CN113264426A - Data identification method and device for fixed lifting equipment and readable storage medium - Google Patents

Data identification method and device for fixed lifting equipment and readable storage medium Download PDF

Info

Publication number
CN113264426A
CN113264426A CN202110276731.9A CN202110276731A CN113264426A CN 113264426 A CN113264426 A CN 113264426A CN 202110276731 A CN202110276731 A CN 202110276731A CN 113264426 A CN113264426 A CN 113264426A
Authority
CN
China
Prior art keywords
data
acceleration
vibration
fixed lifting
lifting equipment
Prior art date
Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
Granted
Application number
CN202110276731.9A
Other languages
Chinese (zh)
Other versions
CN113264426B (en
Inventor
毛晴
钟臻怡
董亚明
杨家荣
俞英杰
郑斌
周俊帆
Current Assignee (The listed assignees may be inaccurate. Google has not performed a legal analysis and makes no representation or warranty as to the accuracy of the list.)
Shanghai Mitsubishi Elevator Co Ltd
Shanghai Electric Group Corp
Original Assignee
Shanghai Mitsubishi Elevator Co Ltd
Shanghai Electric Group Corp
Priority date (The priority date is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the date listed.)
Filing date
Publication date
Application filed by Shanghai Mitsubishi Elevator Co Ltd, Shanghai Electric Group Corp filed Critical Shanghai Mitsubishi Elevator Co Ltd
Priority to CN202110276731.9A priority Critical patent/CN113264426B/en
Publication of CN113264426A publication Critical patent/CN113264426A/en
Application granted granted Critical
Publication of CN113264426B publication Critical patent/CN113264426B/en
Active legal-status Critical Current
Anticipated expiration legal-status Critical

Links

Images

Classifications

    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B66—HOISTING; LIFTING; HAULING
    • B66B—ELEVATORS; ESCALATORS OR MOVING WALKWAYS
    • B66B1/00—Control systems of elevators in general
    • B66B1/24—Control systems with regulation, i.e. with retroactive action, for influencing travelling speed, acceleration, or deceleration
    • B66B1/28—Control systems with regulation, i.e. with retroactive action, for influencing travelling speed, acceleration, or deceleration electrical
    • 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
    • B66B1/3415—Control system configuration and the data transmission or communication within the control system
    • B66B1/3423—Control system configuration, i.e. lay-out
    • 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
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
    • G06F16/20—Information retrieval; Database structures therefor; File system structures therefor of structured data, e.g. relational data
    • G06F16/21—Design, administration or maintenance of databases
    • G06F16/215—Improving data quality; Data cleansing, e.g. de-duplication, removing invalid entries or correcting typographical errors
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
    • G06F16/20—Information retrieval; Database structures therefor; File system structures therefor of structured data, e.g. relational data
    • G06F16/22—Indexing; Data structures therefor; Storage structures
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F18/00—Pattern recognition
    • G06F18/20—Analysing
    • G06F18/24—Classification techniques
    • G06F18/243—Classification techniques relating to the number of classes
    • G06F18/2433—Single-class perspective, e.g. one-against-all classification; Novelty detection; Outlier detection

Landscapes

  • Engineering & Computer Science (AREA)
  • Automation & Control Theory (AREA)
  • Theoretical Computer Science (AREA)
  • Data Mining & Analysis (AREA)
  • Physics & Mathematics (AREA)
  • Databases & Information Systems (AREA)
  • General Physics & Mathematics (AREA)
  • General Engineering & Computer Science (AREA)
  • Bioinformatics & Computational Biology (AREA)
  • Evolutionary Computation (AREA)
  • Evolutionary Biology (AREA)
  • Computer Vision & Pattern Recognition (AREA)
  • Bioinformatics & Cheminformatics (AREA)
  • Artificial Intelligence (AREA)
  • Computer Networks & Wireless Communication (AREA)
  • Quality & Reliability (AREA)
  • Life Sciences & Earth Sciences (AREA)
  • Software Systems (AREA)
  • Indicating And Signalling Devices For Elevators (AREA)
  • Testing Of Devices, Machine Parts, Or Other Structures Thereof (AREA)

Abstract

The invention discloses a data identification method, equipment and a readable storage medium of fixed lifting equipment, wherein the data identification method comprises the following steps: s10, presetting a running state judgment threshold value of the fixed lifting equipment; s20, acquiring vibration data of the fixed lifting equipment in an identification period; and S30, detecting the vibration data according to the operation state judgment threshold value so as to mark a plurality of operation states of the fixed type lifting equipment in the vibration data. The invention sets the running state judgment threshold value related to the fixed lifting equipment parameters, so that the method has the universality of different fixed lifting equipment applications, can accurately mark the running state of the fixed lifting equipment where each vibration data is located according to the running state judgment threshold value, and provides reliable data input for subsequent data applications.

Description

Data identification method and device for fixed lifting equipment and readable storage medium
Technical Field
The invention belongs to the field of equipment fault detection, and particularly relates to a data identification method and equipment for fixed lifting equipment and a readable storage medium.
Background
Along with the increase of high-rise buildings, fixed lifting equipment becomes an indispensable vertical transportation tool for people day by day, the probability of generating faults can be obviously improved along with the accumulation of the running time of the fixed lifting equipment, the faults of the fixed lifting equipment easily generate serious consequences, but the types of the faults of the fixed lifting equipment are various, equipment parts causing the faults are different, the time and labor are wasted when field investigation is carried out by maintenance personnel after the fixed lifting equipment generates failure faults, and huge resource waste is caused.
At present, the health state of fixed lifting equipment is judged mainly based on vibration data, the fixed lifting equipment belongs to mechanical equipment with periodic motion, the running data of the fixed lifting equipment presents an obvious period and is divided into four running states of a static section, an acceleration section, a constant speed section and a deceleration section, and the constant speed section data is usually selected for analysis, so that the identification of the running state of the vibration data of the fixed lifting equipment is the problem to be solved firstly for realizing the health state evaluation of the fixed lifting equipment.
At present, a method for evaluating the health state of a fixed lifting device is based on an acceleration signal generated in the running process of the fixed lifting device, after noise reduction is performed in a wavelet modulus maximum mode, trend-removing processing is performed on data for eliminating the influence generated in the acceleration process and the deceleration process in the running process of the fixed lifting device, and then a health degree evaluation model is established by using the processed data, but the method has the main defects that: the influence of acceleration and deceleration processes in the running process of the fixed lifting equipment cannot be completely removed, and negative influence is generated on subsequent modeling; the vibration generated by opening and closing the door of the fixed lifting equipment at the static section of the car cannot be removed, and the subsequent analysis on the running health state of the car of the fixed lifting equipment is also unfavorable.
Disclosure of Invention
The invention provides a data identification method and device of fixed lifting equipment and a readable storage medium, aiming at overcoming the defect that the influence of the acceleration process and the deceleration process of the fixed lifting equipment in the running process and the vibration of a static section of a car caused by opening and closing a door of the fixed lifting equipment cannot be completely removed in the prior art.
The invention solves the technical problems through the following technical scheme:
a data identification method of a stationary lifting device, the data identification method comprising:
s10, presetting a running state judgment threshold value of the fixed lifting equipment;
s20, acquiring vibration data of the fixed lifting equipment in an identification period;
and S30, detecting the vibration data according to the operation state judgment threshold value so as to mark a plurality of operation states of the fixed lifting equipment in the vibration data.
Preferably, step S10 specifically includes:
and presetting the running state judgment threshold according to the equipment parameters of the fixed lifting equipment.
Preferably, after step S20, the data identification method further includes:
s21, detecting whether the vibration data is complete cycle data, wherein the complete cycle data comprises at least one complete cycle of vibration data, if yes, executing step S30;
wherein, the fixed lifting device starts to run from the stop at the initial floor and stops at the target floor for a complete period;
step S30 specifically includes:
and detecting the complete period data according to the running state judgment threshold value so as to mark a plurality of running states of the fixed lifting equipment.
Preferably, the vibration data includes vertical acceleration data in a direction perpendicular to the horizontal plane, and if the determination result in step S21 is yes, step S22 is executed first, which specifically includes:
s22, calculating acceleration judgment data of the fixed lifting equipment according to the vertical acceleration data in the complete period data;
s23, positioning static segment data in the complete cycle data according to the acceleration judging data, wherein the static segment data are vibration data of the acceleration judging data within an acceleration threshold range in a preset time period; the running state judgment threshold comprises the preset time period and the acceleration threshold range;
s24, removing vibration data before the first section of static section data and vibration data after the last section of static section data in each complete period data to generate pure period data, and then executing the step S30;
step S30 specifically includes:
and detecting the pure periodic data according to the operating state judgment threshold value so as to mark a plurality of operating states of the fixed lifting equipment.
Preferably, after the step S24, the step S25 is executed, which specifically includes:
s25, obtaining speed data of the fixed lifting equipment according to the vertical acceleration data of the pure periodic data;
s26, judging whether the speed data corresponding to the static segment data is in a preset speed threshold range, if not, executing a step S27;
s27, determining that the static segment data is missing, filtering the pure periodic data with the missing static segment data to obtain normal vibration data, and then executing the step S30;
step S30 specifically includes:
and detecting the normal vibration data according to the operating state judgment threshold value so as to mark a plurality of operating states of the fixed lifting equipment.
It should be noted that, in the processes of steps S21-S27, incomplete data in the vibration data, data before and after a static segment in the data, and data with a missing static segment are filtered, respectively, in actual operation, the three are not in a strict sequence relationship, this embodiment merely gives an example of a specific filtering sequence, and data before and after a static segment in the data may also be first filtered, or data with a missing static segment may be first filtered. Preferably, step S30 specifically includes:
s301, calculating to obtain updated acceleration judgment data of the fixed lifting equipment according to the vertical acceleration data in the normal vibration data;
s302, marking a static section label on the vibration data of which the updated acceleration judgment data is in the acceleration threshold range and the speed data is in a preset speed threshold range; and/or marking the vibration data of which the updated acceleration judgment data is within the acceleration threshold range and the speed data is greater than a first preset speed threshold with an upward constant speed section label; and/or marking a downward constant speed section label for the vibration data of which the updated acceleration judgment data is within the acceleration threshold range and the speed data is smaller than a second preset speed threshold;
wherein the operation state judgment threshold includes the preset speed threshold range, the first preset speed threshold, and the second preset speed threshold.
Preferably, after step S302, the data identification method further includes:
s303, splitting the normal vibration data into a static segment, an upward uniform velocity segment, a downward uniform velocity segment and a plurality of data segments to be marked according to the static segment label, the upward uniform velocity segment label and the downward uniform velocity segment label;
s304, marking a downward acceleration section label on the vibration data of which the updated acceleration judgment data is less than 0 and the speed data is less than 0; and/or marking a downward deceleration section label for the vibration data of which the updated acceleration judgment data is greater than 0 and the speed data is less than 0; and/or marking an upward acceleration section label on the vibration data of which the updated acceleration judgment data is greater than 0 and the speed data is greater than 0; and/or marking an upward deceleration section label on the vibration data of which the updated acceleration judgment data is less than 0 and the speed data is greater than 0.
Preferably, before step S304, the data identification method further includes:
s3031, detecting whether the time length of the data segment to be marked is greater than a first preset time threshold, if so, executing a step S304, otherwise, executing a step S3032; wherein the operation state judgment threshold comprises the first preset time threshold;
s3032, selecting the previous label adjacent to the data segment to be marked to mark the data segment to be marked.
Preferably, after step S30, the data identification method further includes:
s40, dividing the vibration data between two adjacent static section labels into single-period data;
s50, detecting whether the single-cycle data is normal or not, and if not, filtering out normal vibration data containing abnormal single-cycle data;
wherein, the normal single-cycle data specifically includes:
the movement directions of the single-period data are consistent, the single-period data comprise three operation stages of acceleration, constant speed and deceleration, the time length of an acceleration section and the time length of a deceleration section in the single-period data are both greater than a second preset time threshold, and the operation state judgment threshold comprises the second preset time threshold.
An electronic device comprises a memory, a processor and a computer program stored on the memory and capable of running on the processor, wherein the processor executes the computer program to realize the data identification method of the fixed lifting device.
A computer-readable storage medium, on which a computer program is stored which, when being executed by a processor, carries out the steps of the above-mentioned data identification method for a stationary hoisting device.
The positive progress effects of the invention are as follows: the invention sets the running state judgment threshold value related to the fixed lifting equipment parameters, so that the method has the universality of different fixed lifting equipment applications, in addition, the method can accurately divide the whole multi-period vibration data into individual periods, meanwhile, the data screening step is embedded, the data is cleaned in the dividing process, finally, the single period is divided to accurately mark the running state of the fixed lifting equipment where each vibration data is located, reliable data input is provided for subsequent data application, and for example, uniform-speed segment data which can more accurately reflect the running health degree of the fixed lifting equipment is conveniently and subsequently screened.
Drawings
Fig. 1 is a flowchart of a data identification method for a fixed type lifting device according to embodiment 1 of the present invention.
Fig. 2 is a flowchart of a data identification method for a fixed type lifting device according to embodiment 2 of the present invention.
Fig. 3 is a flowchart of step 30 of a data identification method for a fixed type lifting device according to embodiment 2 of the present invention.
Fig. 4 is a flowchart of step 30 of the data identification method of the fixed type lifting device according to embodiment 3 of the present invention.
Fig. 5 is a flowchart of a data identification method for a fixed type lifting device according to embodiment 3 of the present invention.
Fig. 6 is a schematic structural diagram of an electronic device according to embodiment 4 of the present invention.
Detailed Description
The invention is further illustrated by the following examples, which are not intended to limit the scope of the invention.
Example 1
A data identification method of a fixed lifting device, as shown in fig. 1, the data identification method includes:
s10, presetting a running state judgment threshold value of the fixed lifting equipment; specifically, the operation state judgment threshold is preset according to the device parameter of the fixed lifting device, in practical application, before the fixed lifting device is put into use, the fixed lifting device is tested and operated, and the operation state judgment threshold is preset according to the test data in normal operation and the device parameter, such as acceleration thresholds of different operation stages, speed thresholds of different operation stages, operation thresholds of different operation stages and the like of the fixed lifting device in a static state.
S20, acquiring vibration data of the fixed lifting equipment in an identification period; it should be noted that, the embodiment of obtaining vibration data is not limited, and preferably, the wireless three-axis vibration sensor is installed to obtain original vibration data, one or more three-axis vibration sensors are installed on the fixed lifting device to obtain real-time vibration data generated in the operation process of the fixed lifting device, the horizontal direction vibration data are set as X and Y, the data perpendicular to the horizontal plane are set as Z, and the real-time vibration data are stored as a data file at intervals.
And S30, detecting the vibration data according to the operation state judgment threshold value so as to mark a plurality of operation states of the fixed lifting equipment in the vibration data. Wherein, the operation state mark can be an upward acceleration section, an upward uniform velocity section, an upward deceleration section, a downward acceleration section, a downward uniform velocity section, a downward deceleration section and a static section of floor stop.
In this embodiment, the operation state judgment threshold related to the fixed lifting device parameter is set, so that the method has universality of different fixed lifting device applications, the operation state of the fixed lifting device where each vibration data is located can be accurately marked according to the operation state judgment threshold, and reliable data input is provided for subsequent data applications.
Example 2
The data identification method of the fixed type lifting device in this embodiment is further improved on the basis of embodiment 1, as shown in fig. 2, after step S20, the data identification method further includes:
s21, detecting whether the vibration data are complete cycle data, if yes, executing a step S22; if not, selecting the next vibration data to continue detection; wherein the complete cycle data comprises at least one complete cycle of vibration data, and the complete cycle is from stopping at a starting floor to running and stopping at a target floor;
s22, calculating acceleration judgment data of the fixed lifting equipment according to the vertical acceleration data in the complete period data; the acceleration evaluation data is obtained by performing sliding mean filtering on data Z (acceleration data) vertical to the horizontal plane direction and then removing the mean, and the value can also be used for compensating the influence of gravity acceleration and sensor null shift.
S23, positioning the stationary segment data in the complete period data according to the acceleration evaluation data; the static section data is vibration data of which the acceleration judging data are all in an acceleration threshold range in a preset time period; wherein the operation state judgment threshold comprises the preset time period and the acceleration threshold range, and the preset time period is
Figure BDA0002976948840000071
The minimum value of the time length of a static period in test data of normal operation of equipment, f is the sampling frequency of the sensor, and the range of the acceleration threshold value is
Figure BDA0002976948840000072
The maximum value of Z of the static segment in the test data of the normal operation of the equipment;
s24, removing vibration data before the first section of static section data and vibration data after the last section of static section data in each complete period data to generate pure period data;
s25, obtaining speed data of the fixed lifting equipment according to the vertical acceleration data of the pure periodic data; and the speed data is obtained by integrating and solving the acceleration evaluation data.
S26, judging whether the speed data corresponding to the static segment data is within a preset speed threshold range, if not, executing a step S27, and if so, executing a step S30 to directly detect the pure period data; in this case, the speed corresponding to the stationary segment data should theoretically be 0, but there may be an influence of vibration or the like in actual operation, and the speed threshold range may be set to a value infinitely close to 0.
S27, determining that the static segment data is missing, filtering the pure periodic data with the missing static segment data to obtain normal vibration data, and then executing the step S30;
further, as shown in fig. 3, step S30 specifically includes:
s301, calculating to obtain updated acceleration judgment data of the fixed lifting equipment according to the vertical acceleration data in the normal vibration data;
s302, marking a static section label on the vibration data of which the updated acceleration judgment data is in the acceleration threshold range and the speed data is in a preset speed threshold range; marking an upward constant speed section label on the vibration data of which the updated acceleration judging data is in the acceleration threshold range and the speed data is greater than a first preset speed threshold; marking a downward constant speed section label on the vibration data of which the updated acceleration judging data is in the acceleration threshold range and the speed data is smaller than a second preset speed threshold; wherein the first preset speed threshold is
Figure BDA0002976948840000081
The second predetermined speed threshold is
Figure BDA0002976948840000082
The minimum value of the speed of the Z-axis uniform-speed operation section in the test data of the normal operation of the equipment is obtained.
Wherein the operation state judgment threshold includes the preset speed threshold range, the first preset speed threshold, and the second preset speed threshold.
S303, splitting the normal vibration data into a static segment, an upward uniform velocity segment, a downward uniform velocity segment and a plurality of data segments to be marked according to the static segment label, the upward uniform velocity segment label and the downward uniform velocity segment label;
s304, marking a downward acceleration section label on the vibration data of which the updated acceleration judgment data is less than 0 and the speed data is less than 0; marking a downward deceleration section label on the vibration data of which the updated acceleration evaluation data is greater than 0 and the speed data is less than 0; marking an upward acceleration section label on the vibration data of which the updated acceleration judgment data is greater than 0 and the speed data is greater than 0; and marking an upward deceleration section label for the vibration data of which the updated acceleration evaluation data is less than 0 and the speed data is greater than 0.
In the embodiment, the whole multi-period vibration data can be accurately segmented into individual periods, meanwhile, a data screening step is embedded, the data are cleaned in the segmentation process, finally, the single period is segmented to accurately mark the running state of the fixed lifting equipment where each vibration data is located, and reliable data input is provided for subsequent data application, for example, uniform-speed segment data which can reflect the running health degree of the fixed lifting equipment more accurately can be conveniently and accurately screened.
Example 3
The data identification method of the fixed type lifting device in this embodiment is further improved on the basis of embodiment 2, as shown in fig. 4, before step S304, the data identification method further includes:
s3031, detecting whether the time length of the data segment to be marked is greater than a first preset timeIf yes, executing step S304, otherwise executing step S3032; wherein the operation state judgment threshold comprises the first preset time threshold; wherein the first predetermined time threshold is
Figure BDA0002976948840000091
The minimum value of the time length of the acceleration period in the test data of the normal operation of the equipment,
Figure BDA0002976948840000092
the minimum value of the time length of the deceleration section in the test data of the normal operation of the equipment.
S3032, selecting the previous label adjacent to the data segment to be marked to mark the data segment to be marked.
As shown in fig. 5, after step S30, the data identification method further includes:
s40, dividing the vibration data between two adjacent static section labels into single-period data;
s50, detecting whether the single-cycle data is normal, if not, executing a step S60, and if so, executing a step S70;
s60, filtering normal vibration data containing abnormal single-cycle data;
and S70, storing the marked vibration data.
Wherein, the normal single-cycle data specifically includes:
the movement directions of the single-period data are consistent, the single-period data comprise three operation stages of acceleration, constant speed and deceleration, the time length of an acceleration section and the time length of a deceleration section in the single-period data are both greater than a second preset time threshold, and the operation state judgment threshold comprises the second preset time threshold. Wherein the second predetermined time threshold is
Figure BDA0002976948840000093
w is a coefficient between 0 and 1.
In addition, after all the vibration data are processed, the average value of the static section data is counted to obtain the null shift value of the data of the batchThe specific calculation steps are as follows: averaging the stationary segment data Z in the normal vibration data to obtain EiTo E, foriAnd removing outliers according to a 3 sigma criterion, averaging the data from which the outliers are removed to obtain a null shift value of the vibration data, wherein the null shift value is used for compensating null shift errors of the divided original data and removing errors caused by the null shift of the vibration sensor.
In this embodiment, after all data are marked, the data are segmented in a single cycle, the data in each single cycle are finally checked based on a marked label, when each single cycle data includes three operation stages of acceleration, constant speed and deceleration, the time length of an acceleration section and the time length of a deceleration section in the single cycle data meet requirements, and the movement directions of the single cycle data are consistent (that is, the data includes upward acceleration, upward constant speed and upward deceleration, or includes downward acceleration, downward constant speed and downward deceleration), it is determined that the cycle data is normal, and if the cycle data is not normal, the cycle data is discarded, and finally only the vibration data which accurately reflects the normal operation health degree of the fixed lifting equipment is retained, so that reliable data input is provided for subsequent data application.
Example 4
An electronic device comprising a memory, a processor and a computer program stored on the memory and executable on the processor, wherein the processor executes the computer program to implement the method of data identification of a stationary lifting device according to any one of embodiments 1-3.
Fig. 6 is a schematic structural diagram of an electronic device provided in this embodiment. FIG. 6 illustrates a block diagram of an exemplary electronic device 90 suitable for use in implementing embodiments of the present invention. The electronic device 90 shown in fig. 6 is only an example, and should not bring any limitation to the functions and the scope of use of the embodiment of the present invention.
As shown in fig. 6, the electronic device 90 may take the form of a general purpose computing device, which may be a server device, for example. The components of the electronic device 90 may include, but are not limited to: at least one processor 91, at least one memory 92, and a bus 93 that connects the various system components (including the memory 92 and the processor 91).
The bus 93 includes a data bus, an address bus, and a control bus.
Memory 92 may include volatile memory, such as Random Access Memory (RAM)921 and/or cache memory 922, and may further include Read Only Memory (ROM) 923.
Memory 92 may also include a program tool 925 having a set (at least one) of program modules 924, such program modules 924 including, but not limited to: an operating system, one or more application programs, other program modules, and program data, each of which, or some combination thereof, may comprise an implementation of a network environment.
The processor 91 executes various functional applications and data processing by running a computer program stored in the memory 92.
The electronic device 90 may also communicate with one or more external devices 94 (e.g., keyboard, pointing device, etc.). Such communication may be through an input/output (I/O) interface 95. Also, the electronic device 90 may communicate with one or more networks (e.g., a Local Area Network (LAN), a Wide Area Network (WAN), and/or a public network, such as the Internet) via a network adapter 96. The network adapter 96 communicates with the other modules of the electronic device 90 via the bus 93. It should be appreciated that although not shown in the figures, other hardware and/or software modules may be used in conjunction with the electronic device 90, including but not limited to: microcode, device drivers, redundant processors, external disk drive arrays, RAID (disk array) systems, tape drives, and data backup storage systems, etc.
It should be noted that although in the above detailed description several units/modules or sub-units/modules of the electronic device are mentioned, such a division is merely exemplary and not mandatory. Indeed, the features and functionality of two or more of the units/modules described above may be embodied in one unit/module, according to embodiments of the application. Conversely, the features and functions of one unit/module described above may be further divided into embodiments by a plurality of units/modules.
Example 5
A computer-readable storage medium, on which a computer program is stored which, when being executed by a processor, carries out the steps of the method of data identification of a stationary hoisting device according to any one of embodiments 1 to 3.
More specific examples, among others, that the readable storage medium may employ may include, but are not limited to: a portable disk, a hard disk, random access memory, read only memory, erasable programmable read only memory, optical storage device, magnetic storage device, or any suitable combination of the foregoing.
In a possible implementation, the invention may also be implemented in the form of a program product comprising program code means for causing a terminal device to carry out the steps of implementing the method for data identification of a stationary hoisting device according to any one of embodiments 1 to 3, when said program product is run on the terminal device.
Where program code for carrying out the invention is written in any combination of one or more programming languages, the program code may be executed entirely on the user device, partly on the user device, as a stand-alone software package, partly on the user device and partly on a remote device or entirely on the remote device.
While specific embodiments of the invention have been described above, it will be appreciated by those skilled in the art that this is by way of example only, and that the scope of the invention is defined by the appended claims. Various changes and modifications to these embodiments may be made by those skilled in the art without departing from the spirit and scope of the invention, and these changes and modifications are within the scope of the invention.

Claims (11)

1. A data identification method of a fixed lifting device is characterized by comprising the following steps:
s10, presetting a running state judgment threshold value of the fixed lifting equipment;
s20, acquiring vibration data of the fixed lifting equipment in an identification period;
and S30, detecting the vibration data according to the operation state judgment threshold value so as to mark a plurality of operation states of the fixed lifting equipment in the vibration data.
2. The data identification method of the stationary type lifting device according to claim 1, wherein the step S10 specifically includes:
and presetting the running state judgment threshold according to the equipment parameters of the fixed lifting equipment.
3. The data identification method of a stationary lift device of claim 1, wherein after step S20, the data identification method further comprises:
s21, detecting whether the vibration data is complete cycle data, wherein the complete cycle data comprises at least one complete cycle of vibration data, if yes, executing step S30;
wherein, the fixed lifting device starts to run from the stop at the initial floor and stops at the target floor for a complete period;
step S30 specifically includes:
and detecting the complete period data according to the running state judgment threshold value so as to mark a plurality of running states of the fixed lifting equipment.
4. The method as claimed in claim 3, wherein the vibration data includes vertical acceleration data in a direction perpendicular to a horizontal plane, and if the determination result in step S21 is yes, step S22 is executed first, and the method specifically includes:
s22, calculating acceleration judgment data of the fixed lifting equipment according to the vertical acceleration data in the complete period data;
s23, positioning static segment data in the complete cycle data according to the acceleration judging data, wherein the static segment data are vibration data of the acceleration judging data within an acceleration threshold range in a preset time period; the running state judgment threshold comprises the preset time period and the acceleration threshold range;
s24, removing vibration data before the first section of static section data and vibration data after the last section of static section data in each complete period data to generate pure period data, and then executing the step S30;
step S30 specifically includes:
and detecting the pure periodic data according to the operating state judgment threshold value so as to mark a plurality of operating states of the fixed lifting equipment.
5. The method for identifying data of a stationary lift device according to claim 4, wherein step S25 is executed after step S24, and specifically comprises:
s25, obtaining speed data of the fixed lifting equipment according to the vertical acceleration data of the pure periodic data;
s26, judging whether the speed data corresponding to the static segment data is in a preset speed threshold range, if not, executing a step S27;
s27, determining that the static segment data is missing, filtering the pure periodic data with the missing static segment data to obtain normal vibration data, and then executing the step S30;
step S30 specifically includes:
and detecting the normal vibration data according to the operating state judgment threshold value so as to mark a plurality of operating states of the fixed lifting equipment.
6. The data identification method of the stationary type lifting device according to claim 5, wherein the step S30 specifically includes:
s301, calculating to obtain updated acceleration judgment data of the fixed lifting equipment according to the vertical acceleration data in the normal vibration data;
s302, marking a static section label on the vibration data of which the updated acceleration judgment data is in the acceleration threshold range and the speed data is in a preset speed threshold range; and/or marking the vibration data of which the updated acceleration judgment data is within the acceleration threshold range and the speed data is greater than a first preset speed threshold with an upward constant speed section label; and/or marking a downward constant speed section label for the vibration data of which the updated acceleration judgment data is within the acceleration threshold range and the speed data is smaller than a second preset speed threshold;
wherein the operation state judgment threshold includes the preset speed threshold range, the first preset speed threshold, and the second preset speed threshold.
7. The method of claim 6, wherein after step S302, the method further comprises:
s303, splitting the normal vibration data into a static segment, an upward uniform velocity segment, a downward uniform velocity segment and a plurality of data segments to be marked according to the static segment label, the upward uniform velocity segment label and the downward uniform velocity segment label;
s304, marking a downward acceleration section label on the vibration data of which the updated acceleration judgment data is less than 0 and the speed data is less than 0; and/or marking a downward deceleration section label for the vibration data of which the updated acceleration judgment data is greater than 0 and the speed data is less than 0; and/or marking an upward acceleration section label on the vibration data of which the updated acceleration judgment data is greater than 0 and the speed data is greater than 0; and/or marking an upward deceleration section label on the vibration data of which the updated acceleration judgment data is less than 0 and the speed data is greater than 0.
8. The method of claim 7, wherein prior to step S304, the method further comprises:
s3031, detecting whether the time length of the data segment to be marked is greater than a first preset time threshold, if so, executing a step S304, otherwise, executing a step S3032; wherein the operation state judgment threshold comprises the first preset time threshold;
s3032, selecting the previous label adjacent to the data segment to be marked to mark the data segment to be marked.
9. The data identification method of a stationary lift device of claim 6, wherein after step S30, the data identification method further comprises:
s40, dividing the vibration data between two adjacent static section labels into single-period data;
s50, detecting whether the single-cycle data is normal or not, and if not, filtering out normal vibration data containing abnormal single-cycle data;
wherein, the normal single-cycle data specifically includes:
the movement directions of the single-period data are consistent, the single-period data comprise three operation stages of acceleration, constant speed and deceleration, the time length of an acceleration section and the time length of a deceleration section in the single-period data are both greater than a second preset time threshold, and the operation state judgment threshold comprises the second preset time threshold.
10. An electronic device comprising a memory, a processor and a computer program stored on the memory and executable on the processor, wherein the processor implements the data identification method of the stationary lifting device according to any one of claims 1 to 9 when executing the computer program.
11. A computer-readable storage medium, on which a computer program is stored, which program, when being executed by a processor, carries out the steps of the data identification method of a stationary lifting device according to any one of claims 1 to 9.
CN202110276731.9A 2021-03-15 2021-03-15 Data identification method and device for fixed lifting equipment and readable storage medium Active CN113264426B (en)

Priority Applications (1)

Application Number Priority Date Filing Date Title
CN202110276731.9A CN113264426B (en) 2021-03-15 2021-03-15 Data identification method and device for fixed lifting equipment and readable storage medium

Applications Claiming Priority (1)

Application Number Priority Date Filing Date Title
CN202110276731.9A CN113264426B (en) 2021-03-15 2021-03-15 Data identification method and device for fixed lifting equipment and readable storage medium

Publications (2)

Publication Number Publication Date
CN113264426A true CN113264426A (en) 2021-08-17
CN113264426B CN113264426B (en) 2023-04-07

Family

ID=77228302

Family Applications (1)

Application Number Title Priority Date Filing Date
CN202110276731.9A Active CN113264426B (en) 2021-03-15 2021-03-15 Data identification method and device for fixed lifting equipment and readable storage medium

Country Status (1)

Country Link
CN (1) CN113264426B (en)

Citations (6)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN104925613A (en) * 2015-06-03 2015-09-23 安徽中科智能高技术有限责任公司 Online safety detection prewarning device of elevator and detection prewarning method thereof
WO2017063532A1 (en) * 2015-10-14 2017-04-20 阿里巴巴集团控股有限公司 Method and device for identifying address type
JP2018145005A (en) * 2017-03-09 2018-09-20 東芝エレベータ株式会社 Elevator system
CN109019218A (en) * 2018-10-18 2018-12-18 杭州市特种设备检测研究院 A kind of elevator traction machine motor vibration state monitoring apparatus
CN110589650A (en) * 2019-09-23 2019-12-20 猫岐智能科技（上海）有限公司 Equipment operation abnormity judgment system
CN110642109A (en) * 2019-04-26 2020-01-03 深圳市豪视智能科技有限公司 Vibration detection method and device for lifting equipment, server and storage medium

Patent Citations (6)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN104925613A (en) * 2015-06-03 2015-09-23 安徽中科智能高技术有限责任公司 Online safety detection prewarning device of elevator and detection prewarning method thereof
WO2017063532A1 (en) * 2015-10-14 2017-04-20 阿里巴巴集团控股有限公司 Method and device for identifying address type
JP2018145005A (en) * 2017-03-09 2018-09-20 東芝エレベータ株式会社 Elevator system
CN109019218A (en) * 2018-10-18 2018-12-18 杭州市特种设备检测研究院 A kind of elevator traction machine motor vibration state monitoring apparatus
CN110642109A (en) * 2019-04-26 2020-01-03 深圳市豪视智能科技有限公司 Vibration detection method and device for lifting equipment, server and storage medium
CN110589650A (en) * 2019-09-23 2019-12-20 猫岐智能科技（上海）有限公司 Equipment operation abnormity judgment system

Also Published As

Publication number Publication date
CN113264426B (en) 2023-04-07

Similar Documents

Publication Publication Date Title
US9996405B2 (en) Embedded prognostics on PLC platforms for equipment condition monitoring, diagnosis and time-to-failure/service prediction
Jin et al. Anomaly detection and fault prognosis for bearings
US9424157B2 (en) Early detection of failing computers
CN115443638B (en) Method and system for diagnosing and mitigating memory leaks in a compute node
US9324192B2 (en) Real-time monitoring of vehicle
US11209345B1 (en) Automatic prognostic qualification of manufacturing products
CN110689141B (en) Fault diagnosis method and equipment for wind generating set
CN111459692B (en) Method, apparatus and computer program product for predicting drive failure
EP3617117B1 (en) Model development framework for remote monitoring condition-based maintenance
CN103810099B (en) Code tracing method and code tracing system
US20150088585A1 (en) Supply chain management using problem and remediation propagation modeling
CN115769235A (en) Method and system for providing an alert related to the accuracy of a training function
JP2009086896A (en) Failure prediction system and failure prediction method for computer
WO2021064144A1 (en) Method and system for continuous estimation and representation of risk
KR20230102431A (en) Oil gas plant equipment failure prediction and diagnosis system based on artificial intelligence
CN113264426B (en) Data identification method and device for fixed lifting equipment and readable storage medium
CN117435883A (en) Method and system for predicting equipment faults based on digital twinning
US20220050763A1 (en) Detecting regime change in time series data to manage a technology platform
CN114936113B (en) Task avalanche recovery method and device, electronic equipment and storage medium
CN116431454B (en) Big data computer performance control system and method
US20240212121A1 (en) System and method for predictive monitoring of devices
US11556446B2 (en) Programmatic performance anomaly detection
CN113269222A (en) Characteristic value selection method, detection method and system for detecting lifting equipment
Medon A framework for a predictive manitenance tool articulated with a Manufacturing Execution System
EA047460B1 (en) METHOD AND SYSTEM FOR MONITORING AUTOMATED SYSTEMS

Legal Events

Date Code Title Description
PB01 Publication
PB01 Publication
SE01 Entry into force of request for substantive examination
SE01 Entry into force of request for substantive examination
GR01 Patent grant
GR01 Patent grant