CN117023309A - Elevator remote monitoring method - Google Patents
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- CN117023309A CN117023309A CN202310892995.6A CN202310892995A CN117023309A CN 117023309 A CN117023309 A CN 117023309A CN 202310892995 A CN202310892995 A CN 202310892995A CN 117023309 A CN117023309 A CN 117023309A
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- 238000012544 monitoring process Methods 0.000 title claims abstract description 32
- 238000000034 method Methods 0.000 title claims abstract description 31
- 238000003745 diagnosis Methods 0.000 claims description 34
- 230000008569 process Effects 0.000 claims description 11
- 230000004927 fusion Effects 0.000 claims description 9
- 239000011159 matrix material Substances 0.000 claims description 9
- 238000005259 measurement Methods 0.000 claims description 8
- 238000004891 communication Methods 0.000 claims description 7
- 238000011156 evaluation Methods 0.000 claims description 5
- 230000003068 static effect Effects 0.000 claims description 5
- 238000004458 analytical method Methods 0.000 claims description 4
- 230000005540 biological transmission Effects 0.000 claims description 4
- 238000000605 extraction Methods 0.000 claims description 4
- 230000015556 catabolic process Effects 0.000 claims description 3
- 230000001364 causal effect Effects 0.000 claims description 3
- 238000006731 degradation reaction Methods 0.000 claims description 3
- 238000013210 evaluation model Methods 0.000 claims description 3
- 238000010606 normalization Methods 0.000 claims description 3
- 239000000126 substance Substances 0.000 claims description 3
- 238000012423 maintenance Methods 0.000 description 14
- 238000005516 engineering process Methods 0.000 description 6
- 238000013461 design Methods 0.000 description 4
- 238000010586 diagram Methods 0.000 description 4
- 238000005299 abrasion Methods 0.000 description 3
- 238000013528 artificial neural network Methods 0.000 description 3
- 238000001514 detection method Methods 0.000 description 3
- 238000007689 inspection Methods 0.000 description 3
- 239000008186 active pharmaceutical agent Substances 0.000 description 2
- 238000002474 experimental method Methods 0.000 description 2
- 238000007499 fusion processing Methods 0.000 description 2
- 239000000463 material Substances 0.000 description 2
- 238000011160 research Methods 0.000 description 2
- 230000006399 behavior Effects 0.000 description 1
- 230000009286 beneficial effect Effects 0.000 description 1
- 238000011161 development Methods 0.000 description 1
- 230000008921 facial expression Effects 0.000 description 1
- 230000004886 head movement Effects 0.000 description 1
- 238000009434 installation Methods 0.000 description 1
- 238000004519 manufacturing process Methods 0.000 description 1
- 238000013507 mapping Methods 0.000 description 1
- 230000007246 mechanism Effects 0.000 description 1
- 238000012986 modification Methods 0.000 description 1
- 230000004048 modification Effects 0.000 description 1
- 230000006855 networking Effects 0.000 description 1
- 238000007500 overflow downdraw method Methods 0.000 description 1
- 238000004806 packaging method and process Methods 0.000 description 1
- 230000003449 preventive effect Effects 0.000 description 1
- 238000012545 processing Methods 0.000 description 1
- 238000004088 simulation Methods 0.000 description 1
Classifications
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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
- B66B1/00—Control systems of elevators in general
- B66B1/02—Control systems without regulation, i.e. without retroactive action
- B66B1/06—Control systems without regulation, i.e. without retroactive action electric
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- B—PERFORMING OPERATIONS; TRANSPORTING
- B66—HOISTING; LIFTING; HAULING
- B66B—ELEVATORS; ESCALATORS OR MOVING WALKWAYS
- B66B1/00—Control systems of elevators in general
- B66B1/34—Details, e.g. call counting devices, data transmission from car to control system, devices giving information to the control system
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- Engineering & Computer Science (AREA)
- Automation & Control Theory (AREA)
- Computer Networks & Wireless Communication (AREA)
- Indicating And Signalling Devices For Elevators (AREA)
- Maintenance And Inspection Apparatuses For Elevators (AREA)
Abstract
The invention discloses an elevator remote monitoring method in the technical field of elevator remote monitoring, which comprises the following steps: the invention can ensure that the selected parameters can accurately reflect the actual running state of the elevator, the engineering limit parameter range used as a state monitoring judgment standard is reasonable and effective, the quality guarantee amount of the maintained elevator can be remotely monitored and supervised at any time, the prediction and early warning can be carried out on the potential faults, and technicians and managers can be helped to make decisions early.
Description
Technical Field
The invention relates to the technical field of elevator remote monitoring, in particular to an elevator remote monitoring method.
Background
The elevator is widely distributed in places with dense population and frequent movement such as office buildings, residential houses, markets and the like, and once safety accidents occur, the elevator has large involved area and bad influence, and the safety and reliability of the elevator are research subjects which are urgently needed to be solved at present. For this reason, the national quality supervision, inspection and quarantine administration has been used to examine potential safety hazards possibly existing in the use of elevators in the country in the form of "elevator safety big fight". Plays a certain role in the safe operation of the elevator in use, but cannot radically and completely eradicate the occurrence of accidents from the elevator. Aiming at the safety problem of the elevator, the method is necessary to carry out deep system research on the aspects of intelligent detection and reliability evaluation of key parts of the elevator, elevator running state signal characteristic extraction and fault prediction theory, elevator safety technology based on the Internet of things and big data and the like in theory and technology.
The intelligent degree of the sensor installed on the elevator is low nowadays, and the measurement error caused by the sensor self causes the detection information to have uncertainty and imperfection. In addition, the current elevator prediction and diagnosis system usually predicts based on the collected sensor data, has certain time delay, can not realize timely prediction of fault diagnosis, and has strong real-time requirements. The accuracy of elevator fault prediction and diagnosis is therefore not limited only by the timeliness of the sensor's data samples, which becomes a critical factor. Based on the above, the invention designs a remote elevator monitoring method to solve the above problems.
Disclosure of Invention
The invention aims to provide an elevator remote monitoring method, which predicts possible data of a sensor at the next moment through a neural network, fuses the possible data by utilizing a DS data fusion theory and obtains a fault diagnosis result according to the predicted data of a plurality of sensors. The problems that the traditional method is difficult to fully and accurately reflect the working state of the elevator due to time delay and measurement information of a single sensor, and thus the fault diagnosis is uncertain and inaccurate are solved.
In order to achieve the above purpose, the present invention provides the following technical solutions: the elevator remote monitoring method comprises the following steps:
s1: determining operation state monitoring parameters of an elevator or a system, determining key factors and state parameters affecting the safe operation of the elevator, selecting key parts of the elevator, establishing state monitoring points for key equipment, and collecting equipment state data;
s2: on the basis of carrying out system analysis on elevator collected data, carrying out multi-source information fusion on information capable of accurately reflecting the actual running state of an elevator to obtain influencing factors and running state signal parameters;
s3: establishing a weak feature extraction method of predictive fault diagnosis based on an evidence theory, further establishing an elevator performance degradation prediction and evaluation model based on multi-source information, establishing an elevator fault diagnosis theory evidence theory model based on multi-source information fusion, and establishing a structural system dynamic failure function by combining reliability evaluation;
s4: the elevator working process is simulated and run, the computer in the running process calls a database and an application program after collecting the information of the diagnosed object, and after asking for necessary information from a user, the computer can quickly find out the final fault or the most likely fault;
s5: the constructed fault diagnosis system comprises a database, a knowledge base, a man-machine interface and an inference engine, and can rapidly and accurately analyze sensor signals sent by the wireless transmission system based on the Internet of things and immediately tell an operator what measures to take.
Preferably, in step S1 and step S2, an internet of things system is constructed by adopting the stm32MCU and the EC20 communication module, and the fault state and the diagnosis result are networked through a network and sent to the cloud; the fault elevator positioning can be realized through the Beidou and the GPS, and the nodes of the Internet of things are provided with various types of sensors for acquiring information of various physical and chemical states of the elevator, analyzing the current running state of the elevator and judging whether the running state of the elevator is normal or not.
Preferably, in step S3, the evidence theory model is as follows:
p sensors, Q-class state, P >1, Q >1 are provided.
Order theRepresenting the measured data feature vector of the kth sensor. S is S ki Is S k Is the i-th measurement data feature of (a); n is n k Is the total number of measured data features provided by the kth sensor,/for>
Establishing a state matrix:
wherein X is j Is a vector describing the j-th class of states, x ji > 0 represents the ith theoretical measured data characteristic representing the jth class of state;theoretical values representing corresponding data characteristics of the 1 st sensor in the j-th state;a theoretical value representing the data characteristic of the kth sensor in the jth state. Wherein i=1, 2, …, n; j=1, 2, …, Q; k=2, 3, …, P.
Definition S k And X is j Minkowski distance in between:
where p is a constant.
Calculating the distance between the feature vector S and the corresponding state vector X of all sensor measurement data, and establishing a distance matrix:
distance d kj The smaller the probability of judging that the object is in the j-th state based on the k-th sensor information is, the more likely it is, thus defining:
let m kj And d kj And (3) performing inverse correlation and normalization:
expressed in matrix form:
m is then k ＝{m k1 ，m k2 ，…，m kQ ，}k＝1,2,…，P
Can be used as the confidence value of the kth sensor for state identification.
Preferably, in step S4, the database is generally composed of two parts, a dynamic database and a static database, the static database being a relatively stable parameter, the dynamic database being a status parameter detected during operation of the device.
Preferably, in step S5, the knowledge stored in the knowledge base is any one or combination of a working environment of the system, system knowledge, a fault feature value of the equipment, a fault diagnosis algorithm, and an inference rule, and reflects a causal relationship of the system, so as to perform fault inference, and the knowledge base is a set of knowledge in the expert domain.
Compared with the prior art, the invention has the beneficial effects that: the invention constructs the elevator remote monitoring method which comprises a sensor, an MCU, an EC20 full-network communication module, a power interface, matched electrical parts, completes corresponding installation, acquires corresponding operation data, and acquires part of elevator system operation state monitoring parameters, such as: the elevator running speed, vibration, abrasion and current and voltage of electrical equipment are obtained from experiments, so that the selected parameters can accurately reflect the actual running state of the elevator, and the engineering limit parameter range serving as a state monitoring judgment standard is reasonable and effective.
Through constructing a comprehensive supervision system, the elevator maintenance unit can inquire the maintenance conditions of all elevators of the unit in real time through the electronic supervision system, and establish an elevator maintenance electronic management file, can timely and accurately acquire real-time operation data of remote elevators, remotely inquire basic information and maintenance records of the elevators and historical data, and can remotely monitor and supervise the maintenance quality of the maintained elevators at any time.
The communication module can enable related department personnel to timely master the running condition of the elevator in the monitored elevator group through the PC, the mobile phone APP and the flat plate at any time and any place, forecast and early-warn potential faults, and help technicians and managers to make decisions early.
Drawings
In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings that are needed for the description of the embodiments will be briefly described below, and it is obvious that the drawings in the following description are only some embodiments of the present invention, and that other drawings may be obtained according to these drawings without inventive effort for a person skilled in the art.
FIG. 1 is a flow chart of evidence theory diagnosis and prediction based on a neural network;
FIG. 2 is a flow chart of a fusion process based on evidence theory according to the present invention;
FIG. 3 is a diagram of an Internet of things system architecture according to the present invention;
FIG. 4 is a waveform diagram of the present invention before and after signal fusion noise cancellation;
FIG. 5 is an exemplary diagram of a knowledge base in accordance with the present invention;
fig. 6 is a diagram of a fault diagnosis system according to the present invention.
Detailed Description
The following description of the embodiments of the present invention will be made clearly and completely with reference to the accompanying drawings, in which it is apparent that the embodiments described are only some embodiments of the present invention, but not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
Referring to fig. 1-6, the present invention provides a technical solution: aiming at the existing fault diagnosis methods, mostly based on the acquired data, an elevator fault diagnosis algorithm based on DS data fusion is provided. The project adopts multi-sensor fusion to obtain predicted data, then the data is corresponding to the membership degree of each diagnosis category to initialize the initial credibility distribution of the sensors, the data collected by each sensor is used as evidence, and the data fusion method is adopted to fuse each evidence, so as to obtain the final diagnosis result. The evidence theory diagnosis and prediction flow based on the neural network is shown in fig. 1, and the fusion processing flow based on the evidence theory is shown in fig. 2.
According to the invention, an Internet of things system is constructed by adopting the stm32MCU and the remote EC20 communication module, so that the fault state and the diagnosis result which cannot be originally networked are networked through a network and are sent to the cloud. The physical architecture is shown in fig. 3, and meanwhile, the fault elevator positioning can be realized through Beidou and GPS. The node of the internet of things is provided with various types of sensors for collecting information of various physical and chemical states of the elevator, analyzing the current running state of the elevator, judging whether the running state of the elevator is normal or not, and when the parameter measured value exceeds the standard limit range, further technical analysis diagnosis is required, so that the diagnosis is carried out on parts and components, the whole equipment and even the system, and the diagnosis on the integrated system of the elevator system is formed by combining the diagnosis on the running process. The video acquisition comprises face recognition, facial expression recognition, lip reading, head movement tracking, gaze tracking, gesture recognition and the like, the mapping relation can be established between the video and the behavior description through processing and analyzing the monitoring video data, and the video is automatically matched with a preset rule by acquiring the content of the video, so that a computer can 'see' the video or 'understand' the video, the monitoring personnel can be replaced to finish the function of partial monitoring, and the manpower is saved.
The invention provides an elevator remote monitoring method, which comprises the following steps:
s1: determining operation state monitoring parameters of an elevator or a system, determining key factors and state parameters affecting the safe operation of the elevator, selecting key parts of the elevator, establishing state monitoring points for key equipment, and collecting equipment state data;
s2: based on system analysis of elevator collected data, from the aspect of predictive design, the method aims at meeting the requirement of elevator power machine state monitoring capability, and early monitoring connotations are brought into the product design, manufacturing, use and maintenance service process in the whole system period of the whole service life, so that the selected parameters can accurately reflect the actual running state of the elevator, and a large amount of information such as vibration, temperature, output power, abrasion, electric parameters and the like are fused into multi-source information. The possible redundancy and contradiction between the multi-sensor information are eliminated, the redundancy and the contradiction are complemented, the uncertainty is reduced, and the influence factors and the running state signal parameters are obtained;
s3: a weak feature extraction method of predictive fault diagnosis based on an evidence theory is established, and then an elevator performance degradation prediction and evaluation model based on multi-source information such as vibration, abrasion and temperature is established. Establishing an elevator fault diagnosis theory based on multi-source information fusion, developing a predictive fault diagnosis system based on big data, and establishing a dynamic failure function of a structural system by combining reliability evaluation;
the evidence theory model is as follows:
p sensors, Q-class state, P >1, Q >1 are provided.
Order theRepresenting the measured data feature vector of the kth sensor. S is S ki Is S k Is the i-th measurement data feature of (a); n is n k Is the total number of measured data features provided by the kth sensor,
establishing a state matrix:
wherein X is j Is a vector describing the j-th class of states, x ji Representation of > representationThe ith theoretical measured data feature of the jth class of state;theoretical values representing corresponding data characteristics of the 1 st sensor in the j-th state;a theoretical value representing the data characteristic of the kth sensor in the jth state. Wherein i=1, 2, …, n; j=1, 2, …, Q; k=2, 3, …, P.
Definition S k And X is j Minkowski distance in between:
where p is a constant.
Calculating the distance between the feature vector S and the corresponding state vector X of all sensor measurement data, and establishing a distance matrix:
distance d kj The smaller the probability of judging that the object is in the j-th class state based on the k pieces of sensor information is, the definition is thus:
let m kj And d kj And (3) performing inverse correlation and normalization:
expressed in matrix form:
m is then k ＝{m k1 ，m k2 ，…，m kQ ，}k＝1,2,…，P
Can be used as the confidence value of the kth sensor for state identification.
S4: through simulation and operation experiments of the elevator working process, after information of a diagnosed object is collected by a computer in the operation process, various rules (expert experience) are comprehensively utilized to conduct a series of reasoning, various application programs can be called at any time when necessary, and after necessary information is required to a user, a final fault or a most likely fault can be quickly found;
s5: databases are typically made up of two parts, a dynamic database and a static database. Static databases are relatively stable parameters such as design parameters of the device, natural frequencies, etc.; the dynamic database is a state parameter detected in the running of the equipment, such as vibration, working rotation speed, elevator passenger flow, traction machine running voltage or current, etc., and fig. 5 is a knowledge base with large vibration peak value of the traction machine.
The knowledge stored in the knowledge base can be the working environment of the system, the system knowledge (reflecting the working mechanism and system structure knowledge of the system), the equipment fault characteristic value, the fault diagnosis algorithm, the reasoning rule and the like, and reflects the causal relation of the system, so that fault reasoning can be carried out, and the knowledge base is a set of expert domain knowledge.
The constructed fault diagnosis system is shown in fig. 6, and consists of a database, a knowledge base, a man-machine interface, an inference engine and the like, and can rapidly and accurately analyze sensor signals sent by the wireless transmission system based on the Internet of things and immediately tell an operator what measures to take.
The elevator remote monitoring technology is an emerging technology for carrying out central centralized remote control detection on an operating elevator along with the development of a computer control technology and a network communication technology, provides a brand-new product concept and service concept, is the operating state in the current elevator service management field, automatically counts the elevator fault rate according to fault records, can effectively monitor the elevator state and the maintenance unit working quality, and provides a reliable basis for inspection and examination.
The Internet of things is integrated into a project, and an Internet of things fault diagnosis system is built by adopting the stm32MCU and the remote EC20 communication module, so that the fault state and the diagnosis result which cannot be networked originally are networked through a network and sent to the cloud, and meanwhile, the elevator is positioned through Beidou and GPS. The method has the advantages that relevant departments and personnel can timely master the running conditions of the elevators in the monitored elevator group through the PC, the mobile phone APP and the flat plate at any time and any place, forecast and early warning is carried out on potential faults, early decision making is carried out on technicians and management personnel, and the situation of preventive attack is avoided; when an accident occurs in the elevator, the type and the address of the accident are accurately judged in time, an alarm is given, relevant departments and personnel are notified, and correct treatment measures are adopted. The system consists of an Internet of things data acquisition system, a transmission network and a control center. The data acquisition system acquires and processes the running state of the elevator and related data, performs data packaging, and sends an information packet to the remote control center through RS or TCP/IP networking according to the set plan, and the remote control center analyzes and processes the information packet and stores the information in the database. The control center provides a standardized monitoring window for the user, and the state of the specified elevator can be displayed in real time. When the elevator fails, the elevator can give an alarm in time, so that maintenance personnel can arrive at the site to process the failure as soon as possible, and meanwhile, the operation data before and after the failure are automatically extracted independently and stored for a long time so as to analyze the failure; analyzing the type of the fault by adopting a data statistics method, preventing the occurrence of the fault of the elevator and improving the operation safety of the elevator; direct data support is provided for daily maintenance of the elevator, so that maintenance work efficiency is improved, and maintenance cost is reduced; automatically recording the type and occurrence time of faults in the operation of the elevator so as to master the actual operation condition of the elevator; and the inspection and maintenance time of maintenance personnel is recorded, so that the supervision and check of maintenance work by a management department are facilitated, and an elevator supervision and evaluation system is realized.
In the description of the present specification, the descriptions of the terms "one embodiment," "example," "specific example," and the like, mean that a particular feature, structure, material, or characteristic described in connection with the embodiment or example is included in at least one embodiment or example of the present invention. In this specification, schematic representations of the above terms do not necessarily refer to the same embodiments or examples. Furthermore, the particular features, structures, materials, or characteristics described may be combined in any suitable manner in any one or more embodiments or examples.
The preferred embodiments of the invention disclosed above are intended only to assist in the explanation of the invention. The preferred embodiments are not exhaustive or to limit the invention to the precise form disclosed. Obviously, many modifications and variations are possible in light of the above teaching. The embodiments were chosen and described in order to best explain the principles of the invention and the practical application, to thereby enable others skilled in the art to best understand and utilize the invention. The invention is limited only by the claims and the full scope and equivalents thereof.
Claims (5)
1. The elevator remote monitoring method is characterized by comprising the following steps of:
s1: determining operation state monitoring parameters of an elevator or a system, determining key factors and state parameters affecting the safe operation of the elevator, selecting key parts of the elevator, establishing state monitoring points for key equipment, and collecting equipment state data;
s2: on the basis of carrying out system analysis on elevator collected data, carrying out multi-source information fusion on information capable of accurately reflecting the actual running state of an elevator to obtain influencing factors and running state signal parameters;
s3: establishing a weak feature extraction method of predictive fault diagnosis based on an evidence theory, further establishing an elevator performance degradation prediction and evaluation model based on multi-source information, establishing an elevator fault diagnosis theory evidence theory model based on multi-source information fusion, and establishing a structural system dynamic failure function by combining reliability evaluation;
s4: the elevator working process is simulated and run, the computer in the running process calls a database and an application program after collecting the information of the diagnosed object, and after asking for necessary information from a user, the computer can quickly find out the final fault or the most likely fault;
s5: the constructed fault diagnosis system comprises a database, a knowledge base, a man-machine interface and an inference engine, and can rapidly and accurately analyze sensor signals sent by the wireless transmission system based on the Internet of things and immediately tell an operator what measures to take.
2. The elevator remote monitoring method according to claim 1, characterized in that: in the step S1 and the step S2, an Internet of things system is built by adopting the stm32MCU and the EC20 communication module, and the fault state and the diagnosis result are networked through a network and sent to a cloud; the fault elevator positioning can be realized through the Beidou and the GPS, and the nodes of the Internet of things are provided with various types of sensors for acquiring information of various physical and chemical states of the elevator, analyzing the current running state of the elevator and judging whether the running state of the elevator is normal or not.
3. The elevator remote monitoring method according to claim 1, characterized in that: in step S3, the evidence theory model is as follows:
p sensors, Q-class state, P >1, Q >1 are provided.
Order theRepresenting the measured data feature vector of the kth sensor. S is S ki Is S k Is the i-th measurement data feature of (a); n is n k Is the total number of measured data features provided by the kth sensor,/for>
Establishing a state matrix:
wherein X is j Is a vector describing the j-th class of states, x ji > 0 represents the ith theoretical measured data characteristic representing the jth class of state;theoretical values representing corresponding data characteristics of the 1 st sensor in the j-th state;a theoretical value representing the data characteristic of the kth sensor in the jth state. Wherein i=1, 2, …, n; j=1, 2, …, Q; k=2, 3, …, P.
Definition S k And X is j Minkowski distance in between:
where p is a constant.
Calculating the distance between the feature vector S and the corresponding state vector X of all sensor measurement data, and establishing a distance matrix:
distance d kj The smaller the probability of judging that the object is in the j-th state based on the k-th sensor information is, the more likely it is, thus defining:
let m kj And d kj And (3) performing inverse correlation and normalization:
expressed in matrix form:
m is then k ＝{m k1 ，m k2 ，…，m kQ ，}k＝1,2,…，P
Can be used as the confidence value of the kth sensor for state identification.
4. The elevator remote monitoring method according to claim 1, characterized in that: in step S4, the database is typically composed of two parts, a dynamic database, which is a relatively stable parameter, and a static database, which is a status parameter detected during operation of the device.
5. The elevator remote monitoring method according to claim 1, characterized in that: in step S5, the knowledge stored in the knowledge base is any one or combination of working environment, system knowledge, equipment fault characteristic value, fault diagnosis algorithm and reasoning rule of the system, reflects causal relationship of the system, and is used for performing fault reasoning, and the knowledge base is a set of expert domain knowledge.
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