, so as to be stored or be viewed and downloaded by related users through a user terminal 64 (such as a mobile phone, a computer, and the like). The data information downloaded from the fault diagnosis cloud server 63 can be subsequently used for multiple purposes, such as expert diagnosis and remote management, for example, the information is sent to relevant maintenance personnel, for example, a corresponding data server is formed according to historical accumulated data, for example, corresponding reminding information is generated according to the information and sent to corresponding staff, and the like.
It should be understood that the above examples are only for clarity of illustration and are not intended to limit the embodiments. Other variations and modifications will be apparent to persons skilled in the art in light of the above description. And are neither required nor exhaustive of all embodiments. And obvious variations or modifications therefrom are within the scope of the invention.
Claims (11)
1. An intelligent fault diagnosis method for an elevator, which is characterized by comprising the following steps:
acquiring running state data of the elevator to be diagnosed in the running process;
determining the operation fault type of the elevator according to a preset fault diagnosis model and the operation state data;
wherein the preset fault diagnosis model is constructed by the following steps:
acquiring multiple groups of operation sample data of the elevator and corresponding operation state information, wherein the operation sample data is converted into data in a unified format through an FFT analyzer;
and training a preset neural network learning model according to the multiple groups of operation sample data and the corresponding operation state information to generate the preset fault diagnosis model.
2. The intelligent fault diagnosis method for the elevator according to claim 1, wherein the determining the type of the operation fault of the elevator according to the preset fault diagnosis model and the operation state data comprises:
calculating the position information of the lifter according to the running state data;
determining the leveling condition of the elevator according to the position information;
and if the height difference between the elevator and the target floor is greater than a preset height value, judging that the elevator breaks down in operation.
3. The intelligent fault diagnosis method for the elevator according to claim 1, wherein the determining the type of the operation fault of the elevator according to the preset fault diagnosis model and the operation state data comprises:
determining acceleration information of the elevator according to the running state data;
calculating speed information and position information of the elevator according to the acceleration information;
judging whether the elevator reaches an upper limit position or not according to the position information;
if the elevator reaches the upper limit position, judging whether the elevator rushes to the top or not according to the speed information;
and if the elevator is in a top rushing state, driving a braking mechanism of the elevator to perform emergency braking.
4. The intelligent fault diagnosis method for the elevator according to claim 1, wherein the determining the type of the operation fault of the elevator according to the preset fault diagnosis model and the operation state data comprises:
determining acceleration information of the elevator according to the running state data;
calculating speed information and position information of the elevator according to the acceleration information;
judging whether the elevator reaches a lower limit position or not according to the position information;
if the elevator reaches the lower limit position, judging whether the elevator is at the bottom of the pier or not according to the speed information;
and if the elevator is in a pier bottom state, driving a braking mechanism of the elevator to perform emergency braking.
5. The intelligent fault diagnosis method for the elevator according to claim 1, wherein the determining the type of the operation fault of the elevator according to the preset fault diagnosis model and the operation state data comprises:
calculating speed information of the elevator according to the running state data;
judging whether the speed information exceeds a preset speed limit value or not;
and if the speed information exceeds the preset speed limit value, judging that the elevator is in an overspeed state, and alarming.
6. The intelligent fault diagnosis method for the elevator according to claim 1, wherein the determining the type of the operation fault of the elevator according to the preset fault diagnosis model and the operation state data comprises:
calculating at least one of speed information, acceleration information and displacement information of the elevator according to the running state data;
calculating the vibration intensity of the elevator according to at least one of the speed information, the acceleration information and the displacement information;
judging whether the vibration intensity is greater than or equal to a preset vibration threshold value;
and if the vibration intensity is greater than or equal to the preset vibration threshold value, judging that the elevator is in an abnormal shaking state.
7. The intelligent fault diagnosis method for the elevator according to claim 6, wherein the determining of the type of the operation fault of the elevator according to the preset fault diagnosis model and the operation state data further comprises:
calculating a vibration frequency and a vibration amplitude of the elevator according to at least one of the speed information, the acceleration information and the displacement information;
and judging whether the tractor of the elevator is in an abnormal shaking state or not according to the vibration frequency and the vibration amplitude.
8. An elevator intelligent fault diagnosis device, comprising:
the running state data acquisition module is used for acquiring running state data of the elevator to be diagnosed in the running process;
the operation fault type determination module is used for determining the operation fault type of the elevator according to a preset fault diagnosis model and the operation state data;
the operation fault type determination module is further used for constructing the preset fault diagnosis model through the following steps:
acquiring multiple groups of operation sample data of the elevator and corresponding operation state information, wherein the operation sample data is converted into data in a unified format through an FFT analyzer;
and training a preset neural network learning model according to the multiple groups of operation sample data and the corresponding operation state information to generate the preset fault diagnosis model.
9. An elevator intelligence fault diagnostic system, comprising:
an elevator body;
the signal collector is arranged on the elevator body and used for collecting the running state data of the elevator body and sending the running state data;
and the remote monitoring equipment is in communication connection with the signal collector, receives the running state data sent by the signal collector, and determines the running fault type of the elevator body according to a preset fault diagnosis model and the running state data.
10. The elevator intelligent fault diagnosis system according to claim 9, wherein the signal collector comprises:
the data acquisition module is used for acquiring the running state data of the elevator body;
the communication module is used for sending the running state data acquired by the data acquisition module;
and the power supply module is used for providing power for the data acquisition module and the communication module.
11. The elevator intelligence fault diagnostic system of claim 9, wherein the data acquisition module comprises: at least one of a speed sensor, an acceleration sensor and a displacement sensor.
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| CN201810898882.6A CN110817628A (en) | 2018-08-08 | 2018-08-08 | Intelligent fault diagnosis method, device and system for elevator |
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| CN201810898882.6A CN110817628A (en) | 2018-08-08 | 2018-08-08 | Intelligent fault diagnosis method, device and system for elevator |
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