WO2018014502A1 - Procédé et dispositif de pré-diagnostic d'ascenseur - Google Patents

Procédé et dispositif de pré-diagnostic d'ascenseur Download PDF

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Publication number
WO2018014502A1
WO2018014502A1 PCT/CN2016/111282 CN2016111282W WO2018014502A1 WO 2018014502 A1 WO2018014502 A1 WO 2018014502A1 CN 2016111282 W CN2016111282 W CN 2016111282W WO 2018014502 A1 WO2018014502 A1 WO 2018014502A1
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WO
WIPO (PCT)
Prior art keywords
elevator
data
fault
weighted average
component
Prior art date
Application number
PCT/CN2016/111282
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English (en)
Chinese (zh)
Inventor
陈涛
黄立明
雷嘉伟
郑海松
仲兆峰
李基源
郭伟文
Original Assignee
日立楼宇技术（广州）有限公司
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Application filed by 日立楼宇技术（广州）有限公司 filed Critical 日立楼宇技术（广州）有限公司
Publication of WO2018014502A1 publication Critical patent/WO2018014502A1/fr

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    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B66—HOISTING; LIFTING; HAULING
    • B66B—ELEVATORS; ESCALATORS OR MOVING WALKWAYS
    • B66B5/00—Applications of checking, fault-correcting, or safety devices in elevators
    • B66B5/0006—Monitoring devices or performance analysers
    • B66B5/0018—Devices monitoring the operating condition of the elevator system
    • B66B5/0025—Devices monitoring the operating condition of the elevator system for maintenance or repair
    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B66—HOISTING; LIFTING; HAULING
    • B66B—ELEVATORS; ESCALATORS OR MOVING WALKWAYS
    • B66B3/00—Applications of devices for indicating or signalling operating conditions of elevators
    • 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

Definitions

  • the present application relates to the field of elevator technology, for example, to a pre-diagnosis method and apparatus for an elevator.
  • the components of the elevator are abnormal such as the elevator components, and the maintenance personnel go to the site for treatment after the fault is reported. This often leads to problems such as stopping the ladder and even being trapped, which greatly affects the customer's use.
  • the maintenance personnel usually have to regularly check all the main components of the elevator. This reduces the efficiency of the maintenance personnel, reduces the number of maintenance personnel to maintain the elevator, and increases the cost of maintenance of the elevator.
  • Embodiments of the present invention provide a pre-diagnosis method and apparatus for an elevator to reduce maintenance cost of the elevator.
  • the embodiment of the invention provides a pre-diagnosis method for an elevator, the method comprising:
  • the embodiment of the invention further provides a pre-diagnosis device for an elevator, the device comprising:
  • a data storage module configured to store elevator running state data uploaded by the elevator
  • a data association module configured to correlate fault identification data associated with operation of the elevator component in the elevator operating state data
  • a fault pre-diagnosis module configured to perform statistical analysis on the fault identification data to Whether the ladder components will fail for pre-diagnosis.
  • An embodiment of the present invention further provides an electronic device, including:
  • At least one processor and,
  • the memory stores instructions executable by the one processor, the instructions being executed by the at least one processor to enable the at least one processor to perform a pre-diagnostic method of any of the elevators of the embodiments of the present invention .
  • the embodiment of the present invention further provides a non-transitory computer readable storage medium, wherein the non-transitory computer readable storage medium stores computer instructions for causing the computer to perform any of the embodiments of the present invention.
  • a pre-diagnostic method for an elevator is provided.
  • Embodiments of the present invention also provide a computer program product, wherein the computer program product comprises a computing program stored on a non-transitory computer readable storage medium, the computer program comprising program instructions, when the program instructions are When executed, the computer is caused to perform a pre-diagnosis method of any of the elevators of the embodiments of the present invention.
  • the pre-diagnosis method and device for an elevator stores the elevator running state data uploaded by the elevator through the big data analysis system deployed in the cloud, and associates the fault identification data related to the operation of the elevator component in the elevator running state data, And performing statistical analysis on the fault discrimination data by using a big data analysis system to pre-diagnose whether the elevator component will be faulty, so that the maintenance personnel can accurately predict in advance whether the elevator component will be faulty.
  • the use of big data analysis system effectively reduces the maintenance cost of the elevator.
  • FIG. 1 is a flow chart of a method for pre-diagnosing an elevator according to a first embodiment of the present invention
  • FIG. 2 is a flow chart of a diagnostic operation in a pre-diagnosis method for an elevator provided by a second embodiment of the present invention
  • FIG. 3 is a flow chart of a method for pre-diagnosing an elevator according to a third embodiment of the present invention.
  • FIG. 4 is a flow chart of a method for pre-diagnosing an elevator according to a fourth embodiment of the present invention.
  • Figure 5 is a structural view of a pre-diagnostic device for an elevator according to a fifth embodiment of the present invention.
  • FIG. 6 is a schematic structural diagram of an electronic device for performing a pre-diagnosis method for an elevator according to a fifth embodiment of the present invention.
  • This embodiment provides a technical solution of the pre-diagnosis method of the elevator.
  • the pre-diagnosis method of the elevator is performed by a pre-diagnostic device of the elevator.
  • the pre-diagnostic device of the elevator is integrated in a big data analysis system such as Hadoop, Storm, Spark Streaming, or Samza deployed in the cloud.
  • the pre-diagnosis method of the elevator includes:
  • the elevator operating state data refers to data collected locally in the elevator and uploaded to the cloud by the communication device local to the elevator to indicate the operating status of the plurality of components of the elevator.
  • the elevator operating state data includes: the load of the elevator, the running direction of the elevator, the running speed, the information of the user's ladder, the output power of the motor, and the on/off status of each component.
  • the pre-diagnostic device of the elevator deployed in the cloud after receiving the elevator running state data, will The above elevator operating state data is stored.
  • the elevator running state data is stored in a distributed database.
  • the distributed data may be an Hbase database.
  • fault identification data related to the operation of the elevator component in the elevator operating state data is associated.
  • not all of the data items in the elevator operating state data are fault discrimination data related to the operation of the elevator components.
  • the user's ladder information is not logically related to whether it is in a fault state.
  • the elevator operating state data includes data that is completely unrelated to whether the elevator component is in a fault state
  • the data is discriminated to use the fault discrimination data for fault diagnosis of the elevator components.
  • whether an elevator running state data belongs to fault identification data related to operation of the elevator component is specified by the maintenance personnel according to their own maintenance experience during the initialization phase.
  • the fault discrimination data associated with the operation of the lights within the elevator should include the number of times the power switch is operated and the total time the lights are on.
  • whether an elevator running state data belongs to the fault identification data may be tuned according to a machine learning process for the elevator running state data, such as adding temperature and humidity data to the fault identification data.
  • the statistical analysis of the fault identification data is performed according to a predetermined statistical analysis model.
  • the fault identification data may be statistically analyzed according to a weighted average model of the fault identification data, or the fault identification data may be statistically analyzed according to a predetermined artificial neural network model.
  • the result of statistical analysis of the fault discrimination data is a determination as to whether the elevator component is currently in a fault state.
  • the statistical analysis result of the fault discrimination data may further include the urgency of the fault requiring maintenance. With the above urgency judgment, it is possible to instruct the maintenance and protection personnel whether they need to go to the maintenance site immediately.
  • the elevator operation state data uploaded by the elevator is stored in the big data analysis system deployed in the cloud, the fault identification data related to the operation of the elevator component in the elevator operation state data is associated, and the fault identification data is counted.
  • the analysis automatically gives an indication of whether the elevator component is in a fault state, and reduces the maintenance cost of the elevator.
  • This embodiment provides a technical solution for the diagnosis operation in the pre-diagnosis method of the elevator based on the above embodiment of the present invention.
  • performing statistical analysis on the fault discrimination data to diagnose whether the elevator component is faulty comprises: acquiring a preset weighted average model, wherein the weighted average model is used to identify the fault data Performing a statistical analysis; and diagnosing whether the elevator component is faulty according to the weighted average model.
  • performing statistical analysis on the fault discrimination data to diagnose whether the elevator component is faulty includes:
  • a preset weighted average model is acquired, wherein the weighted average model is used for statistical analysis of the fault discrimination data.
  • weighted average model is given by the following formula:
  • v i is the ith fault discrimination data item
  • w i is the weighting coefficient corresponding to the ith fault discrimination data item
  • s is the result of weighted averaging of the plurality of fault discrimination data.
  • the weighted average result s of the weighted average model described above is compared with a preset diagnostic threshold. If the weighted average result s is within a range of values determined by the diagnostic threshold, it may be determined that the elevator component does not fail; if the weighted average result s is not within a range of values determined by the diagnostic threshold Inside, it can be judged that the elevator component has failed.
  • the weighted average result s may also be compared with a preset urgency threshold to determine the urgency of the current fault.
  • the advantage of determining the above urgency is that it is possible to determine whether it is necessary to notify the maintenance personnel to rush to the maintenance site based on the determined urgency of the failure.
  • a preset weighted average model is obtained, wherein the weighted average model is used for performing statistical analysis on the fault discrimination data, and according to the weighted average model, whether the elevator component is faulty is diagnosed, An accurate judgment of whether or not the elevator component has failed is achieved.
  • the pre-diagnosis method of the elevator further includes: adjusting a model parameter of the weighted average model according to a machine learning algorithm.
  • the pre-diagnosis method of the elevator includes:
  • the model parameters of the weighted average model are adjusted according to a machine learning algorithm.
  • the model parameters of the weighted average model include: a weighting coefficient of the weighted average model and a diagnostic threshold of the weighted average model.
  • the value of the weighting coefficient corresponding to the fault identification data item may be correspondingly lowered.
  • the value of the weighting coefficient corresponding to the fault identification data item may be correspondingly increased.
  • the machine learning algorithm may be a decision tree algorithm.
  • the fault discrimination data item may also be added or deleted in the weighted average model.
  • the fault discrimination data item can be deleted from the weighted average model.
  • the model parameters of the weighted average model are adjusted according to a machine learning algorithm, so that the weighted average model for fault diagnosis can be adjusted according to real-time changes of data, so that the diagnosis of the fault state of the elevator component is more accurate.
  • the pre-diagnosis method of the elevator further includes: generating an operation status report for displaying an operation state of the elevator according to the elevator operation state data and the diagnosis result; and displaying the operation status report to the user.
  • the pre-diagnosis method of the elevator includes:
  • an operation status report for displaying the elevator operation state is generated based on the elevator operation state data and the diagnosis result.
  • the generated operational status report includes not only the necessary elevator operating state data, but also a determination result of whether the elevator component is currently in a fault state according to the weighted average model.
  • the operating state may include the number of operations, the running time, the number of component actions, the component action time, the pre-diagnosis results, and the suggested replacement device prompt.
  • the generation of the running status report may be generated according to a system setting, or may be generated according to a real-time instruction of the user.
  • the running status report may be directly displayed in the cloud locality, or the generated running status report may be sent to the remote client, and then the running status report is displayed to the user by the client.
  • the maintenance personnel of the elevator can know the current running status of the multiple components of the elevator in real time and Whether it is in a fault state improves the availability of the cloud system.
  • the pre-diagnosis device of the elevator includes: a data storage module 51, a data association module 52, and a fault pre-diagnosis module 53.
  • the data storage module 51 is configured to store elevator operating state data uploaded by the elevator.
  • the data association module 52 is configured to correlate fault identification data associated with operation of the elevator component in the elevator operating state data.
  • the fault pre-diagnosis module 53 is configured to perform statistical analysis on the fault discrimination data to pre-diagnose whether the elevator component will be faulty.
  • the fault pre-diagnosis module 53 includes: a model acquisition unit and a diagnosis unit.
  • the model obtaining unit is configured to acquire a preset weighted average model, wherein the weighted average model is used for statistical analysis of the fault discrimination data.
  • the diagnostic unit is configured to diagnose whether the elevator component has failed according to the weighted average model.
  • the pre-diagnostic device of the elevator further includes: a model adjustment module 54.
  • the model adjustment module 54 is configured to adjust model parameters of the weighted average model according to a machine learning algorithm.
  • the model parameters of the weighted average model include: a weighting coefficient and a diagnosis threshold.
  • the pre-diagnosis device of the elevator further includes: a report generation module 55 and a report display module 56.
  • the report generation module 55 is configured to generate data according to the elevator running state data and the diagnosis result.
  • the operation status report is used to display the running status of the elevator; wherein the running status report includes the running times, the running time, the number of component actions, the component action time, the pre-diagnosis result, and the suggested device replacement prompt.
  • the report display module 56 is arranged to display the operational status report to the user.
  • the pre-diagnostic device of the elevator is integrated in Hadoop, Storm, Spark Streaming, or Samza system.
  • the embodiment of the present invention further provides a non-transitory computer storage medium, wherein the computer storage medium stores computer executable instructions, which can execute the pre-diagnosis method of the elevator in any of the foregoing method embodiments.
  • An embodiment of the present invention further provides a schematic structural diagram of an electronic device that performs a pre-diagnosis method for an elevator, where the electronic device includes:
  • processors 610 and memory 620 one processor 610 is taken as an example in FIG.
  • the electronic device may also include an input device 630 and an output device 640.
  • the processor 610, the memory 620, the input device 630, and the output device 640 may be connected by a bus or other means, as exemplified by a bus connection in FIG.
  • the memory 620 is used as a non-transitory computer readable storage medium, and can be used for storing non-transitory software programs, non-transitory computer executable programs, and modules, such as program instructions corresponding to the pre-diagnosis method of the elevator in the embodiment of the present invention.
  • the processor 610 performs various functional applications of the server and data processing by running non-transitory software programs, instructions, and modules stored in the memory 620, that is, implementing a pre-diagnostic method of the elevator.
  • the memory 620 may include a storage program area and a storage data area, wherein the storage program area may store an operating system, an application required by at least one function; the storage data area may be stored according to the application Data created by the use of interactive terminals, etc.
  • memory 620 can include high speed random access memory, and can also include non-transitory memory, such as at least one magnetic disk storage device, flash memory device, or other non-transitory solid state storage device.
  • the memory 620 can optionally include a memory remotely located relative to the processor 610 that can be connected to the terminal of the application interaction via a network.
  • Examples of the above network may be the Internet, an intranet, a local area network, a mobile communication network, and combinations thereof.
  • Input device 630 can receive input numeric or character information, as well as key signal inputs related to user settings and function control of the terminal that interacts with the application.
  • the output device 640 can include a display device such as a display screen.
  • the one or more modules are stored in the memory 620, and when executed by the one or more processors 610, perform a pre-diagnostic method of the elevator in any of the above method embodiments.
  • the above product can perform the method provided by any embodiment of the present invention, and has the corresponding functional modules and beneficial effects of the execution method.
  • the embodiment of the invention provides a pre-diagnosis method and device for an elevator, which effectively reduces the maintenance cost of the elevator by analyzing the big data of the elevator running state data in the cloud.

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  • Indicating And Signalling Devices For Elevators (AREA)
  • Maintenance And Inspection Apparatuses For Elevators (AREA)

Abstract

L'invention concerne un procédé et un dispositif de pré-diagnostic d'ascenseur. Le procédé consiste en : le stockage de données d'état de fonctionnement d'ascenseur téléchargées par un ascenseur ; l'association de données de discrimination de panne, associées au fonctionnement d'un élément d'ascenseur, dans les données d'état de fonctionnement d'ascenseur ; et la mise en œuvre d'une analyse statistique sur les données de discrimination de panne, de façon à effectuer un pré-diagnostic visant à déterminer si l'élément ascenseur fera l'objet d'une panne. Le dispositif comprend : un module d'analyse de données (51), un module d'association de données (52) et un module de pré-diagnostic de panne (53).
PCT/CN2016/111282 2016-07-19 2016-12-21 Procédé et dispositif de pré-diagnostic d'ascenseur WO2018014502A1 (fr)

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CN201610574558.XA CN106144819B (zh) 2016-07-19 2016-07-19 电梯的预诊断方法及装置

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CN110472563A (zh) * 2019-08-13 2019-11-19 浙江大学 基于小波包分解和神经网络的直梯振动过大故障诊断方法
CN111563229A (zh) * 2020-05-13 2020-08-21 浙江大学 基于高斯混合模型的直梯超速自动复位故障诊断方法
CN113135480A (zh) * 2021-05-13 2021-07-20 上海梯之星信息科技有限公司 基于局部和整体特征的电梯故障预警方法
CN113682911A (zh) * 2021-08-24 2021-11-23 日立楼宇技术（广州）有限公司 一种采样方式的设置、电梯的故障检测方法及相关装置
US11472663B2 (en) 2018-10-01 2022-10-18 Otis Elevator Company Automatic software upgrade assistant for remote elevator monitoring experts using machine learning

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CN106144819B (zh) * 2016-07-19 2018-09-07 日立楼宇技术（广州）有限公司 电梯的预诊断方法及装置
CN107786375A (zh) * 2017-10-20 2018-03-09 朱健雄 一种通讯装置失效预警系统
CN108083044B (zh) * 2017-11-21 2019-12-24 浙江新再灵科技股份有限公司 一种基于大数据分析的电梯按需维保系统及方法
CN109896379A (zh) * 2017-12-11 2019-06-18 日立楼宇技术（广州）有限公司 一种电梯故障预诊断方法、装置、设备以及存储介质
CN111240946B (zh) * 2018-11-29 2021-12-07 珠海格力电器股份有限公司 一种设备故障诊断数据的处理方法、处理系统
US11993480B2 (en) 2019-04-30 2024-05-28 Otis Elevator Company Elevator shaft distributed health level with mechanic feed back condition based monitoring

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US11472663B2 (en) 2018-10-01 2022-10-18 Otis Elevator Company Automatic software upgrade assistant for remote elevator monitoring experts using machine learning
CN110472563A (zh) * 2019-08-13 2019-11-19 浙江大学 基于小波包分解和神经网络的直梯振动过大故障诊断方法
CN111563229A (zh) * 2020-05-13 2020-08-21 浙江大学 基于高斯混合模型的直梯超速自动复位故障诊断方法
CN111563229B (zh) * 2020-05-13 2022-03-22 浙江大学 基于高斯混合模型的直梯超速自动复位故障诊断方法
CN113135480A (zh) * 2021-05-13 2021-07-20 上海梯之星信息科技有限公司 基于局部和整体特征的电梯故障预警方法
CN113682911A (zh) * 2021-08-24 2021-11-23 日立楼宇技术（广州）有限公司 一种采样方式的设置、电梯的故障检测方法及相关装置

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